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+            arch v7.1.1 (+18)
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -87,7 +87,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+            arch v7.1.1 (+18)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -87,7 +87,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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+    arch v7.1.1 (+18)
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--- a/devel/_modules/arch/unitroot/_shared.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -87,7 +87,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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+            arch v7.1.1 (+18)
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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--- a/devel/_modules/arch/unitroot/cointegration.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -87,7 +87,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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--- a/devel/_modules/arch/unitroot/unitroot.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -87,7 +87,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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diff --git a/devel/_modules/arch/univariate/base.html b/devel/_modules/arch/univariate/base.html
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--- a/devel/_modules/arch/univariate/base.html
+++ b/devel/_modules/arch/univariate/base.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -87,7 +87,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/_modules/arch/univariate/distribution.html b/devel/_modules/arch/univariate/distribution.html
index 53edd21e7f..f7dc0ab95e 100644
--- a/devel/_modules/arch/univariate/distribution.html
+++ b/devel/_modules/arch/univariate/distribution.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.distribution - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/_modules/arch/univariate/mean.html b/devel/_modules/arch/univariate/mean.html
index 27588fddb6..8c7f0fbdab 100644
--- a/devel/_modules/arch/univariate/mean.html
+++ b/devel/_modules/arch/univariate/mean.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.mean - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.mean - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/_modules/arch/univariate/volatility.html b/devel/_modules/arch/univariate/volatility.html
index d030d7a5db..df2096509e 100644
--- a/devel/_modules/arch/univariate/volatility.html
+++ b/devel/_modules/arch/univariate/volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.volatility - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/_modules/arch/utility/testing.html b/devel/_modules/arch/utility/testing.html
index fe15cbb3a3..cc38a00748 100644
--- a/devel/_modules/arch/utility/testing.html
+++ b/devel/_modules/arch/utility/testing.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.utility.testing - arch v7.1.1 (+16)</title>
+        <title>arch.utility.testing - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/_modules/functools.html b/devel/_modules/functools.html
index 0d12e39fd5..0017108261 100644
--- a/devel/_modules/functools.html
+++ b/devel/_modules/functools.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>functools - arch v7.1.1 (+16)</title>
+        <title>functools - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/_modules/index.html b/devel/_modules/index.html
index 85ddb53eb9..2ebed36d0f 100644
--- a/devel/_modules/index.html
+++ b/devel/_modules/index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Overview: module code - arch v7.1.1 (+16)</title>
+        <title>Overview: module code - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/_modules/numpy.html b/devel/_modules/numpy.html
index e15f88db65..958ef845ab 100644
--- a/devel/_modules/numpy.html
+++ b/devel/_modules/numpy.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>numpy - arch v7.1.1 (+16)</title>
+        <title>numpy - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/_modules/typing.html b/devel/_modules/typing.html
index 81bbc6d2b7..1ccf7610fa 100644
--- a/devel/_modules/typing.html
+++ b/devel/_modules/typing.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>typing - arch v7.1.1 (+16)</title>
+        <title>typing - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/_sources/univariate/univariate_forecasting_with_exogenous_variables.ipynb.txt b/devel/_sources/univariate/univariate_forecasting_with_exogenous_variables.ipynb.txt
index cd5d0bdb4c..263df8b387 100644
--- a/devel/_sources/univariate/univariate_forecasting_with_exogenous_variables.ipynb.txt
+++ b/devel/_sources/univariate/univariate_forecasting_with_exogenous_variables.ipynb.txt
@@ -589,14 +589,14 @@
    "metadata": {},
    "source": [
     "### Conditional Mean uses target alignment\n",
-    "When modeling the conditional mean in an AR-X, HAR-X, or LS model, the $X$ data is target-aligned.  This requires that when modeling the mean of ``y[t]``, the correct values of $X$ must appear in ``x[t]``. Mathematically, the $X$ matrix used when estimating a model should have the structure\n",
+    "When modeling the conditional mean in an AR-X, HAR-X, or LS model, the $X$ data is target-aligned.  This requires that when modeling the mean of ``y[t]``, the correct values of $X$ must appear in ``x[t]``. Mathematically, the $X$ matrix used when estimating a model should have the structure (using the Python indexing convention of a T-element data set having indices 0, 1, ..., T-1):\n",
     "\n",
     "$$\n",
     "\\left[\\begin{array}{c}\n",
     "X_{0}\\\\\n",
     "X_{1}\\\\\n",
     "\\vdots\\\\\n",
-    "X_{t-1}\n",
+    "X_{T-1}\n",
     "\\end{array}\\right]\n",
     "$$\n",
     "\n",
@@ -609,22 +609,24 @@
     "\n",
     "$$\n",
     "\\left[\\begin{array}{cccc}\n",
-    "E\\left[X_{1|0}\\right] & E\\left[X_{2|0}\\right] & \\ldots & E\\left[X_{h|0}\\right]\\\\\n",
-    "E\\left[X_{2|1}\\right] & E\\left[X_{3|1}\\right] & \\ldots & E\\left[X_{h+1|1}\\right]\\\\\n",
+    "E\\left[X_{1}|\\mathcal{F}_0\\right] & E\\left[X_{2}|\\mathcal{F}_0\\right] & \\ldots & E\\left[X_{h|\\mathcal{F}_0}\\right]\\\\\n",
+    "E\\left[X_{2}|\\mathcal{F}_1\\right] & E\\left[X_{3}|\\mathcal{F}_0\\right] & \\ldots & E\\left[X_{h+1}|\\mathcal{F}_1\\right]\\\\\n",
     "\\vdots & \\vdots & \\vdots & \\vdots\\\\\n",
-    "E\\left[X_{T|T-1}\\right] & E\\left[X_{T+1|T-1}\\right] & \\ldots & E\\left[X_{T+h-1|T-1}\\right]\n",
+    "E\\left[X_{T}|\\mathcal{F}_{T-1}\\right] & E\\left[X_{T+1}|\\mathcal{F}_{T-1}\\right] & \\ldots & E\\left[X_{T+h-1}|\\mathcal{F}_{T-1}\\right]\n",
     "\\end{array}\\right]\n",
     "$$\n",
     "\n",
+    "where $|\\mathcal{F}_{s}$ is the time-$s$ information set.\n",
+    "\n",
     "If you use the same ``x`` value in the model when forecasting, you will see different values due to this alignment difference. Naively using the same ``x`` values ie equivalent to setting\n",
     "\n",
-    "$$ E\\left[X_{j|j-1}] \\right] = X_{j-1} $$\n",
+    "$$ E\\left[X_{s}|\\mathcal{F}_{s-1} \\right] = X_{s-1} $$\n",
     "\n",
     "In general this would not be correct when forecasting, and will always produce forecasts that differ from the conditional mean.  In order to recover the conditional mean using the forecast function, it is necessary to ``shift`` the $X$ values by -1, so that once shifted, the ``x`` values will have the relationship\n",
     "\n",
-    "$$ E\\left[X_{j|j-1}] \\right] = X_{j} .$$\n",
+    "$$ E\\left[X_{s}|\\mathcal{F}_{s-1} \\right] = X_{s} .$$\n",
     "\n",
-    "Here we shift the $X$ data by ``-1`` so ``x[j]`` is treated as being in the information set for ``y[j-1]``. Also note that the final forecast is ``NaN``. Conceptually this must be the case because the value of $X$ at 999 should be ahead of 999 (i.e., at observation 1,000), and we do not have this value. "
+    "Here we shift the $X$ data by ``-1`` so ``x[s]`` is treated as being in the information set for ``y[s-1]``. Also, note that the final forecast is ``NaN``. Conceptually this must be the case because the value of $X$ at 999 should be ahead of 999 (i.e., at observation 1,000), and we do not have this value. "
    ]
   },
   {
diff --git a/devel/_static/documentation_options.js b/devel/_static/documentation_options.js
index ecafa1c4e5..6e456f333b 100644
--- a/devel/_static/documentation_options.js
+++ b/devel/_static/documentation_options.js
@@ -1,5 +1,5 @@
 const DOCUMENTATION_OPTIONS = {
-    VERSION: 'v7.1.1 (+16)',
+    VERSION: 'v7.1.1 (+18)',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/devel/api.html b/devel/api.html
index efaa14ffc8..ecfe7ae6d2 100644
--- a/devel/api.html
+++ b/devel/api.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>API Reference - arch v7.1.1 (+16)</title>
+        <title>API Reference - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/background.html b/devel/bootstrap/background.html
index eac2297464..6960389135 100644
--- a/devel/bootstrap/background.html
+++ b/devel/bootstrap/background.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>References - arch v7.1.1 (+16)</title>
+        <title>References - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/bootstrap.html b/devel/bootstrap/bootstrap.html
index ddb0eff57f..87336126d2 100644
--- a/devel/bootstrap/bootstrap.html
+++ b/devel/bootstrap/bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Bootstrapping - arch v7.1.1 (+16)</title>
+        <title>Bootstrapping - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/bootstrap_examples.html b/devel/bootstrap/bootstrap_examples.html
index af36e8c564..74dfd5cc71 100644
--- a/devel/bootstrap/bootstrap_examples.html
+++ b/devel/bootstrap/bootstrap_examples.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Bootstrap Examples - arch v7.1.1 (+16)</title>
+        <title>Bootstrap Examples - arch v7.1.1 (+18)</title>
       
     
 
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
@@ -1104,9 +1104,9 @@ <h3 id="The-wrapper-function">The wrapper function<a class="headerlink" href="#T
 <div class="output_area docutils container">
 <div class="highlight"><pre>
           Const           gre       gpa
-Const  0.429664 -1.041260e-04 -0.105644
-gre   -0.000104  4.522731e-07 -0.000049
-gpa   -0.105644 -4.891813e-05  0.039339
+Const  0.419742 -8.085836e-05 -0.106778
+gre   -0.000081  4.036474e-07 -0.000047
+gpa   -0.106778 -4.698808e-05  0.039316
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
@@ -1125,14 +1125,14 @@ <h3 id="The-wrapper-function">The wrapper function<a class="headerlink" href="#T
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Const    0.655487
-gre      0.000673
-gpa      0.198341
+Const    0.647875
+gre      0.000635
+gpa      0.198283
 dtype: float64
 T-stats
-Const   -4.582141
-gre      2.442388
-gpa      2.291887
+Const   -4.635980
+gre      2.585318
+gpa      2.292557
 dtype: float64
 </pre></div></div>
 </div>
@@ -1152,8 +1152,8 @@ <h3 id="The-wrapper-function">The wrapper function<a class="headerlink" href="#T
 <div class="output_area docutils container">
 <div class="highlight"><pre>
           Const       gre       gpa
-Lower -4.188488  0.000359  0.026381
-Upper -1.621971  0.002868  0.835709
+Lower -4.113994  0.000367  0.053126
+Upper -1.727693  0.002906  0.802549
 </pre></div></div>
 </div>
 <h3 id="Speeding-things-up">Speeding things up<a class="headerlink" href="#Speeding-things-up" title="Link to this heading">¶</a></h3>
@@ -1186,9 +1186,9 @@ <h3 id="Speeding-things-up">Speeding things up<a class="headerlink" href="#Speed
 <div class="output_area docutils container">
 <div class="highlight"><pre>
           Const           gre       gpa
-Const  0.429664 -1.041260e-04 -0.105644
-gre   -0.000104  4.522731e-07 -0.000049
-gpa   -0.105644 -4.891813e-05  0.039339
+Const  0.419742 -8.085836e-05 -0.106778
+gre   -0.000081  4.036474e-07 -0.000047
+gpa   -0.106778 -4.698808e-05  0.039316
 </pre></div></div>
 </div>
 <h2 id="Bootstrapping-Uneven-Length-Samples">Bootstrapping Uneven Length Samples<a class="headerlink" href="#Bootstrapping-Uneven-Length-Samples" title="Link to this heading">¶</a></h2>
diff --git a/devel/bootstrap/bootstrap_examples.ipynb b/devel/bootstrap/bootstrap_examples.ipynb
index 7bcaa2b744..64a9d84643 100644
--- a/devel/bootstrap/bootstrap_examples.ipynb
+++ b/devel/bootstrap/bootstrap_examples.ipynb
@@ -19,10 +19,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:16.725192Z",
-     "iopub.status.busy": "2024-11-04T11:35:16.724932Z",
-     "iopub.status.idle": "2024-11-04T11:35:18.315224Z",
-     "shell.execute_reply": "2024-11-04T11:35:18.314500Z"
+     "iopub.execute_input": "2024-11-04T12:31:10.945187Z",
+     "iopub.status.busy": "2024-11-04T12:31:10.944928Z",
+     "iopub.status.idle": "2024-11-04T12:31:12.552282Z",
+     "shell.execute_reply": "2024-11-04T12:31:12.551450Z"
     }
    },
    "outputs": [],
@@ -56,10 +56,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:18.319261Z",
-     "iopub.status.busy": "2024-11-04T11:35:18.318100Z",
-     "iopub.status.idle": "2024-11-04T11:35:18.657313Z",
-     "shell.execute_reply": "2024-11-04T11:35:18.656435Z"
+     "iopub.execute_input": "2024-11-04T12:31:12.555943Z",
+     "iopub.status.busy": "2024-11-04T12:31:12.555548Z",
+     "iopub.status.idle": "2024-11-04T12:31:12.868251Z",
+     "shell.execute_reply": "2024-11-04T12:31:12.867421Z"
     }
    },
    "outputs": [],
@@ -84,10 +84,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:18.662633Z",
-     "iopub.status.busy": "2024-11-04T11:35:18.661568Z",
-     "iopub.status.idle": "2024-11-04T11:35:18.682881Z",
-     "shell.execute_reply": "2024-11-04T11:35:18.682129Z"
+     "iopub.execute_input": "2024-11-04T12:31:12.871831Z",
+     "iopub.status.busy": "2024-11-04T12:31:12.871437Z",
+     "iopub.status.idle": "2024-11-04T12:31:12.886289Z",
+     "shell.execute_reply": "2024-11-04T12:31:12.885567Z"
     }
    },
    "outputs": [
@@ -124,10 +124,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:18.735208Z",
-     "iopub.status.busy": "2024-11-04T11:35:18.734501Z",
-     "iopub.status.idle": "2024-11-04T11:35:18.739528Z",
-     "shell.execute_reply": "2024-11-04T11:35:18.738772Z"
+     "iopub.execute_input": "2024-11-04T12:31:12.944797Z",
+     "iopub.status.busy": "2024-11-04T12:31:12.944441Z",
+     "iopub.status.idle": "2024-11-04T12:31:12.949734Z",
+     "shell.execute_reply": "2024-11-04T12:31:12.948960Z"
     }
    },
    "outputs": [],
@@ -151,10 +151,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:18.742355Z",
-     "iopub.status.busy": "2024-11-04T11:35:18.741903Z",
-     "iopub.status.idle": "2024-11-04T11:35:18.751770Z",
-     "shell.execute_reply": "2024-11-04T11:35:18.751072Z"
+     "iopub.execute_input": "2024-11-04T12:31:12.953187Z",
+     "iopub.status.busy": "2024-11-04T12:31:12.952686Z",
+     "iopub.status.idle": "2024-11-04T12:31:12.965241Z",
+     "shell.execute_reply": "2024-11-04T12:31:12.964594Z"
     }
    },
    "outputs": [
@@ -207,10 +207,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:18.754634Z",
-     "iopub.status.busy": "2024-11-04T11:35:18.754216Z",
-     "iopub.status.idle": "2024-11-04T11:35:20.295403Z",
-     "shell.execute_reply": "2024-11-04T11:35:20.294559Z"
+     "iopub.execute_input": "2024-11-04T12:31:12.969480Z",
+     "iopub.status.busy": "2024-11-04T12:31:12.968449Z",
+     "iopub.status.idle": "2024-11-04T12:31:14.539899Z",
+     "shell.execute_reply": "2024-11-04T12:31:14.539046Z"
     }
    },
    "outputs": [
@@ -243,10 +243,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:20.298581Z",
-     "iopub.status.busy": "2024-11-04T11:35:20.298099Z",
-     "iopub.status.idle": "2024-11-04T11:35:20.830047Z",
-     "shell.execute_reply": "2024-11-04T11:35:20.829238Z"
+     "iopub.execute_input": "2024-11-04T12:31:14.543113Z",
+     "iopub.status.busy": "2024-11-04T12:31:14.542676Z",
+     "iopub.status.idle": "2024-11-04T12:31:15.039984Z",
+     "shell.execute_reply": "2024-11-04T12:31:15.039158Z"
     }
    },
    "outputs": [
@@ -282,10 +282,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:20.834281Z",
-     "iopub.status.busy": "2024-11-04T11:35:20.833230Z",
-     "iopub.status.idle": "2024-11-04T11:35:21.320626Z",
-     "shell.execute_reply": "2024-11-04T11:35:21.319786Z"
+     "iopub.execute_input": "2024-11-04T12:31:15.044165Z",
+     "iopub.status.busy": "2024-11-04T12:31:15.043108Z",
+     "iopub.status.idle": "2024-11-04T12:31:15.532010Z",
+     "shell.execute_reply": "2024-11-04T12:31:15.531237Z"
     }
    },
    "outputs": [
@@ -317,10 +317,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:21.325548Z",
-     "iopub.status.busy": "2024-11-04T11:35:21.324333Z",
-     "iopub.status.idle": "2024-11-04T11:35:21.336128Z",
-     "shell.execute_reply": "2024-11-04T11:35:21.335220Z"
+     "iopub.execute_input": "2024-11-04T12:31:15.536431Z",
+     "iopub.status.busy": "2024-11-04T12:31:15.535351Z",
+     "iopub.status.idle": "2024-11-04T12:31:15.545733Z",
+     "shell.execute_reply": "2024-11-04T12:31:15.544977Z"
     }
    },
    "outputs": [
@@ -354,10 +354,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:21.341227Z",
-     "iopub.status.busy": "2024-11-04T11:35:21.339977Z",
-     "iopub.status.idle": "2024-11-04T11:35:21.350939Z",
-     "shell.execute_reply": "2024-11-04T11:35:21.350064Z"
+     "iopub.execute_input": "2024-11-04T12:31:15.549799Z",
+     "iopub.status.busy": "2024-11-04T12:31:15.548781Z",
+     "iopub.status.idle": "2024-11-04T12:31:15.559156Z",
+     "shell.execute_reply": "2024-11-04T12:31:15.558361Z"
     }
    },
    "outputs": [
@@ -389,10 +389,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:21.355564Z",
-     "iopub.status.busy": "2024-11-04T11:35:21.354402Z",
-     "iopub.status.idle": "2024-11-04T11:35:23.023448Z",
-     "shell.execute_reply": "2024-11-04T11:35:23.022515Z"
+     "iopub.execute_input": "2024-11-04T12:31:15.563414Z",
+     "iopub.status.busy": "2024-11-04T12:31:15.562342Z",
+     "iopub.status.idle": "2024-11-04T12:31:17.022468Z",
+     "shell.execute_reply": "2024-11-04T12:31:17.021625Z"
     }
    },
    "outputs": [
@@ -436,10 +436,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:23.028433Z",
-     "iopub.status.busy": "2024-11-04T11:35:23.027278Z",
-     "iopub.status.idle": "2024-11-04T11:35:23.051137Z",
-     "shell.execute_reply": "2024-11-04T11:35:23.050247Z"
+     "iopub.execute_input": "2024-11-04T12:31:17.026103Z",
+     "iopub.status.busy": "2024-11-04T12:31:17.025554Z",
+     "iopub.status.idle": "2024-11-04T12:31:17.044623Z",
+     "shell.execute_reply": "2024-11-04T12:31:17.043879Z"
     }
    },
    "outputs": [
@@ -486,10 +486,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:23.056182Z",
-     "iopub.status.busy": "2024-11-04T11:35:23.055000Z",
-     "iopub.status.idle": "2024-11-04T11:35:23.375085Z",
-     "shell.execute_reply": "2024-11-04T11:35:23.374355Z"
+     "iopub.execute_input": "2024-11-04T12:31:17.049126Z",
+     "iopub.status.busy": "2024-11-04T12:31:17.047829Z",
+     "iopub.status.idle": "2024-11-04T12:31:17.344379Z",
+     "shell.execute_reply": "2024-11-04T12:31:17.343557Z"
     }
    },
    "outputs": [
@@ -539,10 +539,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:23.379262Z",
-     "iopub.status.busy": "2024-11-04T11:35:23.378181Z",
-     "iopub.status.idle": "2024-11-04T11:35:23.383018Z",
-     "shell.execute_reply": "2024-11-04T11:35:23.382356Z"
+     "iopub.execute_input": "2024-11-04T12:31:17.347138Z",
+     "iopub.status.busy": "2024-11-04T12:31:17.346821Z",
+     "iopub.status.idle": "2024-11-04T12:31:17.350186Z",
+     "shell.execute_reply": "2024-11-04T12:31:17.349663Z"
     }
    },
    "outputs": [],
@@ -563,10 +563,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:23.387046Z",
-     "iopub.status.busy": "2024-11-04T11:35:23.386078Z",
-     "iopub.status.idle": "2024-11-04T11:35:23.396928Z",
-     "shell.execute_reply": "2024-11-04T11:35:23.396207Z"
+     "iopub.execute_input": "2024-11-04T12:31:17.352440Z",
+     "iopub.status.busy": "2024-11-04T12:31:17.352108Z",
+     "iopub.status.idle": "2024-11-04T12:31:17.360283Z",
+     "shell.execute_reply": "2024-11-04T12:31:17.358998Z"
     }
    },
    "outputs": [
@@ -600,10 +600,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:23.400976Z",
-     "iopub.status.busy": "2024-11-04T11:35:23.400010Z",
-     "iopub.status.idle": "2024-11-04T11:35:25.843720Z",
-     "shell.execute_reply": "2024-11-04T11:35:25.842972Z"
+     "iopub.execute_input": "2024-11-04T12:31:17.363822Z",
+     "iopub.status.busy": "2024-11-04T12:31:17.362602Z",
+     "iopub.status.idle": "2024-11-04T12:31:19.767092Z",
+     "shell.execute_reply": "2024-11-04T12:31:19.766259Z"
     }
    },
    "outputs": [
@@ -612,9 +612,9 @@
      "output_type": "stream",
      "text": [
       "          Const           gre       gpa\n",
-      "Const  0.441247 -8.654726e-05 -0.112090\n",
-      "gre   -0.000087  4.340024e-07 -0.000050\n",
-      "gpa   -0.112090 -5.041656e-05  0.041426\n"
+      "Const  0.401108 -6.675251e-05 -0.104058\n",
+      "gre   -0.000067  4.266221e-07 -0.000056\n",
+      "gpa   -0.104058 -5.556570e-05  0.040157\n"
      ]
     }
    ],
@@ -632,10 +632,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:25.847883Z",
-     "iopub.status.busy": "2024-11-04T11:35:25.846856Z",
-     "iopub.status.idle": "2024-11-04T11:35:25.855223Z",
-     "shell.execute_reply": "2024-11-04T11:35:25.854475Z"
+     "iopub.execute_input": "2024-11-04T12:31:19.771418Z",
+     "iopub.status.busy": "2024-11-04T12:31:19.770153Z",
+     "iopub.status.idle": "2024-11-04T12:31:19.782900Z",
+     "shell.execute_reply": "2024-11-04T12:31:19.778223Z"
     }
    },
    "outputs": [
@@ -643,14 +643,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Const    0.664264\n",
-      "gre      0.000659\n",
-      "gpa      0.203533\n",
+      "Const    0.633331\n",
+      "gre      0.000653\n",
+      "gpa      0.200393\n",
       "dtype: float64\n",
       "T-stats\n",
-      "Const   -4.521599\n",
-      "gre      2.493269\n",
-      "gpa      2.233426\n",
+      "Const   -4.742445\n",
+      "gre      2.514742\n",
+      "gpa      2.268421\n",
       "dtype: float64\n"
      ]
     }
@@ -667,10 +667,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:25.859341Z",
-     "iopub.status.busy": "2024-11-04T11:35:25.858297Z",
-     "iopub.status.idle": "2024-11-04T11:35:28.334175Z",
-     "shell.execute_reply": "2024-11-04T11:35:28.333294Z"
+     "iopub.execute_input": "2024-11-04T12:31:19.786974Z",
+     "iopub.status.busy": "2024-11-04T12:31:19.785938Z",
+     "iopub.status.idle": "2024-11-04T12:31:22.177071Z",
+     "shell.execute_reply": "2024-11-04T12:31:22.176276Z"
     }
    },
    "outputs": [
@@ -679,8 +679,8 @@
      "output_type": "stream",
      "text": [
       "          Const       gre       gpa\n",
-      "Lower -4.102216  0.000379  0.056749\n",
-      "Upper -1.810706  0.002791  0.817384\n"
+      "Lower -4.137307  0.000301  0.063763\n",
+      "Upper -1.817368  0.003014  0.827887\n"
      ]
     }
    ],
@@ -709,10 +709,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:28.337505Z",
-     "iopub.status.busy": "2024-11-04T11:35:28.337163Z",
-     "iopub.status.idle": "2024-11-04T11:35:28.342207Z",
-     "shell.execute_reply": "2024-11-04T11:35:28.341472Z"
+     "iopub.execute_input": "2024-11-04T12:31:22.180340Z",
+     "iopub.status.busy": "2024-11-04T12:31:22.180049Z",
+     "iopub.status.idle": "2024-11-04T12:31:22.185111Z",
+     "shell.execute_reply": "2024-11-04T12:31:22.184252Z"
     }
    },
    "outputs": [],
@@ -726,10 +726,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:28.346266Z",
-     "iopub.status.busy": "2024-11-04T11:35:28.344991Z",
-     "iopub.status.idle": "2024-11-04T11:35:30.534149Z",
-     "shell.execute_reply": "2024-11-04T11:35:30.533443Z"
+     "iopub.execute_input": "2024-11-04T12:31:22.188657Z",
+     "iopub.status.busy": "2024-11-04T12:31:22.188344Z",
+     "iopub.status.idle": "2024-11-04T12:31:24.484579Z",
+     "shell.execute_reply": "2024-11-04T12:31:24.483834Z"
     }
    },
    "outputs": [
@@ -738,9 +738,9 @@
      "output_type": "stream",
      "text": [
       "          Const           gre       gpa\n",
-      "Const  0.441247 -8.654726e-05 -0.112090\n",
-      "gre   -0.000087  4.340024e-07 -0.000050\n",
-      "gpa   -0.112090 -5.041656e-05  0.041426\n"
+      "Const  0.401108 -6.675251e-05 -0.104058\n",
+      "gre   -0.000067  4.266221e-07 -0.000056\n",
+      "gpa   -0.104058 -5.556570e-05  0.040157\n"
      ]
     }
    ],
@@ -766,10 +766,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:30.538208Z",
-     "iopub.status.busy": "2024-11-04T11:35:30.537187Z",
-     "iopub.status.idle": "2024-11-04T11:36:24.269043Z",
-     "shell.execute_reply": "2024-11-04T11:36:24.268330Z"
+     "iopub.execute_input": "2024-11-04T12:31:24.487788Z",
+     "iopub.status.busy": "2024-11-04T12:31:24.487505Z",
+     "iopub.status.idle": "2024-11-04T12:32:17.944723Z",
+     "shell.execute_reply": "2024-11-04T12:32:17.943993Z"
     }
    },
    "outputs": [
diff --git a/devel/bootstrap/confidence-intervals.html b/devel/bootstrap/confidence-intervals.html
index 7a7fbd9453..60b5b72c4b 100644
--- a/devel/bootstrap/confidence-intervals.html
+++ b/devel/bootstrap/confidence-intervals.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Confidence Intervals - arch v7.1.1 (+16)</title>
+        <title>Confidence Intervals - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html b/devel/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html
index dc78393a7b..0cd83f8b93 100644
--- a/devel/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html
+++ b/devel/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/arch.bootstrap.IIDBootstrap.html b/devel/bootstrap/generated/arch.bootstrap.IIDBootstrap.html
index a2352455db..495311ffbd 100644
--- a/devel/bootstrap/generated/arch.bootstrap.IIDBootstrap.html
+++ b/devel/bootstrap/generated/arch.bootstrap.IIDBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html b/devel/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html
index cab13b557f..44c248b491 100644
--- a/devel/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html
+++ b/devel/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html b/devel/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html
index b71520ebda..1b385765e0 100644
--- a/devel/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html
+++ b/devel/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html b/devel/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html
index c07d828a03..ef40baf1b9 100644
--- a/devel/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html
+++ b/devel/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/arch.bootstrap.optimal_block_length.html b/devel/bootstrap/generated/arch.bootstrap.optimal_block_length.html
index b16c9a5054..e73701aa6f 100644
--- a/devel/bootstrap/generated/arch.bootstrap.optimal_block_length.html
+++ b/devel/bootstrap/generated/arch.bootstrap.optimal_block_length.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.optimal_block_length - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.optimal_block_length - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html
index 9be31da61e..b58752cf0a 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.apply - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.apply - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html
index 5a906f5f8c..a6de788670 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.bootstrap - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.bootstrap - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html
index 0f9c8241d1..6450f3de08 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.clone - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.clone - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html
index 24b0a3143f..aa33468cee 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.conf_int - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.conf_int - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html
index eb335ad3ab..ba7829a65d 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.cov - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.cov - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html
index 47710e25c9..4e67246a53 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.generator - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.generator - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html
index f7016b4eb0..b202a6a5e7 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.get_state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.get_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html
index 1736297500..8bcc2b60ae 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.index - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.index - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html
index dbdae31ec3..715ff47aa1 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.random_state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.random_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html
index e23fc63559..e4be530fe0 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.reset - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.reset - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html
index ab7a008459..7e20920800 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.seed - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.seed - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html
index 1fba3ff07a..1ee166667c 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.set_state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.set_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html
index 6e18fc4af6..1972de1c85 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html
index 150bc81517..5f329f7f69 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.update_indices - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.update_indices - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html
index 39486ee331..1a40720bc9 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.var - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.var - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html
index cdf44f954d..66dfe2e433 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.apply - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap.apply - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html
index 016667cc1a..52140187bb 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.bootstrap - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap.bootstrap - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html
index 7ecef6b78b..63cb5a1c55 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.clone - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap.clone - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html
index c3d4f4f794..692d61f2f9 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.conf_int - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap.conf_int - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html
index f1d9866a9d..3d2775efe1 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.cov - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap.cov - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html
index 0ef7093618..b968bae7e2 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.generator - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap.generator - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html
index a672108c73..178544d0e5 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.get_state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap.get_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html
index b561376111..c194e76470 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.index - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap.index - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html
index a4dbf7fc7a..a4219417f0 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.random_state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap.random_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html
index e5e9c41d67..a532fb4932 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.reset - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap.reset - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html
index 279a31d14f..29cb05e558 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.seed - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap.seed - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html
index 87d4f8ac47..fd5603daf0 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.set_state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap.set_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html
index 64649cb928..cc2b45fc30 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap.state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html
index 385618431f..71986e410b 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.update_indices - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap.update_indices - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html
index cf9f35174a..60027d8948 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.var - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IIDBootstrap.var - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html
index ff720acae7..0f57447f1c 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.apply - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.apply - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html
index 76794d6d39..94f18fc33a 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.bootstrap - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.bootstrap - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html
index 78d3bb2cf8..c28703444b 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.clone - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.clone - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html
index 598e10abd5..bdd69fa873 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.conf_int - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.conf_int - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html
index cd0965f7a1..343dd8f9b3 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.cov - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.cov - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html
index 7364594a89..5c3fcfde03 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.generator - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.generator - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html
index c85b64c362..532fc852ff 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.get_state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.get_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html
index b0c9700562..6137e1eae7 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.index - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.index - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html
index 77d0424813..17affdc8de 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.random_state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.random_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html
index 7dfdaa4abb..36232d2184 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.reset - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.reset - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html
index 4bc5d9fa1f..d178aa2610 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.seed - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.seed - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html
index 210d81f5b3..33c2e844f2 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.set_state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.set_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html
index 9989eaeb50..bff6438da9 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html
index baa825ad7a..a7f1c6dd27 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.update_indices - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.update_indices - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html
index 60abe5062b..466a39beed 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.var - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.var - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html
index c1c47832ef..33b2bf1a49 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.apply - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.apply - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html
index 46ce054ee3..c52c5530a3 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.bootstrap - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.bootstrap - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html
index 19d356639f..75ea01a57b 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.clone - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.clone - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html
index 09f864514b..5db10257e6 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.conf_int - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.conf_int - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html
index a6fab6d2cb..523642d772 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.cov - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.cov - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html
index 589f1d950c..8ca3a78a50 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.generator - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.generator - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html
index 9bb64e587f..91802a2d34 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.get_state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.get_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html
index 5b4d8759f4..0c5da1c11d 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.index - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.index - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html
index 4004fe4d54..a142863cb1 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.random_state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.random_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html
index e68059820b..b0f746ea83 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.reset - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.reset - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html
index 75e350045d..bb5c9ecf40 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.seed - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.seed - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html
index 482bbf1468..6aa4f2f23f 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.set_state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.set_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html
index 18189a1164..6b266b4360 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html
index 4e7319d79e..6ceeab9527 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.update_indices - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.update_indices - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html
index 7d2c830d0c..338773bc9e 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.var - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.var - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html
index 0397e9f9f2..32cb6e598b 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.apply - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap.apply - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html
index 094d94e59a..95ff07292c 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.bootstrap - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap.bootstrap - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html
index 6407a1701c..8aeecc83ef 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.clone - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap.clone - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.conf_int.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.conf_int.html
index b3103d63fe..188ee7e8dc 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.conf_int.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.conf_int - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap.conf_int - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html
index 25097fc746..e17ce55dd8 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.cov - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap.cov - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html
index 9831167823..8596eacc9b 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.generator - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap.generator - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html
index 112da5e1d3..0f94847c3e 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.get_state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap.get_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html
index 25f79c6da7..d433d5ff5e 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.index - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap.index - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html
index 32ec1aef66..098d4579c8 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.random_state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap.random_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html
index 3fff89a947..866311f262 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.reset - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap.reset - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html
index 89fb84b868..b39917942e 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.seed - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap.seed - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html
index ce92aca8ea..06dd7b9014 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.set_state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap.set_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html
index daa3643870..bf2ea2904f 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.state - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap.state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html
index 1613bd5b88..75e802860b 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.update_indices - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap.update_indices - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html
index b3768cf665..f481cdfe06 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.var - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StationaryBootstrap.var - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/iid-bootstraps.html b/devel/bootstrap/iid-bootstraps.html
index 5671a653f4..e85b7b59ae 100644
--- a/devel/bootstrap/iid-bootstraps.html
+++ b/devel/bootstrap/iid-bootstraps.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Independent, Identical Distributed Data (i.i.d.) - arch v7.1.1 (+16)</title>
+        <title>Independent, Identical Distributed Data (i.i.d.) - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/low-level-interface.html b/devel/bootstrap/low-level-interface.html
index a2f6116bda..4c614bdc4d 100644
--- a/devel/bootstrap/low-level-interface.html
+++ b/devel/bootstrap/low-level-interface.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Low-level Interfaces - arch v7.1.1 (+16)</title>
+        <title>Low-level Interfaces - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/parameter-covariance-estimation.html b/devel/bootstrap/parameter-covariance-estimation.html
index 5736aa87bc..26a30e5a4d 100644
--- a/devel/bootstrap/parameter-covariance-estimation.html
+++ b/devel/bootstrap/parameter-covariance-estimation.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Covariance Estimation - arch v7.1.1 (+16)</title>
+        <title>Covariance Estimation - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/semiparametric-parametric-bootstrap.html b/devel/bootstrap/semiparametric-parametric-bootstrap.html
index 678084bdfa..112eac99bc 100644
--- a/devel/bootstrap/semiparametric-parametric-bootstrap.html
+++ b/devel/bootstrap/semiparametric-parametric-bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Semiparametric Bootstraps - arch v7.1.1 (+16)</title>
+        <title>Semiparametric Bootstraps - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/timeseries-bootstraps.html b/devel/bootstrap/timeseries-bootstraps.html
index 587a2d6f02..033352ba65 100644
--- a/devel/bootstrap/timeseries-bootstraps.html
+++ b/devel/bootstrap/timeseries-bootstraps.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Time-series Bootstraps - arch v7.1.1 (+16)</title>
+        <title>Time-series Bootstraps - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/changes.html b/devel/changes.html
index da81893a53..69e9d4f777 100644
--- a/devel/changes.html
+++ b/devel/changes.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Change Logs - arch v7.1.1 (+16)</title>
+        <title>Change Logs - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/changes/1.0.html b/devel/changes/1.0.html
index 5ba363c411..b4530354d1 100644
--- a/devel/changes/1.0.html
+++ b/devel/changes/1.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 1 - arch v7.1.1 (+16)</title>
+        <title>Version 1 - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/changes/2.0.html b/devel/changes/2.0.html
index acbac20b33..927b811b1b 100644
--- a/devel/changes/2.0.html
+++ b/devel/changes/2.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 2 - arch v7.1.1 (+16)</title>
+        <title>Version 2 - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/changes/3.0.html b/devel/changes/3.0.html
index 95f9841d8c..af15343a90 100644
--- a/devel/changes/3.0.html
+++ b/devel/changes/3.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 3 - arch v7.1.1 (+16)</title>
+        <title>Version 3 - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
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@@ -92,7 +92,7 @@
 
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index a2fb04c85e..cdeb419a34 100644
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+++ b/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided_strict.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.covariance.kernel.CovarianceEstimate.one_sided_strict - arch v7.1.1 (+18)</title>
       
     
 
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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--- a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.short_run.html
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.covariance.kernel.CovarianceEstimate.short_run - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 35800165e5..72f88d1a85 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimator.bandwidth.html
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.covariance.kernel.CovarianceEstimator.bandwidth - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimator.bandwidth_scale.html b/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimator.bandwidth_scale.html
index baa3c4ecf6..1a555a04d8 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.CovarianceEstimator.bandwidth_scale - arch v7.1.1 (+16)</title>
+        <title>arch.covariance.kernel.CovarianceEstimator.bandwidth_scale - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index d012b334d8..299b5a2497 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimator.centered.html
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.covariance.kernel.CovarianceEstimator.centered - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 394527161f..3b0c821dd5 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimator.cov.html
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.covariance.kernel.CovarianceEstimator.cov - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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--- a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimator.force_int.html
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.covariance.kernel.CovarianceEstimator.force_int - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimator.kernel_const.html b/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimator.kernel_const.html
index 2ea1c8cc03..5f17ce929b 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimator.kernel_const.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.CovarianceEstimator.kernel_const - arch v7.1.1 (+16)</title>
+        <title>arch.covariance.kernel.CovarianceEstimator.kernel_const - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimator.kernel_weights.html b/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimator.kernel_weights.html
index 2c5d0c7091..45698402e6 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimator.kernel_weights.html
+++ b/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimator.kernel_weights.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.CovarianceEstimator.kernel_weights - arch v7.1.1 (+16)</title>
+        <title>arch.covariance.kernel.CovarianceEstimator.kernel_weights - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 591ed237cd..ee3a31d48f 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimator.name.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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+    arch v7.1.1 (+18)
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+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.covariance.kernel.QuadraticSpectral.bandwidth_scale - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 243b6206b6..5ec853304b 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.cov.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 3853aed1f0..1bca250eb4 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index f6ba6d2e6c..1c5265bd18 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.opt_bandwidth.html b/devel/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.opt_bandwidth.html
index f6d61f694c..221b2050cb 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.opt_bandwidth.html
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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+        <title>arch.covariance.kernel.TukeyParzen.opt_bandwidth - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.rate.html b/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.rate.html
index f886781bd5..1c4bbb645b 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.rate.html
+++ b/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.rate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen.rate - arch v7.1.1 (+16)</title>
+        <title>arch.covariance.kernel.TukeyParzen.rate - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/genindex.html b/devel/genindex.html
index 8f904b1417..675333c9e3 100644
--- a/devel/genindex.html
+++ b/devel/genindex.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>None - arch v7.1.1 (+16)</title>
+        <title>None - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/index.html b/devel/index.html
index de7905d075..d59df45b4f 100644
--- a/devel/index.html
+++ b/devel/index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch v7.1.1 (+16)</title>
+        <title>arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="#" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="#" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -197,10 +197,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="#" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="#" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/background.html b/devel/multiple-comparison/background.html
index 69b319ef36..b4a695504e 100644
--- a/devel/multiple-comparison/background.html
+++ b/devel/multiple-comparison/background.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>References - arch v7.1.1 (+16)</title>
+        <title>References - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/arch.bootstrap.MCS.html b/devel/multiple-comparison/generated/arch.bootstrap.MCS.html
index bfdec005c5..ce9d9d6f53 100644
--- a/devel/multiple-comparison/generated/arch.bootstrap.MCS.html
+++ b/devel/multiple-comparison/generated/arch.bootstrap.MCS.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MCS - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/arch.bootstrap.SPA.html b/devel/multiple-comparison/generated/arch.bootstrap.SPA.html
index 561cd4365d..45c729b647 100644
--- a/devel/multiple-comparison/generated/arch.bootstrap.SPA.html
+++ b/devel/multiple-comparison/generated/arch.bootstrap.SPA.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.SPA - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/arch.bootstrap.StepM.html b/devel/multiple-comparison/generated/arch.bootstrap.StepM.html
index 56c1c4b443..c2c1d435b8 100644
--- a/devel/multiple-comparison/generated/arch.bootstrap.StepM.html
+++ b/devel/multiple-comparison/generated/arch.bootstrap.StepM.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StepM - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html
index 9bed21bd79..1d84c1b74f 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.compute - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MCS.compute - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html
index 7e9dcf41b9..2860872fec 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.excluded - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MCS.excluded - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html
index b64bc88357..86ea34b8da 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.included - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MCS.included - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html
index a9b3e0a3a8..e2a2b32c2a 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.pvalues - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MCS.pvalues - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html
index 936f0bf3fe..730403bb4e 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.reset - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MCS.reset - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html
index bedb4ff94e..bb111e872a 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.seed - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.MCS.seed - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html
index 9736f2b834..758e3fc0eb 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.better_models - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.SPA.better_models - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html
index b7576c8c9d..835c6f655b 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.compute - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.SPA.compute - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html
index 0c4eb7f375..1bddd534b2 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.critical_values - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.SPA.critical_values - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html
index 21727cf225..46c62596f2 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.pvalues - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.SPA.pvalues - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html
index 572f567df2..5a997a100a 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.reset - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.SPA.reset - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html
index 3f99fa78c0..6858e53a55 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.seed - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.SPA.seed - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html
index 32fa44dced..56cbbd31fd 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.subset - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.SPA.subset - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html
index 7736e2323d..be3cee0745 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.compute - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StepM.compute - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html
index 963fb40e3a..e603882c73 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.reset - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StepM.reset - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html
index 7dc4175c9d..4a6db711b2 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.seed - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StepM.seed - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html
index ebb902e8a2..842d3f5bba 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.superior_models - arch v7.1.1 (+16)</title>
+        <title>arch.bootstrap.StepM.superior_models - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/multiple-comparison-reference.html b/devel/multiple-comparison/multiple-comparison-reference.html
index 733cb0273a..285a15706c 100644
--- a/devel/multiple-comparison/multiple-comparison-reference.html
+++ b/devel/multiple-comparison/multiple-comparison-reference.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Module Reference - arch v7.1.1 (+16)</title>
+        <title>Module Reference - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/multiple-comparison_examples.html b/devel/multiple-comparison/multiple-comparison_examples.html
index 5b9917bd79..142b8c5795 100644
--- a/devel/multiple-comparison/multiple-comparison_examples.html
+++ b/devel/multiple-comparison/multiple-comparison_examples.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Multiple Comparisons - arch v7.1.1 (+16)</title>
+        <title>Multiple Comparisons - arch v7.1.1 (+18)</title>
       
     
 
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
@@ -687,9 +687,9 @@ <h3 id="Study-Design">Study Design<a class="headerlink" href="#Study-Design" tit
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-lower         0.312
-consistent    0.454
-upper         0.457
+lower         0.441
+consistent    0.525
+upper         0.525
 dtype: float64
 </pre></div></div>
 </div>
@@ -804,9 +804,9 @@ <h3 id="Power">Power<a class="headerlink" href="#Power" title="Link to this head
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-lower         0.0
-consistent    0.0
-upper         0.0
+lower         0.107
+consistent    0.161
+upper         0.167
 dtype: float64
 </pre></div></div>
 </div>
@@ -849,7 +849,7 @@ <h2 id="Stepwise-Multiple-Testing-(StepM)">Stepwise Multiple Testing (StepM)<a c
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Model indices:
-[&#39;171&#39;, &#39;237&#39;, &#39;263&#39;, &#39;264&#39;, &#39;277&#39;, &#39;281&#39;, &#39;294&#39;, &#39;307&#39;, &#39;313&#39;, &#39;324&#39;, &#39;325&#39;, &#39;342&#39;, &#39;345&#39;, &#39;347&#39;, &#39;350&#39;, &#39;356&#39;, &#39;357&#39;, &#39;365&#39;, &#39;367&#39;, &#39;372&#39;, &#39;379&#39;, &#39;385&#39;, &#39;389&#39;, &#39;391&#39;, &#39;392&#39;, &#39;393&#39;, &#39;395&#39;, &#39;396&#39;, &#39;399&#39;, &#39;400&#39;, &#39;401&#39;, &#39;402&#39;, &#39;403&#39;, &#39;404&#39;, &#39;406&#39;, &#39;407&#39;, &#39;409&#39;, &#39;410&#39;, &#39;412&#39;, &#39;413&#39;, &#39;415&#39;, &#39;416&#39;, &#39;417&#39;, &#39;418&#39;, &#39;420&#39;, &#39;421&#39;, &#39;425&#39;, &#39;426&#39;, &#39;427&#39;, &#39;428&#39;, &#39;430&#39;, &#39;431&#39;, &#39;432&#39;, &#39;433&#39;, &#39;434&#39;, &#39;436&#39;, &#39;439&#39;, &#39;440&#39;, &#39;441&#39;, &#39;443&#39;, &#39;445&#39;, &#39;446&#39;, &#39;448&#39;, &#39;449&#39;, &#39;450&#39;, &#39;451&#39;, &#39;452&#39;, &#39;453&#39;, &#39;454&#39;, &#39;455&#39;, &#39;457&#39;, &#39;459&#39;, &#39;461&#39;, &#39;462&#39;, &#39;465&#39;, &#39;466&#39;, &#39;467&#39;, &#39;470&#39;, &#39;471&#39;, &#39;472&#39;, &#39;473&#39;, &#39;474&#39;, &#39;475&#39;, &#39;476&#39;, &#39;478&#39;, &#39;480&#39;, &#39;481&#39;, &#39;484&#39;, &#39;485&#39;, &#39;487&#39;, &#39;488&#39;, &#39;489&#39;, &#39;490&#39;, &#39;491&#39;, &#39;492&#39;, &#39;494&#39;, &#39;495&#39;, &#39;498&#39;, &#39;499&#39;]
+[]
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
@@ -904,35 +904,35 @@ <h2 id="The-Model-Confidence-Set">The Model Confidence Set<a class="headerlink"
 MCS P-values
             Pvalue
 Model name
-model.200    0.003
-model.20     0.003
-model.160    0.024
-model.0      0.042
-model.40     0.042
-model.60     0.044
-model.120    0.056
-model.140    0.099
-model.100    0.116
-model.380    0.144
-model.80     0.195
-model.220    0.334
-model.180    0.448
-model.260    0.685
-model.320    0.692
-model.240    0.692
-model.340    0.698
-model.460    0.886
-model.300    0.908
-model.360    0.974
-model.280    0.974
-model.400    0.975
-model.420    0.975
-model.480    0.975
-model.440    1.000
+model.0      0.000
+model.100    0.004
+model.20     0.006
+model.40     0.013
+model.80     0.014
+model.380    0.034
+model.200    0.061
+model.260    0.070
+model.160    0.131
+model.180    0.315
+model.140    0.410
+model.360    0.410
+model.340    0.416
+model.60     0.416
+model.120    0.455
+model.460    0.455
+model.280    0.460
+model.300    0.460
+model.240    0.460
+model.400    0.576
+model.220    0.755
+model.320    0.755
+model.440    0.755
+model.420    0.755
+model.480    1.000
 Included
-[&#39;100&#39;, &#39;180&#39;, &#39;220&#39;, &#39;240&#39;, &#39;260&#39;, &#39;280&#39;, &#39;300&#39;, &#39;320&#39;, &#39;340&#39;, &#39;360&#39;, &#39;380&#39;, &#39;400&#39;, &#39;420&#39;, &#39;440&#39;, &#39;460&#39;, &#39;480&#39;, &#39;80&#39;]
+[&#39;120&#39;, &#39;140&#39;, &#39;160&#39;, &#39;180&#39;, &#39;220&#39;, &#39;240&#39;, &#39;280&#39;, &#39;300&#39;, &#39;320&#39;, &#39;340&#39;, &#39;360&#39;, &#39;400&#39;, &#39;420&#39;, &#39;440&#39;, &#39;460&#39;, &#39;480&#39;, &#39;60&#39;]
 Excluded
-[&#39;0&#39;, &#39;120&#39;, &#39;140&#39;, &#39;160&#39;, &#39;20&#39;, &#39;200&#39;, &#39;40&#39;, &#39;60&#39;]
+[&#39;0&#39;, &#39;100&#39;, &#39;20&#39;, &#39;200&#39;, &#39;260&#39;, &#39;380&#39;, &#39;40&#39;, &#39;80&#39;]
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
diff --git a/devel/multiple-comparison/multiple-comparison_examples.ipynb b/devel/multiple-comparison/multiple-comparison_examples.ipynb
index 131bdf4ff4..1e26ca58a7 100644
--- a/devel/multiple-comparison/multiple-comparison_examples.ipynb
+++ b/devel/multiple-comparison/multiple-comparison_examples.ipynb
@@ -19,10 +19,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:39.025597Z",
-     "iopub.status.busy": "2024-11-04T11:35:39.023389Z",
-     "iopub.status.idle": "2024-11-04T11:35:40.771494Z",
-     "shell.execute_reply": "2024-11-04T11:35:40.770582Z"
+     "iopub.execute_input": "2024-11-04T12:31:33.762545Z",
+     "iopub.status.busy": "2024-11-04T12:31:33.762255Z",
+     "iopub.status.idle": "2024-11-04T12:31:35.433447Z",
+     "shell.execute_reply": "2024-11-04T12:31:35.432686Z"
     }
    },
    "outputs": [],
@@ -42,10 +42,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:40.776815Z",
-     "iopub.status.busy": "2024-11-04T11:35:40.775417Z",
-     "iopub.status.idle": "2024-11-04T11:35:40.781491Z",
-     "shell.execute_reply": "2024-11-04T11:35:40.780659Z"
+     "iopub.execute_input": "2024-11-04T12:31:35.437864Z",
+     "iopub.status.busy": "2024-11-04T12:31:35.436608Z",
+     "iopub.status.idle": "2024-11-04T12:31:35.442297Z",
+     "shell.execute_reply": "2024-11-04T12:31:35.441585Z"
     }
    },
    "outputs": [],
@@ -112,10 +112,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:40.786232Z",
-     "iopub.status.busy": "2024-11-04T11:35:40.785082Z",
-     "iopub.status.idle": "2024-11-04T11:35:41.098608Z",
-     "shell.execute_reply": "2024-11-04T11:35:41.097681Z"
+     "iopub.execute_input": "2024-11-04T12:31:35.446714Z",
+     "iopub.status.busy": "2024-11-04T12:31:35.445675Z",
+     "iopub.status.idle": "2024-11-04T12:31:35.787565Z",
+     "shell.execute_reply": "2024-11-04T12:31:35.786518Z"
     }
    },
    "outputs": [],
@@ -142,10 +142,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:41.104094Z",
-     "iopub.status.busy": "2024-11-04T11:35:41.102786Z",
-     "iopub.status.idle": "2024-11-04T11:35:41.471557Z",
-     "shell.execute_reply": "2024-11-04T11:35:41.470595Z"
+     "iopub.execute_input": "2024-11-04T12:31:35.792460Z",
+     "iopub.status.busy": "2024-11-04T12:31:35.791258Z",
+     "iopub.status.idle": "2024-11-04T12:31:36.134872Z",
+     "shell.execute_reply": "2024-11-04T12:31:36.133941Z"
     }
    },
    "outputs": [],
@@ -187,19 +187,19 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:41.475075Z",
-     "iopub.status.busy": "2024-11-04T11:35:41.474540Z",
-     "iopub.status.idle": "2024-11-04T11:35:42.327782Z",
-     "shell.execute_reply": "2024-11-04T11:35:42.326855Z"
+     "iopub.execute_input": "2024-11-04T12:31:36.139579Z",
+     "iopub.status.busy": "2024-11-04T12:31:36.138453Z",
+     "iopub.status.idle": "2024-11-04T12:31:37.000621Z",
+     "shell.execute_reply": "2024-11-04T12:31:36.999867Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "lower         0.084\n",
-       "consistent    0.096\n",
-       "upper         0.096\n",
+       "lower         0.573\n",
+       "consistent    0.716\n",
+       "upper         0.733\n",
        "dtype: float64"
       ]
      },
@@ -228,10 +228,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:42.393614Z",
-     "iopub.status.busy": "2024-11-04T11:35:42.393142Z",
-     "iopub.status.idle": "2024-11-04T11:36:24.284174Z",
-     "shell.execute_reply": "2024-11-04T11:36:24.283441Z"
+     "iopub.execute_input": "2024-11-04T12:31:37.036578Z",
+     "iopub.status.busy": "2024-11-04T12:31:37.036155Z",
+     "iopub.status.idle": "2024-11-04T12:32:19.061156Z",
+     "shell.execute_reply": "2024-11-04T12:32:19.060393Z"
     }
    },
    "outputs": [
@@ -353,16 +353,16 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:36:24.287185Z",
-     "iopub.status.busy": "2024-11-04T11:36:24.286600Z",
-     "iopub.status.idle": "2024-11-04T11:36:24.585339Z",
-     "shell.execute_reply": "2024-11-04T11:36:24.584604Z"
+     "iopub.execute_input": "2024-11-04T12:32:19.063740Z",
+     "iopub.status.busy": "2024-11-04T12:32:19.063497Z",
+     "iopub.status.idle": "2024-11-04T12:32:19.370642Z",
+     "shell.execute_reply": "2024-11-04T12:32:19.369861Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -403,19 +403,19 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:36:24.588341Z",
-     "iopub.status.busy": "2024-11-04T11:36:24.587660Z",
-     "iopub.status.idle": "2024-11-04T11:36:25.241948Z",
-     "shell.execute_reply": "2024-11-04T11:36:25.241236Z"
+     "iopub.execute_input": "2024-11-04T12:32:19.373634Z",
+     "iopub.status.busy": "2024-11-04T12:32:19.373088Z",
+     "iopub.status.idle": "2024-11-04T12:32:20.023449Z",
+     "shell.execute_reply": "2024-11-04T12:32:20.022673Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "lower         0.070\n",
-       "consistent    0.092\n",
-       "upper         0.092\n",
+       "lower         0.003\n",
+       "consistent    0.003\n",
+       "upper         0.003\n",
        "dtype: float64"
       ]
      },
@@ -453,16 +453,16 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:36:25.244256Z",
-     "iopub.status.busy": "2024-11-04T11:36:25.243779Z",
-     "iopub.status.idle": "2024-11-04T11:36:25.407238Z",
-     "shell.execute_reply": "2024-11-04T11:36:25.406348Z"
+     "iopub.execute_input": "2024-11-04T12:32:20.026064Z",
+     "iopub.status.busy": "2024-11-04T12:32:20.025548Z",
+     "iopub.status.idle": "2024-11-04T12:32:20.186172Z",
+     "shell.execute_reply": "2024-11-04T12:32:20.185378Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -496,10 +496,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:36:25.409886Z",
-     "iopub.status.busy": "2024-11-04T11:36:25.409616Z",
-     "iopub.status.idle": "2024-11-04T11:36:25.834494Z",
-     "shell.execute_reply": "2024-11-04T11:36:25.833723Z"
+     "iopub.execute_input": "2024-11-04T12:32:20.188912Z",
+     "iopub.status.busy": "2024-11-04T12:32:20.188333Z",
+     "iopub.status.idle": "2024-11-04T12:32:20.627031Z",
+     "shell.execute_reply": "2024-11-04T12:32:20.626237Z"
     }
    },
    "outputs": [
@@ -508,7 +508,7 @@
      "output_type": "stream",
      "text": [
       "Model indices:\n",
-      "[]\n"
+      "['334', '351', '354', '362', '374', '389', '398', '411', '412', '432', '442', '444', '447', '449', '450', '456', '460', '462', '467', '471', '473', '477', '479', '480', '481', '482', '485', '489', '492', '495', '497']\n"
      ]
     }
    ],
@@ -526,16 +526,16 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:36:25.837019Z",
-     "iopub.status.busy": "2024-11-04T11:36:25.836513Z",
-     "iopub.status.idle": "2024-11-04T11:36:26.020130Z",
-     "shell.execute_reply": "2024-11-04T11:36:26.019428Z"
+     "iopub.execute_input": "2024-11-04T12:32:20.629590Z",
+     "iopub.status.busy": "2024-11-04T12:32:20.629018Z",
+     "iopub.status.idle": "2024-11-04T12:32:20.807012Z",
+     "shell.execute_reply": "2024-11-04T12:32:20.806131Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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uyPgMng8ycLzHjx+L+NmB/Y8U4I0mOTkZN910C2666RbU1h7Ftm1bsHLl3/Huu//AsWN1WLDgt50+c82adYo+32iK54TcsGEDDh48iCuvvLIjAAkAWVlZmDNnDtra2rB06VLVO/TWW28BAK6//vpOf3/33XfR2NiI73//+50mSO3Zsye+//3v4/jx43jvvfdUfy8ZK7CiWGjjHVhRrKyi1qI9IyIiIiIiIgoXWHg1FpkXXnW5XEhP79Lpb4Fh0MHDswM++2y7ofsTmCtx166dmj6nuPjMKuDbtm0Je+3UqVP44ovdSEtLQ9++/TR9T8DIkaPR0tKCt99+CydOnMDIkWMAnIlHFRb2xtatn2Lbti3o0qVLp2HUGRmZOOusQhw6VIWjR2vCPjew/0VFxar2y+PpjssuuxzPPPM8evfug82bN+H06RYA3/7WgWHZslAchNy06cxY8gkTJoS9Fvjbp59+GvaaiK1bt6KyshJDhw7FkCFDFH9vYBuSi+iKYl6f36Q9IiIiIiIiIopP9oVX33nnbygv/zziax9+uBYHDnyFzMwsDBgwEAA6krp27Njeadtt27bg739Xn1AmYurU6ejaNQMvvvgCvvyyMuz1lpYWoQDleed9B4WFvbFhw3p8+unGTq/96U8voaGhAZdeOqVjbkatAtmNr732J7jd7k5zUY4cORpbtmxGefnnOO+87yA5ufOA4yuuuBLt7e1YuPC38Pu/jXns21eBVatWIDMzExdddLHQfrS2tmLnzs/C/t7ScgqnTp1EcnIyXK4zYb4ZM65HUlISnn32/3DkyJGw9zQ1NWHv3j1C36snxcOx9+/fDwDo1y88oty9e3d07doVBw4cULUzgSzI7373u4q+N/A3Nd+rIhvXNgLHZvUxKllRbHTfXHN2isgEstRBokTGekhkPdZDIus5uR6acUwyL7y6ceN6PP30E+jduw/OO+878Hi649SpU6io+AKffbYNbrcb9903D6mpZ4Ko//EfF6FXr7Pw+uuv4KuvKnH22QNRVXUA69d/hAsvvBhr175v2L7m5eXh0Ucfx8MPz8Ntt92EcePGo2/f/mhra8Xhw4exfftWDB06DAsWPB/zc9xuN/7rvx7Ffffdg5///GeYNOlS9OzZC7t27cC2bVtQWNgbc+b8RLf9HjZsOJKTk1FffxzFxYORnZ3d8dqIEaPx97+/0/HfoW666QdYv/4jrF69EgcO7MeYMWNRV1eHsrJ/wuv14oEHHhIeLn369GncddeP0KdPXwweXIIePXri1KmTWL/+I9TV1eHGG2/pOM8DBgzCfffNxzPPPImbbroO48dfgMLC3jh58iT+/e9qbN++FVdccSV+/vP/Evpulyt6XVNSBxUHIZubz0zsmZWVFfH1zMzMiAvOxHPixAmsWrUKXbp0wZVXXqnoewPzQKr53vz8yMfhJFYf4+lDjWLbud3weJx/PijxWF0HiYj1kEgGrIdE1nNiPWxpacGxY24kJbmQnKx4sKewZADjzo68wIiV7rnnZ/jOd4Zj06aN+OyzbairOzPVWffu3TF16lWYOfN7GDLknI7ts7Mz8bvfLcLzz/8a27dvxbZtWzBgwEA8+uhj6NYtH2vXvg+3O/y3dLnC/5aU5I77WuhnXXTRRXjllTfw5z+/gk8/3YhPP92ILl26oHv3Alx55XRcfvlUofM4atRI/OEPf8If/7gYn366Ac3NzfB4uuN737sRt902G7m5eRHfp6aMZGVloKTkXOzc+RlGjx7b6TPGjh3T8d9jxowO+/zk5C544YUX8eqr/w/vvbcGf/nLa0hPT8eIEaPwwx/OwvDhIzpt7/4msJ2U5A77rKysrrj77p9i8+ZN2LFjO44fP4asrGz069cPP/7xT3DZZVM6zXs5Y8Z1GDJkCN5448/Yvn0rPv74X8jMzESPHj1xww03Y+rUK+P+Hj6fC263G3l5GWGro6vh8gfngwqYNWsWPv74Y6xZsyZiVuKFF16IkydPYsuW8LH5sfz1r3/FQw89hGuvvRZPPvlk2OtTpkzB/v378fnnn4elt7a1tWHo0KEYPHgwli9fruh76+qaoOwXsA+X68xFxupj3HywHnOW7Ii73cKZw5gJSY4iSx0kSmSsh0TWYz0ksp6T62FbWytqaw8jP78XUlKsX6WaKJbkZDfa231W74awtrZW1NUdhscTvX4F2hcRijMh42UdNjc3IydH+cpQf/vb3wCEL0gT6Xvz8jpHtONlZ8bi98NxjXAoq49RyYpiTj8XlJisroNExHpIJAPWQyLrObEeOu14iGSkV9uhOA+1f//+ACLPv3j06FGcPHkyYoZkLPv27cO2bdswYMAAjB4dPoY+3vcG/qb0e8kcdl9RjIiIiIiIiIiItFEchBwz5sx4948++ijstcDfAtuICixIEy0LUvR7x44dq+h7yTyyryhGRERERERERETGUTwnZHt7Oy6//HJ8/fXXWLJkCUpKSgCcGSZ9/fXXo7q6Gu+++y569+4NAKipqUFTUxMKCgoiDpdua2vDRRddhKamJqxbtw75+fkRv7ehoQGXXHIJkpOT8c4773QsK3/kyBFcc801AID33nuvY9i2qNpa582JEeByAR5PllTH6PX5pVxRjMgIMtZBokTDekhkPdZDIus5uR4G5qzjnJBkB3adEzJW/Qq0LyIUzwmZnJyMxx57DLNnz8bNN9+MadOmISMjA2vWrEF1dTXmzZvXEYAEgAULFmDp0qV44oknMGPGjLDPKysrw7FjxzB58uSoAUgAyMnJwcMPP4wHHngA1157LaZOnQoAWLlyJerr6/Hss88qDkCS+ZLcLozqk2v1bhARERERERERkYkUByEB4Pzzz8frr7+O5557DitXrkR7ezuKi4tx//33dwQHRYkMxQ64+uqrkZeXh0WLFuHtt98GAAwdOhR33XUXLrjgAuUHQkRERERERERERIZTPBzbaZyYjh7g5JR7IjtgHSSyHushkfVYD4ms5+R6yOHYZCeJPhxb8cI0REREREREREREREowCElERERERERERESGYhCSiIiIiIiIiIiIDMUgJBERERERERERERlK1erYRCQXr8+P7dUNqG1uhSczFcMLc5Dkdlm9W0REREREREREABiEJLK9sopaPFO2DzXNrR1/K8hMxX2lg1Ba5LFwz4iIiIiIiIiIzuBwbCIbK6uoxbzluzsFIAGgprkV85bvRllFrUV7RkRERERERET0LWZCEtmU1+fHM2X7Ym6z4INKTByYz6HZREREREREDnX48L/x3e9OD/t7eno6Cgt7Y+LEUtxww/fRtWtX1d9x/fVXAQDeeuvvYa9t3boZP/3pHNx22+340Y/uVP0d5HwMQhLZ1PbqhrAMyFBfN53G9uoGjOqTa85OERERERERkSUKC3tj8uQrOv59/PhxbNy4Hn/844vYuPETvPDCH5CUlGThHlKiYxCSyKZq4wQglW5HREREREREnbmbquFuORb1dV96N/iyCk3co+gKC/uEZSK2trZizpzb8PnnO7F9+1aMGjXGor0jYhCSyLY8mam6bkdERERERETfcjdVo9trF8HlPR11G39SGo7d/KE0gchQqampGDFiNPbu/QL19fWdXjt+/BheffVlfPzxv1BT8zW6du2K4cNH4kc/uhMDBgwCED7Ue8KE0R3/fdtttwMAXn55ccf/B/4bAP761+Xo1essAEBbWxv+9rc3sWbNKhw8eAAulxtFRcW46aZbMGHCxE779fjjj2LVqhV488138OGHa/GPfyxDdfUhXHrpFPziF4/q9tuQ+RiEJLKp4YU5KMhMjTkku0dWGoYX5pi4V0RERERERM7gbjkWMwAJAC7vabhbjkkbhGxra8O2bVvgcrlQVFTc8ffq6kP4yU/uRE3N1xg79nxceOHFOH78GNatK8OmTRvw61//HueeOxSZmVm47bbb8de/vgEA+O53b+z4jBEjRgEAjhw5jFWrVmD48JEdfwOAzMwsAGeyMe+77yfYtm0LioqKceWVV6O9vR3r13+E+fPvw9y5P8d1130vbN9//ev/w+ef78L48f+BCy64EHl53Qz5jcg8DEIS2VSS24X7Sgdh3vLdUbe5d9JALkpDRERERESUAKqrq/DSS4s6/l1fX49Nmz7B0aNHcdddP0Xfvv06XnvssUdQV1eLZ555HuPGje/4+w9/+CPMnv0D/OpXj+FPf/oLsrKy8KMf3YlVq1YAQNSFZ1atWoERI0ZFfP3llxdj27YtuPXW2fjRj+6Ey3XmHvXkyRP46U/vwm9/+2tMnFgKj6d7p/dVVu7DH//4Gnr27Kn+RyGpuK3eASJSr7TIg6emn4OCkCHXPbLS8NT0c1Ba5LFoz4iIiIiIiMhM1dWHOoZEv/zyYixd+ldUVx/C6NFjMWbM2I7t9u7dg507d+Dyy6d1CkACQN++/XDVVdegsnIfvvxyn+Z98vl8eOedv6GwsHenACQAdO2agVtvnY22tjasW1cW9t4bb7yFAUiHYSYkkc2VFnkwcWA+tlc3oLa5FZ7MVAwvzGEGJBERERERUQIZO3Y8Fix4vuPfDQ312LnzM/zmN8/grrt+hN/8ZiHOPXcoPv98F4Azc0IGZ04GHDy4HwBw4MD+jrkh1Tp48ACamhrh8Xjwxz++GPZ6YJ7KAwf2h71WUnKupu8m+TAISeQASW4XRvXJtXo3iIiIiIiISBI5ObmYMGEi0tLSMXfu3Vi8+AX8+tcvoLGxAQCwfv1HWL/+o6jvb2lp0bwPge/66qsv8dVXXyr6rm7dOAek0zAISURERERERETkUOeeOxQAsGfPmfUEMjIyACDqgjB6ysjIBABcfHEpHnvsV4reGzx0m5yBc0ISERERERERETlUY2MTAMDn8wMAzjnnTFBy166dwp/hdrvh8/kivpaUlPTN54e/3q9ff2RkZGDPnnK0t7cr2m9yHgYhiYiIiIiIiIhC+NK7wZ+UFnMbf1IafOlyDxt+883XAADDh48AcCYIec45Q/Hee6vx/vtrwrb3+XzYtm1Lp79lZ+egvr4ep0+fDts+KysbAFBT83XYa8nJybjmmutx5Mhh/Pa3v44YiPzyy304fvyY8gMj2+FwbCIiIiIiIiKiEL6sQhy7+UO4W6IHyHzp3eDLKjRxr6Krrq7qtNBMU1Mjduz4DHv37kFWVjbmzPlJx2uPPvo4fvrTOfjlL/8LS5a8geLiIUhLS8PXXx/B55/vRH39cZSVre/YfuTI0dizZzfuv/+n+M53RiA5ORnDh4/E8OEj0a9ff3g83fH++2uQkpKCgoIeAIDrr78BmZmZ+NGP7sTevXvw1lt/wSeffITvfGcE8vK6oba2BpWVldi3by8WLnwZeXlyB3NJOwYhiYiIiIiIiIgi8GUVShNkjKe6+hBefnlxx79TU1PRvXsBrrnmenz/+7eiZ8+eHa+ddVYh/vjH1/Dmm6/hX/9ai5Url8PtTkJ+vgff+c4IXHzxJZ0++9ZbZ6OpqQnr1/8LO3Zsh9frxW233Y7hw0ciKSkJjz/+f/j975/De++twcmTJwAAU6ZMRWZmJlJTU/H0089hxYplePfdf2DdujK0tbUhL68b+vcfgGuumYGBA7Wtwk324PL7/X6rd8JKtbVNcOov4HIBHk+Wo49RFl6fH9urG1Db3ApPZiqGF+Ygyc1JdBMd6yCR9VgPiazHekhkPSfXw7a2VtTVHUZ+fi+kpKRavTtEMSUnu9HeHnluTRmJ1K9A+yKCmZBEGpVV1OKZsn2oaW7t+FtBZiruKx2E0iKPhXtGRERERERERCQHLkxDpEFZRS3mLd/dKQAJADXNrZi3fDfKKmot2jMiIiIiIiIiInkwCEmkktfnxzNl+2Jus+CDSnh96sY7eH1+bKmqx+ryGmypqlf9OUREREREREREVuNwbCKVtlc3hGVAhvq66TS2VzdgVJ9cRZ/NId5ERERERERE5CTMhCRSqTZOAFLpdgEc4k1ERERERERETsMgJJFKnkyxlddEtwOMH+JNRERERERERGQFBiGJVBpemIOCOAHGHllpGF6YI/yZSoZ4ExERERERERHZBYOQRColuV24r3RQzG3unTQQSW6X8GcaNcSbiIiIiIjIyfx+jhYj0pve9YpBSCINSos8eGr6OWEZkT2y0vDU9HMULyJjxBBvIiIiIiIip0pKSgIAtLaetnhPiJyntbUFgKujnmnF1bGJNCot8mDiwHxsr25AbXMrPJmpGF6YoygDMiAwxDvWkGylQ7yJiIiIiIicyu1OQpcumWhuPg4ASE1Ng8ul/F6MyAw+nwter9xZu36/Hz6fFy0tJ9HScgJdumTC7WYQkkgaSW4XRvXJ1eVz7isdhHnLd0fdRukQbyIiIiIiIifLzu4GAB2BSCJZud1u+Hw+q3dDiNudhOzsfHTpkqHbZzIISSSZwBDvZ8r2dcqI7JGVhnsnDVQ8xJuIiIiIiMjJXC4XcnLykZWVB6+33erdIYrI5QLy8jJw/PgJyD6FqdudBLfbrXtWMYOQRBLSc4g3ERERERFRInC73XC7OX8+ycnlAtLT05GS0iZ9ENIoDEISSUqvId5ERERERERERFbj6thERERERERERERkKAYhiYiIiIiIiIiIyFAMQhIREREREREREZGhGIQkIiIiIiIiIiIiQ3FhGiIiHXl9/k6rml/WLdPqXSIiIiIiIiKyHIOQREQ6KauoxTNl+1DT3Nrxt16r92LuxAGYVOSxcM+IiIiIiIiIrMXh2EQaeH1+bKmqx+ryGmypqofX57d6l8giZRW1mLd8d6cAJAAcaWjBA8t3o6yi1qI9IyIiIiIiIrIeMyGJVIqU9VaQmYr7SgehlFlvCcXr8+OZsn0RXwuEpRd8UImJA/OR5HaZt2NEREREREREkmAmJJEK0bLeappbMY9Zbwlne3VDWFkI9XXTaWyvbjBpj4iIiIiIiIjkwiAkkUKxst4CFnxQyaHZCaQ2TgBS6XZERERERERETsMgJJFCzHqjUJ7MVF23IyIiIiIiInIaBiGJFGLWG4UaXpiDgjgBxh5ZaRhemGPSHhERERERERHJhUFIIoWY9Uahktwu3Fc6KOJrgWVo7p00kIvSEBERERERUcJiEJJIIWa9USSlRR48Nf2csLLRMycdv5p+DldMJyIiIiIiooSWbPUOENlNIOtt3vLdUbdh1ltiKi3yYOLAfGyvbkBtcys8mam4bHhvHD/WDD/XKSIiIiIiIqIExiAkkQqBrLdnyvZ1WqSmR1Ya7p00kFlvCSzJ7cKoPrkAAJcLDEYTERERERERgUFIItUiZb0NL8zRPejk9fkN/w4iIiIiIiIiIiMxCEmkQXDWWzxqgollFbVh2ZYFmam4r3QQsy2JiIiIiIiIyDYYhCQygZpgYllFbcR5J2uaWzFv+W48xcVOiIiIiIiIiMgmuDo2kcECwcTgACTwbTCxrKI27D1enx/PlO2L+bkLPqiE18fVToiIiIiIiIhIfgxCEhlIbTBxe3VDWNAy1NdNp7G9ukHzPhIRERERERERGY1BSCIDqQ0m1sZ5j9LtiIiIiIiIiIisxCAkkYHUBhM9malC7xPdjoiIiIiIiIjISqoWptmxYweef/55bNu2De3t7SguLsatt96KqVOnKvqcuro6LFq0CGvXrsXhw4fRtWtX9O/fH1dffTVuuummTtsOHjw46udce+21ePLJJ9UcCpGh1AYThxfmoCAzNWYWZY+sNAwvzNG0fxSbmhXNiYiIiIiIiCic4iDkhg0bMHv2bKSmpmLatGnIyMjAmjVrMHfuXBw5cgSzZs0S+pzy8nLMmjULjY2NmDhxIqZMmYKTJ0+isrISH3zwQVgQEgAKCwtx7bXXhv29pKRE6WEQmUJtMDHJ7cJ9pYMiro4dcO+kgQyIGUjNiuZEREREREREFJnL7/cLL6/b3t6OK664AkeOHMGSJUs6gn9NTU24/vrrUV1djdWrV6OwsDDm5zQ3N+Oqq65CS0sLXn75ZQwZMiTse5KTO8dHBw8ejLFjx+LVV18V3V0htbVNEP8F7MXlAjyeLEcfox0EVseO5qnp50QNakULhF0zrBf65nZhdp5BtJyzYKyDRNZjPSSyHushkfVYD4ms59R6GDguEYrmhNywYQMOHjyIK6+8slP2YVZWFubMmYO2tjYsXbo07ue8/vrr+Pe//4377rsvLAAJICwASfbn9fmxpaoeq8trsKWqPmw1aCcrLfLgqennoCBkyHWPrLS4wazSIg+W3z4OC2cOw2NTh+DOC/oBAF5cfwAPrdyDOUt2YPrijSirqDX0GBKJ2hXNiYiIiIiIiCg6RdG+TZs2AQAmTJgQ9lrgb59++mncz1m5ciVcLhemTJmCL7/8Eh9//DFaWlowYMAAXHjhhUhNjTyPXmNjI958800cP34cOTk5GDlyZMy5IkkOHNZ6Jpg4cWC+qvkFk9wujOqTi7KKWixafyDs9ZrmVsxbvls4O49iU7Ki+ag+uebsFBEREREREZHNKQpC7t+/HwDQr1+/sNe6d++Orl274sCB8CBJsNbWVuzduxfdunXDq6++iueffx4+n6/j9T59+uB3v/tdxODinj178Mgjj3T624UXXoinnnoK+fn5Sg6lg8vBo1gDx2blMZbtjTysNRA4+9X0c1BanBiBs+QkF0b3zVX1XtHsvIsH5XNotka1JwRXND/RGrduyVAHiRId6yGR9VgPiazHekhkPafWQyXHoygI2dzcDODM8OtIMjMz0dTUFPMzGhoa4PV6UV9fjxdeeAE///nPcfXVV6O9vR1/+ctf8Pvf/x533XUXVq1ahbS0tI73zZo1C5MnT0b//v2RkpKCiooKvPDCC/jwww9x55134s0330RSUpKSwwEA5OeLjVu3M6uO0evz49l1m6K+7gLw7Lovcd35/Rk4i+OTyjqh7Lwvm9owfqC6gDydMfAssSDkwLNyhee9SIR2hkh2rIdE1mM9JLIe6yGR9RK5Hpo++WIg69Hr9eLmm2/utJr2z372M3z11VdYtWoV3n33XVx99dUdr82bN6/T54wYMQKLFi3CD3/4Q2zatAnvv/8+Jk+erHh/6uqcNSFoMJfrTOG26hg3H6zH4YaWqK/7ARxuaME/tx9SnSGYKCr/XS+8XVFO5OkMSMyArBShFc0HZKWgtjb2Qxer6yARsR4SyYD1kMh6rIdE1nNqPQwclwhFQcjMzEwAiJrt2NzcjJycnJifEZxFWVpaGvZ6aWkpVq1ahV27dnUKQkbidrvx3e9+F5s2bcLWrVtVBSH9fjjq5Edi1THWxsncC97O6edAK0+GWGDRk5HK31Ijt8uF+0oHxVwd+95JA+F2uYR/60RoZ4hkx3pIZD3WQyLrsR4SWS+R66Gi1bH79+8PABHnfTx69ChOnjwZcb7IYF27dkWPHj0AANnZ2WGvB/52+vRpoX3Ky8sDAJw8eVJoezKPJ1MwcCa4XSIbXpgTtrp2qB5ZaRheGPshAInRsqI5EREREREREYVTFIQcM2YMAOCjjz4Key3wt8A2sZx//vkAgH37whfaCPytsLBQaJ8+++wzAEDv3r2FtifzMHCmnyT3mey8WO6dNJBza+qotMiD5bePw8KZw/DY1CFYOHMYls0ea1oA0uvzY0tVPVaX12BLVT28vgR9VEZERERERESOoCgIOX78ePTp0wcrVqxAeXl5x9+bmpqwcOFCpKSk4Jprrun4e01NDSorK8OGb99www0AgMWLF6OxsbHj70ePHsUrr7wCt9vdaWj1F198gba2trD92bp1K/7whz8gJSUFl19+uZJDIRMwcKYvZueZL8ntwqg+uZhSUoBRfXJNK6tlFbWYvngj5izZgYdW7sGcJTswffFGlFXUmvL9RERERERERHpz+f3KRqJv2LABs2fPRmpqKqZNm4aMjAysWbMG1dXVmDdvXqeFZubPn4+lS5fiiSeewIwZMzp9zpNPPomXX34ZvXr1wqRJk9De3o73338fdXV1uPfee3HnnXd2+py1a9di1KhR6NWrF5KTk1FRUYGPP/4YLpcLjzzyCG688UZVP0BtrbMmBA3mcgEeT5blx1hWUYtnyvZ1WuijR1Ya7p00kIEzFbw+P7ZXN6C2uRWezFQML8xhIFdSaupgWUVtzPkoGXAmUkaWayFRImM9JLIe6yGR9ZxaDwPHJULx6tjnn38+Xn/9dTz33HNYuXIl2tvbUVxcjPvvvx9Tp04V/pz58+ejuLgYr732GpYuXQqXy4WSkhL893//Ny677LJO215yySVobGzEnj17sH79erS1tcHj8WDatGn44Q9/iGHDhik9DDJRaZEHEwfmM3Cmk0B2HjmP1+fHM2Xh01QEW/BBJSYOzGf9ISIiIiIiIltRnAnpNE6LQAdzapSdyC6U1sEtVfWYs2RH3O0WzhzGQDSRIF4LiazHekhkPdZDIus5tR4qyYRUNCckEREZpzZoygI9tiMiIiIiIiKSBYOQRESS8MRZTV7pdkRERERERESyYBCSiEgSwwtzwlY/D9UjKw3DC3NM2iMiIiIiIiIifTAISUQkiSS3C/eVDoq5zb2TBnJRGiIiIiIiIrIdBiGJyHBenx9bquqxurwGW6rq4fU5aBZenZUWefDU9HPCMiJ7ZKXhqennoLTIY9GeEREREREREamXbPUOEJE9eX1+bK9uQG1zKzyZqRhemBMxQ6+sohbPlO1DTdBiKgWZqbivdBADalGUFnkwcWC+0O9LREREREREZAcMQpIqogEocibRwGJZRS3mLd8d9v6a5lbMW76bmX0xJLldGNUn1+rdIFKE1wYiIiIiIoqGQUhSjJltiU00sOj1+fFM2b6Yn7Xgg0pMHJjPIAWRA/DaQEREREREsXBOSFIkEIAKvskEvg1AlVXUWrRnZAbRwGIgGyq0nIT6uuk0tlc36LmLRGQBXhuIiIiIiCgeBiFJmJIAFDmTksBibZztAkS3IyI58dpAREREREQiGIQkYcxsIyWBRU/I6s7RiG5HzsXV0+2N1wYiIiIiIhLBOSFJmJMy27h4gjpKAovDC3NQkJkaMzjRIysNwwtz9No9siHOI2h/Tro2EBERERGRcRiEJGFOyWxj0EM9JYHFJLcL95UOiriITcC9kwYy+JvAuHq6Mzjl2kBERERERMbicGwSFghAxSJ7ZpvTFk8wexhrILAYS3BgsbTIg6emnxNWbnpkpTHAlOA4j6BzOOHaQERERERExmMmJAmze2abaNBj4sB8aY8hmFUZnYHAYuh398hKw72TBoZ9d2mRBxMH5nP4O3WiZB7BUX1yzdkpUsXu1wYiIiIiIjIHg5CkiNIAlEycFPSwehir0sBiktsl/W9K5rLrPIKcTzYyO18biIiIiIjIHAxCkmJ2zWyza9AjlCwZnQwskhZ2nEeQ88nGZtdrAxERERERmYNBSFLFjgEoOwY9InFSRiclLrutnm519rEMRLJA7XhtICIiIiIiczAISQnDbkGPaJyS0UmJzU7zCMqSfWwlZoESEREREZFWXB2bpGPUis9KV3aWlVMyOonssnq6kuxjJwpkgYb+BoEs0LKKWov2jIiIiIiI7ISZkCQVo7NtnLB4glMyOokAe8wjmMjZx8wCJSIiIiIivTAISdIwa841OwQ9YrHTMFYiEbLPI5jI2cecg5aIiIiIiPTC4dgkBdFsGz2HZo/qk4spJQUY1SfXdgE7uwxjJXKCQPZxLE7NPk7kLFAiIiIiItIXMyFJCsy2Uc7uGZ1EdpHI2ceJnAVKRERERET6YhCSpMBsG3VkH8ZK5BROmE9WDc5BS0REREREemEQkqTAbBsikl0iZh8nchYoERERERHpi0FIkoIe2TZenz+hggNa8fciUi4Rs48TNQuUiIiIiIj0xSAkSUFrtk1ZRW3YDXJBZiruKx3EG+QI+HsRkRKJmAVKRERERET64urYJA21Kz6XVdRi3vLdYVmUNc2tmLd8N8oqag3bZzvi70VEagSyQKeUFGBUn1wGIImIiIiISBFmQpJUlGbbeH1+PFO2L+ZnLvigEhMH5vOGGfy9iIiIiIiIiMgaDEKSdJTMuba9uiHmPJIA8HXTaWyvbki4edwi4e9FRERERERERFZgEJIsodeiKLVxAmpKt3O6RPm9RMoXF+YhIiIiIiIiMg+DkGQ6PRdF8YTMH6l1O6dLhN9LpHxxYR77YdCYiIiIiIjI3hiEJFMFFkUJFVgUJdYCNJEML8xBQWZqzCHGPbLSMLwwR9X+Oo3Tfy+R8gVA1zJIxmPQmIiIiIiIyP64OjaZRnRRFK/PL/yZSW4X7isdFHObeycNZMbUN5z8e4mUr2fK9uleBslYXM2diIiIiIjIGRiEJNMoWRRFidIiD56afg4KQoYQ98hKY1ZbBE79vUTKV01zqyFlkIxhxIMLIiIiIiIisgaHY5NpjFwUpbTIg4kD8zlnnCAn/l56LqZj94V5nIKruRMRERERETkHg5BkGqMXRUlyuxiIEBC6wMelg7vbOvgYoOdiOnZemMdJEmU1dyIiIiIiokTAICSZxumLothBtAU+5k4aiLwuKbbOihQpX4Eh6CyDncm68nQirOZud7KWHSIiIiIikg+DkGSawKIokVYmDrDroih2EGvl6Af/Xt7pb3ZceVikfAUW5WEZ/JbMK0/zwYXcZC47REREREQkHy5MQ6Zy6qIoshNZ4COYXVceFilfLIPfkn3laSev5m53spcdIiIiIiKSj8vv9yf0sqK1tU1w6i/gcgEeT5aUx8ghfObaUlWPOUt2KH5fj6w0LJs91nbnRqR8mVEGZa+D0xdvjJtlKMP5j5Rx1yMrDfdOGphQQWNZ2KnsAHLXQ6JEwXpIZD3WQyLrObUeBo5LBIdjkyW4iIxyWoJmahfusOvKwyLlK9HLoJ1Wnnbiau52ZqeyQ0RERERE8mAQMkEw89DetM69pmXhDq487ExGrDxtZDuT6EFjmXDVciIiIiIiUoNByARQtrcWT3PxANuKtaDMvOW7heYxFFngIxquPOxMeq88zUVKrGHFAyauWk5ERERERGpwYRqHe3fXYTzAxQNsS2RBmQUfVMLriz2hhMgCH5Fw5WHnCgSmYxE9/1ykxBplFbWYvngj5izZgYdW7sGcJTswffFGw39vPcsOERERERElDgYhHczr8+O//x6eQRdMJIBF1lEy91o80VaFjoUrDzuXXitP6xUoJ2WsDPxy1XIiIiIiIlKDQUgH23aoAYcbWmJuIxrAImvoPfdaaZEHy28fh4Uzh+GxqUOwcOYwPHFlSVhgskdWmtAwb7K3aIFpJedfz0A5iZEh8KtH2SEiIiIiosTCOSEdrPaEfgEsM+Yd4+I54YyYey3SAh+Tijz87b+RaOVQ68rTXKTEfFavTh2oI23tPjx6+WD4ARw/2ZYQ9YWIiIiIiNRjENLBPBn6BLDMWHCCi1pEJrKgjB5zr3Hl4TPilUOnBii1nH8uUmI+KwO/seoI2xAiIiIiIoqFw7EdbETvHPTKSY+5TbwAlhnzjnFRi+g495p54pXD59Z9ackiILLjIiXmsyrwy7aaiIiIiIi0YBDSwZLcLvzyqnNibhMrgGXGvGMyzG0mO869ZjyRcvjq5kMMvkTAQLn5rAj8sq0mIiIiIiKtGIR0uMuH9sKvVAawzFhwgotaiIm0oMyy2WMZgNSJSDmMJdGDL04JlHt9fmypqsfq8hpsqaqX9pxaEfhlW01ERERERFpxTsgEUFrswUUqFp4wY94xLmohzsx5G50692E0WsuXkYuA2IXWBW6sZrd5aQOB39B97pGVhnsnDdR9nxO1rU60tpCIiIiIyEgMQiYINQEsM+Yd46IW8rFbMEYPepQvpwVf1LDrAkeBuQ5DBYbby5rNaWbgNxHb6kRsC4mIiIiIjMTh2BSVGfOOcVELuSTqwhMi5TAeJwVfEond5zoMBH6nlBRgVJ9cw7L0Eq2tTtS2kIiIiIjISAxCJhglc56ZMe8YF7WQh92DMVqIlMNYnBR8cQIl7RznOhSTSG11IreFRERERERG4nDsBKJmaJkZ846ZPbcZRaYkGGPHIbfxxCqHkwd3x6ubD0V9r1OCL06gtJ1L1LkO1UiUtjrR20IiIiIiIqMwCJkgtMx5Zsa8Y3Zf1MIJGIyJXQ6HnpXt+OCL3alp5xJxrkMtEqGtZltIRERERGQMBiETgOjQsokD86PeSJqx4IRdF7VwCgZjzohWDhMh+GJnatu5wFyHsTLf7Drc3qiVnZ3eVrMtJCIiIiIyhuog5I4dO/D8889j27ZtaG9vR3FxMW699VZMnTpV0efU1dVh0aJFWLt2LQ4fPoyuXbuif//+uPrqq3HTTTeFbf+vf/0LixYtwueffw6Xy4Vzzz0XP/7xjzF+/Hi1h+J42w5xaBnF5+RgjF6cHnyxM7VDaANzHUbKoAwwYri9UQHCAK7srB7bQiIiIiIiY6hamGbDhg246aabsGXLFlxxxRW44YYbUFtbi7lz5+KPf/yj8OeUl5fjyiuvxGuvvYZBgwbh1ltvxZVXXokuXbrggw8+CNt+2bJlmD17NiorKzFjxgxce+212LdvH2677Ta8++67ag4lIdSe4NAyii+RFp4g59EyhDYw12Ho6s89stJiTlWhVllFLaYv3og5S3bgoZV7MGfJDkxfvFG3FZf1WtlZyQI/TsK2kIiIiIjIGC6/36/orqK9vR1XXHEFjhw5giVLlqCkpAQA0NTUhOuvvx7V1dVYvXo1CgsLY35Oc3MzrrrqKrS0tODll1/GkCFDwr4nOfnbRM2GhgZceumlSEpKwjvvvIOePXsCAI4cOYJrrrkGAPDee+8hMzNTyeGgtrYJyn4B+3C5AI8nC+9urcKcJTvibr9w5jBmeVHEDCrOfahOoA46uZ2RxZaqes3tnNHZiUD0eSsDtAY9vT4/pi/eGDeLb9nssTGPzUmZlGrrIdtCIv3wekhkPdZDIus5tR4GjkuE4uHYGzZswMGDBzFjxoyOACQAZGVlYc6cOZg/fz6WLl2Ke+65J+bnvP766/j3v/+Nxx9/PCwACaBTABIA3n33XTQ2NuInP/lJRwASAHr27Invf//7eP755/Hee+91BCTpWyN6c2iZrMwIeiglw9yHMv4uJDc9htAaPdxe7byVSuqD2mHpwd9RVX8Ki9YfCHufyEJmTiJDW0hERERE5CSKg5CbNm0CAEyYMCHstcDfPv3007ifs3LlSrhcLkyZMgVffvklPv74Y7S0tGDAgAG48MILkZraeVhcvO99/vnnsWnTJgYhI7BqzjOKTeZMIyvnPpT5dyF52aGdUxMgVFof1AxLj/QdscRbyMxJOA8sEREREZF+FAch9+/fDwDo169f2Gvdu3dH165dceBAeAZFsNbWVuzduxfdunXDq6++iueffx4+n6/j9T59+uB3v/sdBg8eLPS9gb/F+95IXA6+hwocm8sFXFLswa+mn4OnIwwtu2/SQJQWM7hjprK9kYdkBjKNfjX9nIQ8J077XYLrIBlP9nZOeH7eE61wudTVByUrO8f6jlgCgdLRfXMVvc8qrIdE1mM9JLIe6yGR9ZxaD5Ucj+IgZHNzM4Azw68jyczMRFNTU8zPaGhogNfrRX19PV544QX8/Oc/x9VXX4329nb85S9/we9//3vcddddWLVqFdLS0uJ+b2AeyHjfG0l+vti4dTsLHONMTxauO78/Nn11DDVNLSjISsfYs7slRDaLTLw+P55dtynq6y4Az677Eted3z+hzo2Tf5dEaGdkIXM7N/AssSDkwLNykdctU1V9uKxbJnqt3osjDS2INM2MC0DPnHRcNrw3AMT8jlhOu93C877IgvWQyHqsh0TWYz0ksl4i10PFQUg9BLIevV4vbr75ZsyaNavjtZ/97Gf46quvsGrVKrz77ru4+uqrDd2XujpnTQgazOU6U7hDj7EoJxVFOWeyZY4fa7Zo7xKH1+fHtkMNqD3RCk9GKnx+Pw43tETd3g/gcEML/rn9kG0yjfSw+WC9436XaHWQjCdjOzcgK0Vo3soBWSn45/ZDquvD3IkD8ECU7Eb/N68fP9Yct87FkubzobZW+YM/K7AeElmP9ZCiCe0nj+jNuXeNwnpIZD2n1sPAcYlQHISMl3XY3NyMnJzYC5wEZzOWlpaGvV5aWopVq1Zh165dHUHI4O/Ny8sL+87QzxXl98NRJz+SRDhGWUWaay07LUnovbXNrQlx3gILYry/t1Zoezv+LqyD6jlpkSK3S2zeSrfLpWhux9CyNanIg6emnxN1ZedJRR74/eLzR4YKLPBjtzLNekhkPdZDCsZ5wK3BekhkvUSuh4qDkP379wdwZv7FoUOHdnrt6NGjOHnyJIYNGxbzM7p27YoePXrg66+/RnZ2dtjrgb+dPn260/fu2rULBw4cCAtCBuaCjDRfJJFVyioiz7XWeNor9H7Rud3sTOmCGEBi/C6hnBSIU8KJNyelcQKEgeNSMrdjtO+Jt7Kz2rpk9QI/RERkf9H6yYF5j5+afo5tr/VERBSd4iDkmDFjsGjRInz00UeYNm1ap9c++uijjm3iOf/887Fs2TLs27cP5557bqfX9u3bBwAoLCzs9L0rVqzARx99hOHDh0f83rFjxyo9HCJDeH1+PFO2T/X7A5lGThat8xlLIvwuoawKxFkd+HTyzYlIgHB4YY7Q0O1Y9SHeys4i3xH6fcGBUkocVrcHROQsIv3kBR9UYuLAfLY1REQOozgIOX78ePTp0wcrVqzAD37wA5SUlAA4M0x64cKFSElJwTXXXNOxfU1NDZqamlBQUNBpuPQNN9yAZcuWYfHixZg0aVJH9uPRo0fxyiuvwO12Y/LkyR3bX3HFFXj66afx5z//Gddffz169uwJADhy5Aj+/Oc/Iy8vD5deeqmqH4FIb9urGxRl94VyeqaR2iCtk3+XSDf56yrrLAnEWZ2BmAg3J/EChElusaHbgeNXEyQS+Y47LuiHvrldGHhKYFa3B0TkPCL95K+bTmN7dUPMayUREdmP4iBkcnIyHnvsMcyePRs333wzpk2bhoyMDKxZswbV1dWYN28eevfu3bH9ggULsHTpUjzxxBOYMWNGx99HjhyJ2267DS+//DKmT5+OSZMmob29He+//z7q6upw77334uyzz+7YPicnBw8//DAeeOABXHvttZg6dSoAYOXKlaivr8ezzz7bMW8k2YsTMyxE51rLTk9GY0t7x78TJdNIaZDW6b9LpJv87hkpaPXGnijEiECcDBmIvDk5Q3TotpYgkeh3UGKSoT0gIudRMu8xERE5i6rVsc8//3y8/vrreO6557By5Uq0t7ejuLgY999/f0dwUMT8+fNRXFyM1157DUuXLoXL5UJJSQn++7//G5dddlnY9ldffTXy8vKwaNEivP322wCAoUOH4q677sIFF1yg5lDIYk7NsBCda+3JK0vgdrscFYAVIdqp/O7ws3BJscfRv0u0m/yjJ9rivlfvQJwsGYiJenMS6YFMvKHbegSJRIaH21XgNz19qBFpPp9jjssMsrQHROQ8Wuc9JiIi+1IVhASAYcOG4Q9/+EPc7Z588kk8+eSTUV+fMWNGpwzJeC666CJcdNFFwtuTvJycYSE6n9vIPrkJefMm2qm8pNjjqEy30CDTeb2yNc0dCugbiJMlAzERb07iPZCJ9HvrGSSKNzzcjpz6kMsssrQHROQ8esx7TERE9qQ6CEmkhdMzLJTO56Y32Ye4J2LnM1JAJDc9GfVBw/HV0DMQJ0sGYqKVD7UPZESDRFur6hMu49rJD7nMIkt7QETOY3U/mYiIrMMgJFkiETIsrJprzQ7ZP4nW+SzbGzkgojUAqXcgTjSg2a1rCrZU1RsW1EpyuzD34oF4cEV51G2cUj60PJARDf7MX1Heae5Z2doDvcn4kEv2B0ORJGJGMhGZh3MSExElJgYhyRKJkmFh9lxremf/BN84d+uaAj+A4yfbdDmOROl8en1+PK1xyHU0egfiRDIQc9KT8ei7Xxga5C6rqMWzaysjvua08qHlgYxo8KcxJNjt9GxA2R5y2eHBUCSJlpFMROZz8pzEREQUGYOQZIlEyrAwa641vbN/It04B9PjJjoROp+bvjqmaCXwUDnpyUhLdpsSqBXJUG2IkL2pZ1ArWiA94D8vHiB14EYpLQ9kRIJEsdh5yotYZHrIZedh4YmWsU5E1nDinMRERBQdg5BkCWZY6E/P7J94gSBAv5top3Q+Iw23TE5yoaapRdPn/tfkYlMDtdEyVAsyU3G63RcxCBmgNaglEkj/9dovMWmQxzGBDy0PZESCRLHINOWFnsOVjX7IJbqvMgwL1/q7JkrGuhHsOASfiIiIyGgMQpIlmGGhP72yf0RunIM5NZtKiWjDLe8vHYQ+PbJVfWboTb6ZgaJIGapenx93v7Uz5vu0BrVkG0ZrBq0PZKIFibLTk8OGYUciw5QXeg9XNvIhl5J9tbo86/W7JkLGut7sOgSfiIiIyGhuq3eAElfg5rkgJBulR1aa1EPUZKVX9o/IjXOwwE202bw+P7ZU1WN1eQ22VNXD6/Obvg/At1mjob9ZTXMrHli+G8dPnA4r4yKsHnYcyFCdUlKAUX1ycfxkm9D7tAS1ZBpGa5bAA5lY4j2QKS3yYPnt47Bw5jA8NnUIFs4chieuLBH6fqunvIhVf+Yt342yilrFn6nHb6rHvlpZnvX+XUPbAwYgozOiTBMRERE5BTMhyVJ2yLBQO6TK7KFYemX/qLkhNjsoJEuWiUjW6P/3j3LcO2kg5v89+krPkcg27NiMeVwTaa7YYHoMeQ2d1sDr80s/5YWRw5X1HkasZl+tKs8yDANPVPztiRIXp2AgIhLDICRZTuY5AdUGu6wIkuk1xF3NDbFeN9EiHTiZFnoQyRo93NCC3C4pEQMiscg27NiMeVwTea5YvR/I2GHKC6OHKwf/pqfdbqT5fKp/UzX7alV5tnoYeCLjb0+UmGR5OE5EZAccjk0UhdohVVYOxdJjiHvgxlmUXjfRZRW1mL54I+Ys2YGHVu7BnCU7MH3xxk6/l2iWiVlDs0UzQDcdPI6JA/M7hsx+d/hZun6+GYwa4mr2d8hM7yGvsk95YcZw5SS3C6P75uLq4YUY3Vf9b6pmX60qz4k4rYEsEum3l2VKFCKrcQoGIiJlmAlJCcXoVU1lGIqlNaNK6Yq7etxEi2Y3as0y0XuojGgG6B83VGHFrq87PRH/6/Z/6/b5ZjFjpVyuxqsvmae8sNPwe7X7akV5ttPv6jSJ8tsz64voDBn6/UREdsMgJCUMM1Y1lWUoltYh7tFunIOJ3ESLBP2UdOC0ZJkYcdMkMtwyIDigOnFgvm2HHZsR1JI5cGZHsk55Yafh91r21ezybKff1WkS4beXaUoUIqvJ0u8nIrITBiEpISjtNKsNdjlpKFbojXO3rinwAzh+sk3oJlo06KekAyeaPVJ3shVen79j/4y6aVKaNQp8G1BVM1+fLJOemxHUkjVwRvqxw7yVAVr31czybKff1Wmc/tsz64uoMyf1+4mIzMI5IUk3ss4PpGYeQbVDqpw2FCt4jrox/fIwtl+e0Hx1SubHUdKBE52v8tm1X3bMJ2n0PJLR5t2LJhBQVTpfn8icmUR2I/u8lcG4ryTCyb+9koeGRInAaf1+IiIzMBOSdCHz/EBmrmqaCEOx4lGaKaGkA6ck8zAQ8Lzjgn6GD5UJZI0u/mQ/XtpQFXf7QOBVdJgmh7/pT5asUrLX8HvuK4lw6m/PrC+iztjvJyJSjkFI0ky2AElocKGm6bTQ+yKtaqp0SJXTh2KJUBr0VdqBE5mvMthftlYL7bfWm6Yktwtj+uYJBSGDA6/xhmly+Jv+zHpoYmSg02lBVDsNv+e+kggn/vbM+iLqjP1+IiLlGIQkTWQLkEQKLuSmixXzupOtWF1e03FDr3ZV00Rf3VdppoSaDlwgy+TNbdV4du2XMb+nsaVdaH/0uGky4ok4Jz3Xl1kPTYwMdMqceU5EzsWsL6Jwid7vJyJSikFI0kSmAEm04EK9QBDK7UKnYFbwDb2aIVVOHYolQk2mhJoOXJLbhfyuYt+VnZ4cMxip102TEU/EOfxNPyIPTZ78ZwUmnN0Nqcnqp0w2MtApW+Y5ESUOZn1F57TsdFImkfv9RERKMQhJmlgdIAl0+mqaTmNBnIy4WELXJAm9oVcTQHXiUCwRopkS5/XKxpaq+o7O2sSB+Yo7cKIBzxtHFmLR+gNRX9fzpknvJ+Ic/qYfkYcmx0+1YdqLG/HgZUWqgnlGZofLlnkus9CAwGXdMq3eJSJHYNZXOGanE5C4/X4iIqUYhCRNrAyQROr0xZPbJQX1p9o6/u12hQcgg/GGXjmRTInJg7vj2pc2ae6wiwY8bxvXFwM8GYbcNEXKfgh+In7a7Uaaz6f6iTiHv+lH9GFI/ak21VmFRmaHy5R5LrNI14Zeq/di7sQBmMSAQMJgZppxmPX1LWanExERKcMgJGliVYDkvS+O4sEV5Yrfd+/FA1CQlYba5lbUnWyNO59g8A09b2jExcqUmDy4O17dfCjsPWo67EqGhhlx0xQv+2F031x4PFmorW2CP0awOxYOf9OP0ochsR5CRGsPjMwOtzrz3A6iBQSONLTgAQYEEoZVmWmJtBgVs76YnU5ERKQGg5CkiRUBkvf2HsUv/qE8AAkABVlpHZ3m1eU1Qu+pbW7lUBsVIgX9zuuVjWtf2hTzfUo77EqGhul50ySS/XBJsT5lg8Pf9CHy0CRYtKzCWO2BkdnhHJofW6yAQOAZgMwBAdmCTHZlVWaakxejYtmMjNnpREREyjEISZoZFSCJ1OldV1mHB/+uLgAZmpF5sP6U0Puq6k9FnE+QQ23iCw36bamq173D7vX5kZOejHsuPBvHT7Uhr0sKCr4510bdJIlmP1w8KF+37+TwN+1EHpqECs0qjBfgeOKqEsOywzk0PzY7BwSsDjI5hVWZaU5ejIplM7pEyE5nAJqIiPTGICRpZkQQKFKnt3tGClq9Kse0onNGZllFLV6MsVBJQEFmKpbuOBxzG5kza6wSrdO6bl+d0PtFO+yxbo6MPB+iwY5thxpweUG2bt/L4W/aBR6aPLFmL+pjrJgeEJxVKBLg+PXaLzH34oExp4tQmx3Oofmx2TUgYHWQyUlE2+Y3t1XjeyMKdakrRgQ+lSy6Z2QfhGUzNqdnpzMATURERmAQkjQxIggUrdN79ERbhK3jC83IFLlhCLhmWK+4wUpZM2usEq1MTBlSgDe2Vgt9hkiHXe+bIyVP+4WDHSfkCnbQGaVFHkw4uxumvbix00JVoUKzCkUDHHldUwwbPs+h+dHZMSBgZAArETOXRNvmZ9d+idc2H9IlmKJ3Bq7SRfeM6oNwvsP4nJydzgC0c3l9fmw+WJ+Q1wgikgODkKSaER0UJQHCWPK6pGDuN4vQhF5cRW4YAODOC/qhT24Xoe+raTqtel+dJFaZiLQYTSQiHXa9b46UPu0XDnZkyBPsoM5Sk9148LIiRVmFSjLtppQUGDZ8nkPzIxMNCJzXKxtbquS4ATMjgCWaueSE4KWSALNewRQ9M3CjXUP12gcl7Dy9gVmcmp3OALRzle2txbPrNuFwQ0vH35jdSkRmYxCSVDGqgyIaIIxn/mVFUS+mop31PrldhG9oFqz9EmkpSQl9AdcrgCzSYdfz5khNMF002DGit/LsBycEAuKR5RiVZhUqzbQzcvg8h+aHixUQcOHM4jSTB3fHtS9tkmZ4oRkBLJFgm1OGXSpdfArQHkzRKwNXyzXUiOxeu05vYDYnZqczAO1MzG4lIlkwCEmqGNVB0dqZdbuAx6eVxLyIKrlhEL2hqT/VJv0F3OjAjx4B5BtHFgr9fnrdHKkNphuV/eCUQEAssh2jkqxCs4feyRKsNZPWY44WEOiZk45LizwRM7Ij3YCZ9dubGcCKFmxz0o2pmsWntAZT9GoX1F5DjRrua8fpDaxi9+z00PZOdHRPogeg7YTZrUQkEwYhSRWjnpBr7cw+fmUJLi3uHnMbkRuGvC4pqPnmxiTeIhPBZL2AmxH40aMzOlFwNWm9bo60BNP1zn5wUiAgGlmPUTSr0Myhd7IFa82g1zFHCgiUDivEhCffj/m+x9fsRWZqEhpb2vHs2kpTfnszA1iR2jIn3phGa5tj0XL90qtdULsPRg33dfJ8h0awa3Z6pHY3N13s9pABaPtgdisRycRt9Q6QPRn1hDzQ6Y0lJz05bJseWWl4avo5cQOQwLc3DLEcP9WGR1Z9gTlLduDZtZW4ZXRvoU5Z4AIuk0DgJ7TzEQj8lFXU6vI9WjujSm5mRMqJyOdpDaaXFnmw/PZxWDhzGB6bOgQLZw7DstljDZkLdcEHlfD61K8ObzWnHGMgwBGtDdIjSGVWnZWJ3sccCAhMKSnA6L652HLgeNwbsMaWdtz91k48uKLctN9e5HqkZwArdDslN6Z2Emib5148QGh7rdcvPdoFpfugZ5sTiV5lk+QVrd2tb2mP+16ZAtBenx9bquqxurwGW6rqpe9HWIHTKxCRTJgJmcBCh1+c1ysbOw83Cg0lMeoJuUhGwX9NLtY87EVJpkRgUZXvjTwLb279d9zPlukCbmaWi5q5uIIpuZnRK/NEj2C6HtkPifCE2knHaOTQOydmpsVjxjHXNLXE30iAEb+9HlnVatsyJ9+YJrldKMhKg9sFxIpJ6BVM0douiFxDM1OTcH/pIPTMDl90zwhOnO+QztA6j7csAehEHDWgBqdXICKZMAjpcF6fH5sPhq8CGumiHdpRj3URN3JYominV2ugIviGoabpNBas/RL1p9qibr+m/KjQ54pcwM2ab8zMwI9ImbhldG+s3lOjy82MHjdHsgw3s0MgQGuZtcMxKmHU0DsnBWtFiR7z1qp6uN0uVWWwICtdj1017Lc3I4AVqS1z8o1pWUUtHvx7/KlU9AymaGkXRK6hza1evPDRV7ivdJBpASC7z3dIkYnOQZrbJaVT31imALSsU7zISJb+LhERwCCko5XtrcWz6zbhcMO3GSAFmamYMqQg4uT8oZkC8S7iRj4hj9bpBYAtVeFBVbUCNwxbqupjBiCBM0O0QztjoUQu4GY+tTU78CNSJu6+8Gzdbma03hyZOcdfLEYEAiIFDQGo+q30KLNODnboyWnBWhGixzJ/RTkag4YJKimDY8/upilTO5hRv73RAaxIbZlTb0xFsrxEFrIzm8goDSsCLHad75CiE23H7r14AAqy0qQLQCfiqAEtgq8RLgCRksNlyW4lIudjENKhYj0djBSAjCXWRdzIJ+ShnV4jg3einbErSgrwxtbqqK/Hu4Cb/dTWisBPvDKh982M1s+TYbiZ3oGASHUlOz0ZLgANCoM4epVZpwY79JaIwVrRY2kMmadMSRlMcrtwf+kgPKBg1eRoZP3t1bRlsjyI0ZtIlpfPD+R1TTFpj8SVFnkw4exumPbixpgPPRlgIS1E27GCrDQpA9CJOGpAq9IiD341/Rw8u+7LTgkqMmW3ElFiYBDSgbTO8xIq3kXcjCfkRgfvRDtjEwflY3jvHFUBKyue2ioN/Og1TNxuWRNWDzfTIxAQOHfr9tVFDJSHBnCA+PVHzzLr1GCHKNG6lYjBWq3zyYqWwdJi5asmhzLrt1fbFqtpy2R4EKM3u2cU7zzcGHd0RnDfzKwpXsg57H6tsXsdt0ppsQfXnd8f/9x+iO0FEVmGQUgHEp3nRQmr56IzOninpDOW5HapClhZ8dRWSeAn0Sf3tjpwqiUQEOncKRGt/uhdZp0Y7BChpG45KVgrGhgROeZYlJbBiQPzsbWqPmx4twgzfnutbbGatszqBzF6s3tGsZIAi5Ov3QyuGsfu1xq713ErJbldGN03F34uIk5EFmEQ0oGMCBhaeRE3I3gn0hn7z4sHaBpSbNVTW5HADyf3loOaQEC0c6dEtPpjRJl1WrAjHjV1y8xgrVE3+UoDI9GOOTs9WShQqKQMJrldGNMvD7+YXCxcd8wKlFvZFlv9IEZPds/yEu1zVdWfwqL1B8L+7oRrt5ODq7Kw84NBu9dxIqJExiCkA+kdMLT6Im5W8C7ehPDPflAJt8ululNm5VPbWIEfTu4tFyWBAD2nXohUf4wqs1qCHXbKjNFSt8wI1hp1k682kBbpmL0+P+5+a2fc71TTbsa6Af/Piwcgr0uKqeWMbbF+7J7lJRJgKchMxdIdh2N+jl3LCx+MmseuDwbtXseJiBIZg5AOpHV+rVDBF3ErAgBmBu9Kizzw+fx4cEV52GtaO79WP7WNFvjh5N72pefUC5Hqj9VlNpSVmTFq2j6tdcvIzDSjbvKVBtIi/a7Bx+z1+Q0tgzLdgLMt1peds7xEAizXDOuFFyNkQQazY3lhMN58ds2CtnMdJyJKZAxCOlBw59UFINKUH7eM7o3Ve2o6XbTdrjOrRQaEXsStCgCYGQjx+vx4dm1lzG3Udn5FbipKizzYXt1g6k0wJ/e2L73OSbT6I1OmgZWZMWrbPlnrlpE3+UoCaQ0t7XF/VzPKoCw34LKWFzuTKcisVLwAS1u7T+hz7FZeGIwnJexcx2Vip1EmRGR/DEI6VGmRB7+afg6eXfclDje0dPw9OLB494Vnd7rgnNcrGzsPN0a8AFk9T5VZgRCjO7/RbioCAeA3tlbjja3Vps57pDTTlB0Veeg1dD9W/TEy0yC0LEVrg6zMjNHS9sk6cb7Sdk5JnRcNeERbxT3S75oo2S6ylhe7kyXIrEasAMuWqnqhz7BbeWEwnpSycx2XAedfJSKzMQjpYKXFHlx3fn/8c/uhiDePkS7akS7iMgyNMesm1IzOb/BNReBG3BeSrmrmvEdKMk3ZUZGL1qkX8rqkYP5lRXHPnRGZBpHKUmg2dqBs5aQnW5IZo7Xtk204e4CRK++KBjxWldfEfD30d02EbBdZywtZK1qAxanlhcF4IvNw/lUisoLb6h0gYyW5XRjdNxdTSgowqk+uqhs2JVkzRiot8mD57eOwcOYwPDZ1CBbOHIZls8fqenE0q/Ob5HZheGEO3t97NOZ2Cz6ohDc0QqmzQKZpLPdOGoh1lXWYt3x3WFkIdFTKKmqN3E2K4tphvVS9L69LClbcMU64/gRuhLW0JQGBTm9oWYoWjF+3r07oc/XOjNHa9onWLbMDaUpW3lVa5wOBkVjyuqSg/lRbzG0i/a56lkEZyVpeEpXX58eWqnqsLq/Blqp6w6/FSjm1vIi0IXYMrhLJRvRBq2xtHxHZH4OQFJdMQ2OMvgk1s/MrS3AX+DbTNPTYe2Sl4anp52DiwHx2VCRTVlGL6Ys3YlGchQmimX9ZEVKTzb8EqFnR+904WXMBemfG6NH2xatbVmQYiLRzoivvhtZ5kcDI5SUFQvuZiMMtZSwviSjQvs5ZsgMPrdyDOUt2YPrijdI9bBO5dsscSI3EqcFVItnIdB9CRImFw7EdKjCH1+lDjUjz+RQNWwud/yuva4rQ+5wwNMbM+SdlCu4C8eeesuNE8U6dvzLa8BkRZs2jF+23V7Oi9/FTbciNkz1nRGaMXpnRsg0lNnrl3XjTZ+SkJ0ecDzKUE64pashWXhKN3YYnRisv6yrrMH3xRltOn5Io88ASWUm2+xAiShwMQjqQlnn7Ir23e0YKctKT0dDSHvV9ThoaY1bnV8Z5j6LNPWXHjopT569Uk0kIANnpyXjiyhJThrHG+u1FV3QNdUVJQczAlRGZMXrOuSbbxPlGr7wbK5Dm9fkdOZednmQrL4lChjmw1QgtL3YLpEbCYDyRsWS8DyGixMAgpMNo6XhGe+/RE7Hn7gKMHxpjdkabGZ1fO00qb7eOihNuwKJRk0kIAI0t7Uhyu0wJQMb67e+4oJ+qz504KB/De+eYmhmjNjPaLhm4Rq+8Gy2QZmbGOZESosMT39xWje+NKJSyjNo1kBoJg/FExrHTfQgROQuDkA6ipeMp8t6M1CR0TU3C0TgBAL1vwK3KaDO682vWjbge58NOHRU73IBpOSdask2NzlQV+e3f2XFY8YregbKV5HaZnhmjNDPabhm4Vq28y+GW8tPzWm6XwLxoG/ns2i/x2uZDUtZrJfO8McBHZrFLG5BI+ECQiKzCIKSDaOl4irz3RKsXyW4X7rigH/rmdonYidD7BtzJGW2A8Tfiep0PqzsqSjqvRtyA6dl51npOtGSbGp2pKvLb1zS34s4L+ilaUCe4bFmRGSOaGe2k9sqMOs/hlvLS81qu9LOsDFYoaSNlrdd2nD6FnM1uD+cSCR8IEpEVGIR0EC0dT9H3NrS048X1B/DU9HPCAgF634DbIaNND0bdiOt9PqzqqCjtvOp9A6ZH59nr82PboQas21cXcV5DJedEJEMtErVZa0oCAqK/aZ/cLhHLktsFBC/eKlq2zAhaxAt+OrG9MqPOc7ilfPS8dij9rEjtbXZaEm4Y1RuzxvU1vO6oaV9lq9d2mz6FnM2oh3PMrNQPHwgSkdkYhHQQLR1PpZ3R0E63ETfgiTSkSO8bcaMCImZ3VNR0XvW8AdOj8/zursN45J1dQje1IudEJEMtEjVZa0oDsEp++1F9csPK0nm9srHzcKOisiVLhoVT2yvenCQWPa8dSj8rWnvbeNqLF9cfwJtbq/Ffk4sNn4ZFafsqW7220/Qp5GxG9UVlue47CR8IEpGZ3FbvAOkn0PGMJVrHU+S9wQKd7gAlN+CiOKRIPSPOR0CgozKlpMDQlZZFO6/e4NQ5aKsHenx/sLK9tbjrz1uFs2pEz0kgQy30OHPSk5GT3vnZUo+sNFWZBoGAQOi+BwKwZRW1Ye9R+tuHlqXUZLeisqVmH43i5PbKrDpP1tPz2qHks0Ta24aWdlPqdbT2NZZ1++oM3CNlAoHUWDjPG5nBiL6oTNd9IiJSh0FIB9HS8RR5b6jgm2kjbsA5pEg9JwRE1HZe9boB09p59vr8eLpsH6KHKCMTPSelRR4sv30cFs4chsemDsHCmcOw+q7xWH3X+E5/WzZ7rOIApNoArJk3vyL7+EzZvphBYj2xvSK1vD4/tlTVY3V5DbZU1ZtWZiPR89qh5LNE2tuAeA9/9BBoX+dePEBo+ze2VksV/IgWSFX7UIpIDb37ono8HCYiIutxOLbDaJnDK/DeJ9bsRX1Le9zvCr6ZNuIGnEOK1HNCQERL51WPuey0dp6V3FQHU3JOog2f0brgjpahxWbNHSq6CM7LGw9i9vh+unxnLGyvSA3ZhhXqee1Q8llKHoiZNfw5ye3C90YU4rXNh3SbTsNMnEpBHok6f6HefVGnTntCRJRoGIR0oOCO52m3G2k+n3CHp7TIgwlnd8O0Fzei/lRb1O1Cb6aNuAG3ekVmO3NCQERr51XrDZjW71eTZWrmOYkV/Ghr9wl9RrRjNOPmV/T3XbT+AAZ4MgwP6LC9ii5Rb8DjkXE1dT2vHef1ykZulxShvoTSqUHMyuJXMkekjMEPzvNmPdkeNJhJ776oE0b5EBERh2M7VpLbhdF9c3H18EKM7qtsDq/UZDcevKwo5jahN9NGDcPkkCJ1nDAnlB5zO2qZy07r96vJMjXrnMSbU+lg/Smhz4l1jEbPI6jk9zVreBbbq3BlFbWYvngj5izZgYdW7sGcJTswffFGqYauWkHWYYV6XTvKKmpx7UubYgYggz9L6bzUZmbxlxZ5cOPIQqFtIwU/ZBpubzQlx5oIv0uiz1+od1/UCaN8iIiImZAUhZohlUYNw+SQInXMGhZrFKszy0S+v7TIg+3VDRHLY+Cm+mhza9x5Ic08JyLBjyVbq5GdnozGGNMyWJ1JK5JhEWBmhhLbq2/JmOknC5mHFWq9dkQ778FCP0tJxmG8tkdN5m2890wclI83tlbH3bfQ4EciZcEpOdZE+F2MWhnabvTsizphlA8REQEuv9/vvEePCtTWNsGpv4DLBXg8WZqO0YjOPJnL7ucj0s2KmUG7SN/vdgHBSRvRbp4++OZmPFr1u3FkISYOyjf1nGypqsecJTs0f44MASSRYEfAY1OHYEpJgcF7RAFenx/TF2+Me7O4bPZYw8u+HtdCva0ur8FDK/fE3c7Kcqv2+h/vvOd1ScGKO8YhNTl8ME5ZRS0eX7M35gOQWG2PmuCWyHvUlOd47ZMMbaheRI71kmIPPJ4sLFn/FR5IgN9F9Fq7cOawhBgyr1dfNJHqlRFkvB4SJRqn1sPAcYlgJiTFpGY+Ic5BJBe7nw+rM8uCv3/dvjq8sbUaoaPGomV2lRZ78Pvvj8Qj7+ySJhtV61xJMmXSlhZ5cMcF/fDi+gNxt7XT8Cy7PzgA5M70M5LoubPDsEI11w6R8378VBt2Hm6M+NmB9vbljQfxxtbqTsHIeG2Pmsxb0fcozcxXmgVn5zoveqwXD8qH1+fH0wmSHcj5CzvTqy9q91E+RESkMgi5Y8cOPP/889i2bRva29tRXFyMW2+9FVOnThV6/9tvv40HH3ww6uuvvPIKxo0b1+lvpaWlqK6OPBRm7NixePXVV8UPgIhsxepAamDOskfiZC5Funm6fGgvjCjIwLZDctxgqg1qZKcn44krSwyZ31GLWeP64p0dhx0zPMspwxQT8QZcyblz6rBCPc57ktuF2eP74bZxfYUDc2qGvip9j5Lgh5IgfENLu63rvOixbjvUgJymtoR5OGGHBw1aWRU8t/rhNBERaaM4CLlhwwbMnj0bqampmDZtGjIyMrBmzRrMnTsXR44cwaxZs4Q/65JLLkFJSUnY3wsLI08AnpWVhR/+8IfC2xMR6UVLZpfVQdRgSuZSDNbY0o4kt0u6Tr7Vc4fqyUlzKCbCDXgwpefOSeU2mJ7nXUm7qaZ9VvMe0eCHaDA2kF0fyk51XjjwfKIVp91i62E64eGEUx80BFj9wExtv8rOWcdERE6hKAjZ3t6Ohx9+GC6XC6+99lpHAPHuu+/G9ddfjwULFmDKlCnCQcFLL70UM2bMEP7+7Oxs/OQnP1Gyy0REunBKZpeSBSBCyXpsdh6eFbghqmk6jQVrv4y5rZ2GKTr9BjyY2gUo7Fxuo7HqvKtpn9W26SLBD9Fg7Krympiv26HOCweeM1KRk5Wu62fKzKkPGgD7PjCLFzh1aoAy9Lgu65Zp9S4RUYJTFITcsGEDDh48iBkzZnTKYMzKysKcOXMwf/58LF26FPfcc4/uO0pEZCUnZXZFC37EI/Ox2XF4VqQboljsNEzRyTfgobRkSdux3MZi1XnP65oitF1wG2Zkmy4SjM3rkoLjp9pifo4d6rxo4HlE7xx4PFmagtR2CxI58UGDXVf9jhc4vWV0b6zeU2PbaRGiidTP6LV6L+ZOHIBJNj4uIrI3RUHITZs2AQAmTJgQ9lrgb59++qnw5+3evRv19fVob29H7969MX78eOTl5UXdvrW1FW+//TZqamqQmZmJ8847D9/5zneUHAIRkSpOy+wKDn4EsvDqY9wQ2+HYZBr2Ho+Slb2DyZqNGokTb8Aj0ZolbadyK8Ls815WUYun36+Iu11oG2Zkmy4SjL28pCDiUOxQstd5JYHnJLcL95cOirk6drQgtdXDf9Vy2oMGOy46JhI4fXXzobC/yZ7ZGU+0fsaRhhY8YOPjIiL7UxSE3L9/PwCgX79+Ya91794dXbt2xYED8VcpDQhdTCY9PR1333037rjjjojbHz16NGxBm/POOw8LFixA3759hb83mMuefQAhgWNz8jESmSU5Kf7N032TBiI56dsKJ3sdTE5yYXTfXABAekqSomML5vX5zyy8c6IVnoxUjOht3xssM4jcEEXjyUyVtjxFckmxBxcPyre0fBhdD0Uz5arqT9nq3Glh1nkv2ysezA9tw9S06dFEagMvKfbgV9PPwdMRgrH3TRqI7PRkoSCkHep8vGMtLfZ0HMMlg+NvGyraeQ4EiX41/ZyI7wul9Vql9v3B11q7qz0hPgeoLOVWJHAaS2B1dzv1a2L1M/zf/L8dj4vICWS/P1RLyfEoCkI2NzcDODP8OpLMzEw0NTXF/ZzevXvj4YcfxoQJE9CzZ080NDTgk08+wYIFC/DMM8+gS5cuuOWWWzq9Z8aMGRg1ahSKi4vRtWtX7N+/Hy+//DKWLVuGW2+9FcuXL0dmpvI5LvLzIx+LkyTCMRKZYaYnC9nZ6fjvv+/G4YaWjr/3yknHL686B5cP7RXxfXaog2qP7d1dhxW/J9F9Ulmn+IbIBaBnTjouG97bljcMlxdkW70LhtXDy7plotfqvTjS0NJxcxfJi+sPYMSAfEfUC6/Pj01fHUNNUwsKstIx9uxuEculkefd6/Pj2XWb4m4Xqz1S2+4Fi9UGzrzgbFx3fv+Iv5XX549ZbuxW52d6sqIea7D8/CzMvEBsWyD+eXYBeHbdl7ju/P4xfyet1ype684YeJbYtWvgWbnweOTo+5w+1Kjp/V83ncaXTW0YPzBfpz0ynkg/w47HReQkdrg/NIrL7/fH6jN3MmvWLHz88cdYs2ZNxGzICy+8ECdPnsSWLVtU7UxFRQWuu+46dOnSBR9//DGSk+PHSB944AEsW7YM8+fPx2233ab4O+vqmiD+C9iLy3WmcDv5GImsIJoNYcc6qCTTo2xvbcwsItHslETzbnkNHvrHHsXv4++pTrR6qGcGb7y6ENAjKw3Lbx9ri6BSNGV7a8Oy2AoyU3F/6SBTy+fmg/WYs2RH3O1euP48jO0ffaofQH1Z0NoGJlIbqvZ6KHqeF84cFjXbkOdJP16fH1e9uDFmgMvtAv73yhJcOri7iXsWnWgZiuWxaUNweUmBTntkPNF+ht2Oi8gJ7Hh/KCJwXCIUZUIGMg2jZTs2NzcjJ0f9nGFFRUUYNWoU1q9fj8rKSgwePDjue773ve9h2bJl2Lp1q6ogpN8PR538SBLhGInM5HaFz+EWq47ZqQ6KHpvX58fTcYYUP/NBJS6SbHJ6GXgylC10EZhLb1KRx/ByZLeFH5QIrod6zC8X+lvdfn5fLN5wMOZ7vm46jW2H5JkrTalYizuYPceY6FyJx062xa03Stt0QJ82cFKc+TPNqPNmU3o9VDLnqhHXKl7rOnO74s8B6vMD8/9ejqdcLinmHBSZ/zUeT0aqreqiaD/DbsdF5CR2uj/Um6IgZP/+/QEABw4cwNChQzu9dvToUZw8eRLDhg3TtEOBhWlOnTqlaPuTJ09q+l4iIhJnx8npZSG6cu7ciweg4JuFMcy4ubXrwg9KxVslVSSQFum3yk5LEvp+2RcaiUa2VXGNXN1ahF5toNMWLtGb1vOs9TzxWheutMiDJ64qwS9WlMMX4wZallWyRRZPisUOC/OFctpiikTkLG4lG48ZMwYA8NFHH4W9FvhbYBs1vF4vdu3aBQA466yzhN6zY8eZ9PrCwkLV30skC6/Pjy1V9VhdXoMtVfXwxurdEVlI64rAMjO6HgZuiGKZf1kRrjinB0b1yTUtADlv+e6wG5ZAYK6sotbwfTCDaCAt1jmP9ls1nvYK7YNRQTGjKQnGmCFwkx2LkTfZeraBgRXSp5QUmFbn7ULredZ6npx8rdMir0tKzAAkYG57EE/pN1nHoWWpR1YabhndO+Z7o63YLrNY/YzAkdjxuIjIGRRlQo4fPx59+vTBihUr8IMf/AAlJSUAzgzPXrhwIVJSUnDNNdd0bF9TU4OmpiYUFBR0Wsxm165dYZmUXq8XTz/9NA4cOIBx48ahoODb+SkqKytx1llnoUuXLp3eU1lZiaeffhoAcNVVVyk5FCLpGDE8kNkUZBSrs5CMYlY2YGmcYZhmZh7KluFmJK1ZTVpWNgfsnXkiWzBGJLvJyJtsp7aBstF6nrWeJ57nyGRrD0TEyjoeela2FNdjPUXrZ/TMScfciQMwyabHRUT2pygImZycjMceewyzZ8/GzTffjGnTpiEjIwNr1qxBdXU15s2bh969v32atGDBAixduhRPPPEEZsyY0fH36667DoMHD8bgwYPRo0cPNDQ0YNOmTdi/fz969uyJxx9/vNP3rly5Ei+//DLGjBnTEYzcv38/PvzwQ7S1teHOO+/UlIFJzmSngJxRwwOdOJSS5GC3oT4i7YEe9VDpfiz90VjsPNxoaTtl1nBDGdpkrTfOIr9VLEqDYjL8ZgEyBmP0DuYr+b3t1gbamZbzrPU88TxHJmN7ICKQdRzKqdMiRDquy4b3xvFjzQk7Fx0RWU9REBIAzj//fLz++ut47rnnsHLlSrS3t6O4uBj3338/pk6dKvQZs2bNwvbt27F+/Xo0NDQgJSUFffv2xV133YXbbrstbHGbcePGobKyEuXl5di8eTNaWlqQl5eHiy66CDfddBMmTJig9DDI4ewUkNMjC8mM4AlRMKuzkJQQaQ/MyAaMtR9TTF6dMjjY8tWxE0Lv0ZLRIkubrPXGWfQ3yE5PRmNLe8e/1QTFZPnNAmQNxugVPFD6e9upDXQCtedZ63nieY5M1vZAi2gBSrsLPi6XCwlXVolIPi6/P7Gfg9TWOmtp9GAuF+DxZDn6GCOJFpALkC0gt6WqHnOW7Ii73cKZw6IOD5y+eGPcjuCy2WPZ8TBZItTBSDfuMg1hEm0PtNZDvfbDDJHOmQi7HntwPWz3amsvRcvJC9efB7fbpTooZvVvFo2s+6WVluOSvQ2UhdXXQ63nKdr7//PiAcjrkqJb9pxM2c/xOLU9cDKr6yERObceBo5LhOJMSCKZ2XFuMzOGBybayo1kHpmHMClpD4yc30qmdineTWM0ajNaZDp2QHtWk2j2z0gNi4vI9psFk2kuU71o/b1lbgPpW1rPU6T3Hz/Zhmc/qNQtW1m27Od4nNgeEBGR8RiEJEexY0DOrOGBRk0Obqen9mQMWYcwKWkPjJzfSpZ2ScuiKmqHG8py7MG03DibMTRTxt8smNOCbnr83rK2gWo59bqu9TwFv7+sohYPrigP20btNDgyTKuj5rw7rT0gOWhtg5zahhE5BYOQ5ChWB+TU0DqvjpWTg9vtqT0lFiXtwaWDuxs2v5Us7ZKaRVW0ZrTIcuyhtNw4RwtiZqcn44aRhZg4MF/Tvsn6mwVzUtDNDr+3mXhdj0/vbGUZsp+1nHcntQekjp5BP61tENswIvm5rd4BIj2JBtqq6k8ZvCfiApk1sYgMD4zFiMnBA0/tQ4Magaf2ZRW1un4fkVJK2gOt9VCP/TB6FVHRIMqscX3w2NQhWDhzGJbNHivUaff6/NhSVY/V5TXYUlUPr+/MJDdGHHu071IqcOM8paQAoxQOny4t8mD57eNw5wX9kJ1+5nluY0s7Xlx/ANMXb9TU/slSXhIFf+9vyXpd16vO60VJ9qwVn6eUrOed7KGsohbTF2/EnCU78NDKPZizZIfq66DWssiyTGQPzIQkRxHJKgSAResPYIAnQ5onYrIPDwwlw1N7onjUtAdGzG8lyyqiokGUsf3yFGW1xMo6mDgwX/OxB2dYVNWfwtIdh03PcIiU5bGusg6L1h8I21Z0+GS0zBFZyotadhsGZ/ffWy+yXtdlzGrSO3vWymxcWc+7DOzWlllBz2kEtJZFlmXzsY6QWgxCkqOIBOQCZLsQGTE80KjJwWWfsywUL5KJSU17YMT8VlY8KIjEiGCLyA2IlmMXWcnb6DnTIu1D94wUtHpjZ2PFusbEC6zIUF7UkDFgFI8s9dNqMl7XZZgnMRK9s2eVbhfo09Q0ncbxU23I65KCgm/abqXlVMbzLgM7tmVm0zvop7Ussiybi3WEtGAQkhyntMiDOy7ohxcjZKgEk/FCpGVeHTMnB7fTHFq8SBrHDsFdNe2BEfNbybCKqN7BFtEbkGWzx6o6dqUreRvxYCnaPhw90Rb3vV83ncbWqnq43a6wDEqRwIrV5SWSWHVe1oCRCFl/bzPJdl2XOatJ7wc6Sj4v1oMZNX0b2c67DOzclplJ76Cf1rLIsmwe1hHSikFIcqS+uV2EtnPahcisycHtMocWL5LGsVNwV5b2QIZVRPUMtii5AVF67GpW8tb7wZKW1cQD5q8oR2NLe8e/lWRQylBegsUbdi9rwEiUbL+32WS7rsuc1aT3Ax3Rz4v2ACNATd9GtvNuNZmD37LRO+intSyyLOsjXoIB6wjpgUFIciReiIxlhzm0RC+SE87uhp2HGxPyplMtuwV3ZWoPZFhFVK9gi9IbECXHrmYlbwBYt69Ot99X7T4ECw5AAuIZlEZm5aoRr87fcUE/aQNGSsjye1tBtuu6HgEOI7P19c6ejfd5EwfmY/rijUKfpSQAINt5t5rMwW/Z6N230loWWZa1E0kwYB0hPTAISY7EC5Gx7DCHluhFctqLG1F/6tvAgKzZfLKw2xNQr88Pn8+P7PTksIBQMLXtgR2GpEeiR7DFyOCu2qzUN7ZWY3jvHF3qr5WZ8jJl6YvU+b9srRb6LJmOyyqythmyXde1ti+Rbqaz05Jww6jemDWury7HoXf2bKzP21JVL/xQREkAQKbzLkPd4JBecXrfa2ktizKVZTsSTTBgHSE9MAhJjsQLkfFkn0NL9OIXHIAE5M3mk4WdnoCKLGoSoKY9sNOQdCMY+bBHS1aqXkFwKzPlZcrSF6nzsQL8wWQ6LivI3mbIdF3X0r5Eu5luPO3Fi+sP4M2t1fivycW6HI/e2bPRPk/pDb2S7WU477LUDZlGTsjOiHstrWVRhrJsR0oSDFhHSA8MQpJj8UJkPJnn0NJ68ZMpm08mdnkCKrqoidr2wG5D0o1g5MMekQBENHoFwUX2ISc9GWnJ7s7ZVnGybuMRDdyalTUkWpeNyjZ2Cru0GbJc10XbFwDYUlXfsa/n9cqOezPd0NIu1W8uQmmfRun2Vp53meoGR1IpY8S9ltayKEsbZidKEgxYR0gPDEKSo/FCZDxZ59DSEsQA5Mnmk40dnoCKPNHNTk/GE1eWdMy7p/fnyxjENiJoZdTDHpEARCx6BMFF9uG/JheHXWO8Pj/ufmun6u+NFLgNPXfHT7bh2bWVpmQNidblG0cWYlGMVegTefSB3doMWa7r8doXAJi+eGOn13LTk1Ev+BBApt88HiV9GrUBACvOu2x1gyOplDPiXktrWbS6DZNhagEllCQYsI6QHhiEJMez+kJE6mm5iGsNYgDWZ/PJyA5PQEWHjya5Xao6SXYakh5g5FA3ox72RAtAiNArCC4aZA0+z16fX1UGZbTArei0AkZlDYnW+dvG9cUATwZHH0RgdpthpxvgePsarX2JtlK0aAASkK+djkVJn8ZOAQAZr6ccSaUc77W+JcvUAkooTTBgHSGtGIQkIinpcRGPdpHM65KC46fir1LL+UzC2eEJqNFDxu0yJD3AjKFuRt2AhAYgunVNwaPvfmFqEFxpkFVtBmWkzxSdViCY3llDSuo8Rx9EZmabYacbYNF9DW1fRLLnRMnSTouI92DGjgEAWa+nbMtIDZmmFlBCTYIB6whpwSAkEUlHz4t4pIvkeb2yce1Lm6TO5pOZkiegVmTkGD1k3A5D0gNkG+qmRmgAwooguNIgq5oMylBqAy1Ks4ZE6qiSOs+MmHBmtRnxrp13XNAPfXO7SHGzqOU6L5I9J0qGdlqJ4D5NTdNpHD/VhrwuKSj4ps8iazsejRF1Q69+B9syucie4W3n/pbaBAPWEVKLQUgikooRF/FIF0nZs/lkJ/IE1KqMHKOHjNthSHqAjEPdtLLLMCCtWQJaAi2iWUNK6iizHtQzo80QuXa+GDRnp5XZkVqv83plxcnSTivlpBt/veuGnTKBSVyk89o9IwXXfuesmA9WzAxc2r2/ZZe+FTkDg5BEJBWzLuK82GoX60YoXpbLE1eV4NLi7obtl5FBZjsMSQ+QdaibVnYJiGkJFmg5JyJZQ2oy0bQGP2TPZDGKGW2G0qC1lcMDtV7nRbPiuqYm4WSrN+rrsrTTiUzPumHXobAUW7TzevREW8wHK2YHpJ3Q37JL34rsj0FIIpKKmRdxXmyNIZLl8osV5cA04NLBxgQijQ4y2yWIbaeh40oFB8ScGNxSe07cLuD4ydhz3loxbCzRM5T0bjNCy3xN02lV+2XF8ECt13nR7Lm3Z43BK59W4Y2t1WgMWrBGtnY60elRN+w8FJaiUzItSXCwGYDpAWmn9LeclGlN8mIQkoikYvZFnBdb/Ylkufj8wIMryuH+ZjELIxgdZLZDENtOQ8fVEg1ueX1+bDsk77kKJXLuIolUt0IDVl6f39RhY8xQOkOvNiNSmc9NV9elN3J4YGi5u6xbJgDt13nR7LnUZDdmj++H28b1lbqdJnOmr5B5KCxFpmZaEpGgpREBaav6W058CEvOxyAkEUklEYImTqckS9XozASjg8yyB7HtNHRcDZHg1iXFHry76zAeeWeXrbLwRM5dLIG6ta6yLixglZ2WJPQZemScM0OpM61tRrQyXx+U6aeUEcMDIwVKe63ei7kTB+Cigfmar/NcLMl5zJi+QuahsBROzfkSCVoaEZC2or+V6CMMyL7cVu8AEVGwwEU8FjsHTRKBkizVQEeQjBO4WS8IOS89stJsnYEmGtx674ujuOvPW8NuTAKByrKKWiN3U5PSIg/uuKCfqvd+3XQaL288iHnLd4cde+Pp6PPkBdMj41xJhhLFpnbF9Hj0Hh4YCJSGnvcjDS14YPlurKus0+U6X1rkwfLbx2HhzGF4bOoQLJw5DMtmj7VtmxaJ1+fHlqp6rC6vwZaqenh9fqt3SUpOGQobSyKWBSPPlxEBaTP7W9HaWTv0bYiYCUlE0rHLfHsUmdJhpMxMMJ4dho4rJRrcevK9fYh1qyZ7Ft6scX3xzo7DqlbKfmNrterv1SvjnBlK+hEdmpjbJQX1p2LPCxqg98iCWIHSQD1c8EElls0eq8t13slZjsxyEuf0UTSJWhbUTksiwqgApxn9LY4wILtjEJKIpOTEoEmiUDqM1M6ZCXbitJt10aBVvGCM7POEaRmW3ahhiK5eGeeJkKFkFtEyf+/FA1CQlYba5lZU1Z/CoqAVZMO21XlkgZLMV17no+M8qso4eeqRRC4Laq5/gSxEKwPSRve3OAcq2R2HYxORtAIX8SklBRjVJ9eWncdEVVrkwRNXlSDeKbNzZgJZS8+glexZeNGGeMWSLbhQSeh2eg8bC2SyxKK2HUi04YmiZb4gK63j2jl7fD9Tp2NQmvnK6/wZwWX50wPHhbKcnF7elXLi1COiGW9OLgtKr3/3lQ5y/LROHGFAdsdMSCLSjRkrtHEVOPu4tLg7MO3MSr3R2L0jSNYRGaaV1yUFxwWGpdohCy80ayxehtsNIwvxYozXA568sgRut8uwNtWoDKVEHJ6odsipmRmHiZD5qnc/JFJZjodZTpE5LbuWGW9nRLr+LQ2ZpiR0KgcnT+uUCO0sORuDkMSgDunCjBvCRLzptLtLB3eH2+1ybEeQrCMS3Lr/koH4zdovcbS5Neq8kHbKxg0d4jXAkxG1bk0cmB93LskeWWkYaUL2md7z/Jo5PFGmPpKWgG5o2Qlk3ul9XJybT/nnqZlqAWCWUzROmnqEGW/fCj2vt43rG7NtdlpAOpjT21lyPpff73du/raA2tomOPUXcLkAjycr5jEyqEN6iNeJ1uOG0Izv0JtIHUwUMt3IGyURjlFGsbKICjJTMWVIAf68+VDUIKSMbYcSscqdbO2mHnXE6/Nj+uKNcW++ls0eq7n+6dlH0rN9iLRfSgK6Wo5L5DiilTsXzixOE6/cydqW6l2fRMpyLAtnDnNMsI0i21JVjzlLdsTdTrQssF/qHLJd30mcU+th4LiEtmUQ0lknP1i8As7Gi/Rgxg2hmTedenLqRYbC8YGOtd774mjMYf93XnQ2lm6tTshsXK0BK9nofVMejZ59JCPah0iBOgCqA4QBsY5LyXFE2rZXTjrmThyASTGOWca21OvzY2tVPeavKI+52JPSfohoWdbju8ie9O7/sl8qRtYHIaGcdn1PFE6th0qCkByOnaBEJzqeODBfykaX5GHGfDWcE4dklsgrV6qhd+fe6/Pj2bWVMbdZ/tlhvDN7LHb8u1H6mwq9yTgkTUsZWLevTmg7keGJ0fZDzz5SvPbhjgv6oW9uF8W/Q+jQRJHgndLjCv59os1BGjiOG0cWYuKg/I5jiFTuLhveG8ePNcccnSNbW6pkvkal/RAtQ2iDh93bJWBCyjl51W9ZyfggJBoZr+9EIhiETFAM6pBezJivhnPikKz4QEcZIzr3Itezww0t2PHvxoS9nsk0R5qWMlBWUYs3tlYLfU+8Cflj7UdOerIufSSR9iF48SC1dUE0eKek79fQ0q5osZQ3tlbjja3VKMhMxdxJA5HXJaXjpvjSwd2RnOSK2QbK2Jaqma9RST9EzaIRoVlOdgqYkDp6z6lL0cn4ICQema7vRKIYhExQDOqQXsxYoY2rwJGs+EBHnFGde+Hr2Qlez6ympQyIBKkC4k3IH28/bhxZKPQ98cqeSPsQ6fuV1AUlwTvRurJuX51wsDdUTXMrHvx756kRCjJTcX/pIMyMMUxLtrZUSXkLpqQfIrK4REFmKn55+WAcP9kmPOerDAETZmfqixlv3zKqbMn4IITIqRiETFAM6pBezFihjavAkaz4QEeMkZ174etZBq9nVtJaBpQE9GINTxTZj3fLa4S+J17ZU1vvldQFJcE70bqySvD4RdU0t+KB5buRnZ2O0T0zI2/TdFros8xqS5UGkAHl/RCRobb3lQ7C2H55YX+XOWAimp3JQKUyzHgzNvNXtgchRE7mtnoHyBqBoE4sDOqQiEAnOhat89WY8R1EavCBjhglnXulRK5nvXLSMaI3r2dWUlsGvD4/tlTV4/29tULfc+PIwpg3oyL7cfxUG3K7pMTcRqSPpLbeK6kLSh6EiNSVvC4pqD/VJvSZSs3/205sOnAcXl/nSSHLKmqx4IPY87oGmNWWqgl2qumHBIbahp6XHllpMTMZjWxTtQhkZ4buWyA78729Rzu2m754I+Ys2YGHVu7BnCU7MH3xRpRViNVzso9AG766vAZbqurD6r+oeGVLa9nhQ2Ui8zATMkFxomPSkxnz1XBOnG8xe0AezNIVY2TnXuR69surzkGS2+WoVQjtRk0ZULIoSMDEQfm67McVJQUxhySL9JFE2odoRPdTyYMQkbpyeZzj1qL+VBt+/NednTKXlMy7GNyWGn0dVBLs1NoPUTPUVsaAiUh25i9WlGPXyEa8tiW8jMkwjJz0pVfmohmZv2Y8VGb/negMBiETGIM6pCcz5qvhnDichF42fKAjxujOfazr2X2TBuLyob1QW9uk6rNJH0rLgJpFQfTMTpw4KB/De+do6iOJtA9a91Ppg5B4fb+c9GTDgpABgWDTE1eV4FnBDEjg27bUjOugyO+anZ6MJ64swag+uZrbeKVDbWXMwhfJzvT5ETEAGUxtMIkBHrnoOWepGUOljX6ozP470bcYhExwDOqQnsyYryaR58SReRL6RMYHOvGZkTEa7XqWnMTrmQyUlAG1i4LolZ0Y2I8kt0tzHyla+xCLkrqg5kFIrL6f1+dXnb2p1K/e24fjAkO/87qkYP5lRTEzJ4MXFZo4KF9zX1bkd/3F5OKI8zWaQWmbakaATq+sSzXBJAZ41LHLIi9mZP4a+VCZ/ffEwAch4hiEpIQO6hDZhcyT0BMf6MRjVsYor2fyUlIGtlTVKwqC6Z2dGFwW9ShToe1DVf0pLFp/QOj7RT9f6YOQaMcl8vvccUE/NLW0a86YFAlAAsDciwegtMgjdB18Y2s13tharUsASuYHTErKsVkBOj2zLpUEkxjgUUeGRV7e3FaN/K6pcftMZmX+GlHn2X9PDHwQoozL70/sGZJqa5scO0eUywV4PFmOPkYimelZB7dU1WPOkh1xt1s4cxiDMCStSJ00o2/oldRDPsU2nkgZWF1eg4dW7on7Wd8dfhYuKfaoOk9WlEWjv19r+Q1+f1X9KSzdcTjm/qmZs1ONwHVN9DoYTI8AlMztQrxyFG9aAz0DdF6fH9MXb9SlPIj2ZUS+s0dWGpbNHmv6OZOx3ASuh0vWf4UHDCwXom14sFgBGz3Pc+h5Oa9XNnYebux0ngDodu7Yf3c+pe2sU2M0geMSwUxIIiIbkHESeiKlZM4Y5VNsc4iUAdFslkuKPapv2qwui0Z8v5aszUjlv3tGCu64oB/65naJuH+RjuH4yTY8u7ZSOBCVG2c17uAhxWqub3pkGMmSYR0pqBVvaL2ZGVha5kANpmQ6AjPmClRDz+uJ3sFMr8+PpyVZ5CVYrMxVvUZTRDovbteZuUoD9L7uJ3r/XcZgvJ6Y6aoOg5BERDYg4yT0RGrIckMfzAnD+ezU0Y9XBsxadd7qsmj19wdEK/9HT7ThxfUH8NT0c6LuZ6RjmFTkwdaqesxfUY7Glvao39sjKw3/OXEAHlxRHnWb4MCCmuubFQEoI8QLakU6Pi0BOrXtSWmRB09cVYJfrCjvFNhRQsl0BDIGePS8nhjxcGzTV8ekWOQlmmgBG61DpaOdl9Byqvd1P5H774nwcFfWByGyYxCSiMgGzLopJ0o0TniK7bSOPledN48R5T/J7cKYfnn4xeTiuOewtMgDt9slFFhQG9iwe4aR2qCW2gCd1vbk0uLuwDTEDC7fMro3Vu+p0TwdgWwBHj3rk1EPx2qaWoS2M3qRl2hiBWzUZpCrWexMr+t+ovbfnfBwV4SMD0LswG31DhARUXyBDl0siXZT7vX5saWqHqvLa7Clqh5etWkXlNCUPMWWUaCjH3oMgY5+WUWtRXumTSDrpSAkeNAjK80xNy8yMLL8RzuHeV1ScOPIQuSkJ8Pr86O0yIPlt4/DwpnD8NjUIVg4cxiWzR4bdUimUnbOMBINakW6/qkJ0OnVnlw6uHvM+vvTiQOEznk8gQBPLGYGePSqT6LnvbXdp7gfVJCVHncbQL9FXuKdn0hiBWwC2ddTSgowqk+uUL9X5LyE0uu6n4j9dy3tlt3I9iDELpgJSURkEzKv1Gk2p2V+kXXs/BRbadaNbEO24+2P1fM2JgKjy3/wOdxU3Yi3t1bj+Km2iKtYiwxVi3YdjMbuGUZahvopzcDSOys2Xv3VYzoC2bKm9apPoud92osbO82pKtIPGnt2N9My80LLQN3JVjy79su479M7YKO2/dLrup9o/fdEGqKcqJmuWjEISURkI7wpT5whHmQOOz/FVtLRb2hplypwH+lBQm56Mq44pwcmDsrvaNdkmTfRqcwo/0luFxpb2vHyx/sRmveipt0Ovg6u21eHN7ZWR902UgBKtmB8LFqCWkoDdEYEDsyovzIFePSqT6LnPXRRJ5H6lOR24f7SQTFXx9YzcBtcBrw+P17bfMj0gI3a9kvP634i9d9leLhrVjsv24MQu2AQkojIZhL5ptwJ8/eRXOz8FFu0Ax8tUKNH4F5NRz/ag4T6lvaIGXKyMmLVWqNvmkK/47xe2YaX/8BqvLEG3qmZd3JUn1yM6pOL4b1zhANQdsui1xrUUhKgkyFwEI2arOnzemVj5+FGrC6vMS3go9f1RGvwK159Ki22JnBrVcBGzXyyRlz3E6X/bvXDXbPbeZkehNgFg5BEOrLT03UiO0qkIR5kDjs/xRbtwK8qr4n5utrAvZqOvugCAbJnNut9k2PGTVO075gypACvbj4U9X1ay7/R7bZohpEds+j1CGpF+30AYEtVfcff8rqmCO2T2VnhonUjOMBTVlGLa1/aZHqwWa/riZbVpQGx+mRVZp4VARs1C+XIet2PR4Z7USsf7lrVzidSpqseXH6/3/4zgmpQW9sEp/4CLhfg8WQ5+hhlYren62Q81kH9rS6vwUMr98Td7rGpQzClpEDTd8nQkSPtROthpDZc9qfYXp8f0xdvjNnRz+uSguMhQ/YiWThzmKIAULSOfkC0jv6WqnrMWbJD+Ht6ZKVh2eyxUtU9tcdu1uep+Q69ViuOxMx2OxqRumJ2WRO9xhhRPiK1d90zUtDq9aOhpT3q+8z+jdQcuxn1KSDaOdTjehLvOOKJVJ+U9EuN7gNZ0ceKdF7cLiB4fRTZr/uxyHQvamY9DJCxnY/EqfeHgeMSwUxIIh3Y8ek6kR2ZNcRDpo5cIrLi5sSOT7FFsjsuLymIOWdegJIhllqmRVA6lFO2zGa9p4QwY4oJke9Y88VRLP3RWOw83Kh7+bd6aB4gXxa9kmuM3plj0fqsR0/Ef1hhZnaYmrph5pQt8c6h1uuJ0kWYQmmpT2b0gawYmhxr2L5drvvRyHYvqqTd0qvPZ0Q7z4QEYzAISaQR56gju7HzBdWMIR6ydeQSjZUBYDvO1xSvo5+TniwUhFRyw6qlo6/mxlimlcn1vskxIzgm+h07DzcaUv4D7fbR5tao80IaPe+q0vkOjbxOqrnG6PWQRKTPmpOejLRkd1gbfM2wXmhr92FLVb0p/QY1dcOsYLPoOdRan4LPe03TaSxY+2XYYjSRaKlPTu8DRbrO2+26H0rWe1GRdkvPPp/e89oyIcE4DEISaSTb03WiWOx+QTV6/j5ZO3KJwuk3P4AxwY1YHX2vz6974F7Jgjih1z01c53JtDK53jc5ZiwGYvWCI4HVeK2cd1VJNqaR10kt1xg9HpKI9FkbWtrxwvXnwe12oba5FVX1p7B0x2G8uP5AxzZm9BvUlFszyrrZ/YTAed9SVS8UgATU1yen9oG0Xndlf3hv9L2oluOP1W7p3efTM+s+EfqjVmIQkkgjqzv3RKKcckE1clJzPlSwjlNvfoIZGdyI1tE3InAv2tF/Y2s1hvfOCVs8QskCAbKtTK730GKl26m5GZRhOHRpsQe///5IPPLOLkvmXRXNoj9+sg0PrigPe02v66TV1xjRvuixk22YUlKAsopaLAoKPgaY0W9QU27NKOtWnUPRc3fjyELV58Tq8mkErdddOzy8N/Je1KjjN6LPp9doqUToj1rNbfUOENmdDJ17onhEL6henz1mSC4t8mD57eOwcOYwPDZ1CBbOHIZls8dq7hDyoYJ1lNz82FHgIUDoMQZu5ssqag377kDgviDkOtQjK01VECHQ0RcRqV2Jtj+RBAdIvT4/tlTVY3V5DbZU1VvSXokcu5LAqZLPK6uoxfTFGzFnyQ48tHIP5izZgemLN8YtO3rvs1qXD+2Fv9+hf7stIhD8juU/Lx6AZ9dWxtxG63XS6muMkj6r1f0GNeXWjLJu1TkUPXcTB+Wr/g6ry6fetF53rbxuKyFaNrp1TVF0DTXy+I3o84m08yIPXZ3eH5UBMyGJNDJjjjoirZz4dNuI+fv4UME6Trv5CSbDU3U9F95Rks0YrV0J3p91++qwqrym01DD0Aw5WbJR9M4sFf28dZV1qjPZjZ7GQgkr510VmT/V6Ouk1dcYJX1Wq/sNasqtGWXdqnNoxv2G1eVTT1qvu0rfb8SQbdHPFCkbOenJePTdL4SvoUb3W4zq8+kxWsrJ/VFZMAhJpJFMnXuiaHhBFcOHCtZx0s1PKKtv5gP0DACVFnlw48hCTStvB/ZnVJ9c/GzigKg3W7JNJaH3lBDxPm/iwHxMX7wx5mfEuxk0choLO4kVjF9dXiP0GVquk1ZfY5T0WWXoN6gpt0aXdavOoRn3G1aXTz1pve4qeX9DS7vuD8mUPHgTKRsNLe1hf4t1DTW632Jkn0/pQ9fQYG9e1xTD9o3OYBCSSAfs3JPsnBzg0RMfKljHSTc/oWS4mTfCxEH5uq28HS1AKkMWaSR6ZpbG+7wtVfW63Azqvc92Fa2smXGdlOEaI9pnlaXfoKbcGlnWrTyHRt9vyFA+9aL1uqtkAbZI10EtD8nUPHiLVjYKMlNxut0XMQgZEOkaanS/RWmfT2mmqehD10jB3u4ZKchJT475m9m1PyoLBiGJdMLOPcnMyQEevfGhgjWcdPMTSpabeb2Z0a7IkkUaid5Di6N9np43g1YOh5ZR8I1tt64pplwnZbjGiPRZZeo3qCm3RpZ1K8+h0fcbMpRPPWi97oq+f1WcDGqlD8m0PHiLVDa8Pj/ufmtnzM+LdA01ut+ipM9n1HQs0YK9R0/EX4Xerv1RWTAISaQjdu5JVk4O8BiBDxWs4ZSbn1Ay3czryYx2xYhsDCPm7jKSU4PYVot0Y5udHvvWSK/rpAzXmHh9VvYbYtNyDrW2QUbfb8hQPrXSet0VeX9elxQcPxU7YKX0IZnWB2+hZUPtNBNm9FtE+nxGTcciEuzNSU9GWrLbUf1RWTAISUSUIJwa4DEKHypYwwk3P6GcfDNvdLuidwBOlgVulHBqENtK0W5sG78Zfhc6FM+I66QdrjHsN8Sm5hzapQ2yQ/mMRet1V+T9l5cUaJoXWcu2otupvYaa1W+J1eczcjoWkWBvQ0s7Xrj+PLi/mSPXCf1RWTAISUSUQJwY4CHnsfvNTyROvpk3sl3RMwAn2wI3opwcxLaCyI1tWrIbv7v+PBw/2Zbw10n2G/RjZRsUyL48fagRaT5fQpxDrdfdeO/PSU/WbV5kpduKbqflGmpWvyVan8/I6VhEg7jHTrZhSkmBos+m+FQHIXfs2IHnn38e27ZtQ3t7O4qLi3Hrrbdi6tSpQu9/++238eCDD0Z9/ZVXXsG4ceN0/14iokTnxAAPkR04+WbeqHZFrwCcrAvciHJyENtsIje2Nc2tSHK7ePP5DfYbtLOyDbJL9qURtF5342Xq6Z2lrnfmu9ZrqJX9FiMXx+E0J9ZSFYTcsGEDZs+ejdTUVEybNg0ZGRlYs2YN5s6diyNHjmDWrFnCn3XJJZegpKQk7O+FhYWGfi8RERGR2Xgzr5weATiZF7gR5eQgtpmculo9yc2qNsiuGeB60nrdjfZ+I7LUjfhMrddQq/otRgYKOc2JtRQHIdvb2/Hwww/D5XLhtdde6wgg3n333bj++uuxYMECTJkyJWIQMZJLL70UM2bMMP17iYiIiMgetAbgnBJ4YhBbOzMyYOy2+BEZT2kbpEcZsnsGuB0YkaVu1GcGrqE1Tadx/FQb8rqkICc9GV6fX8rzb2SgkNOcWEtxEHLDhg04ePAgZsyY0SmDMSsrC3PmzMH8+fOxdOlS3HPPPbruqFXfS0REJAve2FIi0xKA49ArCjA6A0amoa+RrhkAeB2xgJI2SK8ypDT7MlYfQ2v/w8n9FyOy1I34zCS3Cw0t7fjtv76Son2Kx+hAIac5sY7iIOSmTZsAABMmTAh7LfC3Tz/9VPjzdu/ejfr6erS3t6N3794YP3488vLyDP9eIiIiO5HpxpbIbjj0igKMvLGVaehrpGtGdnoyXECnlb95HTGHaBt0/GQbHlxRHvaamjKkJPsyVh8DgKb+R7z+ixMClEZkqev9mTK1T6KMDhRymhNrKA5C7t+/HwDQr1+/sNe6d++Orl274sCBA8Kf9+qrr3b6d3p6Ou6++27ccccdhn5vgMvB5StwbE4+RiKZsQ6SXsr2xu44/mr6OSgtlqvjKAvWQwKA5CQX7i8dhAdiBJ7umzQQyUksKEaQrR5eUuzBr6afg6cj3NjeN2mgqvZUdOjrxYOMH/oa7ZrRGBR8DDDzOuL1+bHtUANqT7TCk5GKEb0T52ZfpA2ae/EALPigMubnKClDotmXVfWnsGh9+H10oGxEIlpu4vVfbhndG6v31IQFKO8vHaS4PCZy+YpHpvZJqUuKPbh4UL5h5zY5yYXRfXN1+SwRsl0P9aLkeBQHIZubmwGcGQYdSWZmJpqamuJ+Tu/evfHwww9jwoQJ6NmzJxoaGvDJJ59gwYIFeOaZZ9ClSxfccsstun9vqPz8yJ/nJIlwjEQyYx0kLbw+P55dtynq6y4Az677Eted31+6jqNMWA9ppicL2dnp+O+/78bhhpaOv/fKSccvrzoHlw/tZeHeJQaZ6uFMTxauO78/Nn11DDVNLSjISsfYs7upbkc/qawTGvr6ZVMbxg/MV/Ud8Xh9fmyorMP//rNC0fvMuI68u+twwte9eG1QTpfYmZKAsjJ0WbdM9Fq9F0caWuCP8LoLQI/sNCzb9bXCI/n2/bHKTbz+CwC8uvlQ2N+OfhOg/P33RwqXDZav2GRon7S6vCDb6l3QlUzXQ7OpWh1bD2PHjsXYsWM7/p2eno5rrrkG5557Lq677jr89re/xY033ojkZGN3sa6uCf5IrbIDuFxnCreTj5FIZqyDpIfNB+s7dapD+QEcbmjBP7cfMvVJrl2wHlKw0T0z8c6PxkTMqKitVf4wm8TIXA+LclJRlHMmY+z4sWbVn1P573rh7QLfp6eyvbVhmZ2ijL6OlO2tjZgBeLihBXP+vNX0bH4rM+ZitUHPlMXOggxQUobmThwQNfvSD+DqoT0jZkGKiFdu4vVfYn0uAPxy2ecYUZAR99zIVr5kZHX7RN+S+XqoReC4RCiO8GVmZgJA1KzD5uZm5OSon0+nqKgIo0aNwvr161FZWYnBgwcb+r1+Pxx18iNJhGMkkhnrIGmhZE4nlrPoWA8pwO0Kn2eLZcMcTq6HngzBhUcyUnX/DaLN9aaUEdcRr8+Pp+MMA33mg0pcZNIKzTLMrxypDXp/by3e2Fot9H4lZWhSnDn12tp9orsdVbRyI9p/iebrptP4y9Zq5HdNjTpXn2zlS1ZWtk8UmZOvh/EoDkL2798fAHDgwAEMHTq002tHjx7FyZMnMWzYME07FViY5tSpU6Z+LxERkWy4qi8RkfysWvxIZK43UUZcR5Su0GwkMxbmULPIipJzqKYMBS++cdrtRprP17FfW6rqFX1WJNHKjR7l6dm1X3b8d6RgsUzlS2ZcnI1k4lb6hjFjxgAAPvroo7DXAn8LbKOG1+vFrl27AABnnXWWad9LREQko0DHMRZ2HImIrBVYdTsWtatuxyIShBFh1HVESTa/kUQX5vD61KcmlVXUYvrijZizZAceWrkHc5bswPTFG1FWURvzfUrOodoylOQ+s/jG1cMLMbpvbsdniPQxYolVbrR+dqhAsDj496xpOi30XqPLl+ysap+IIlEchBw/fjz69OmDFStWoLy8vOPvTU1NWLhwIVJSUnDNNdd0/L2mpgaVlZVhw6gDgcZgXq8XTz/9NA4cOIBx48ahoKBA9fcSERE5ATuORET2UPrN0NfQwEuPrDRdsuwi0Su4YtR1RJZsfiUZc2oEsixDvyNS4CyU6Dm8cWSh7mVIpI8RS6xyo/WzowkEi8sqauOuJh7A0SLWtE9O4/X5saWqHqvLa7Clql7TQ4tEpng4dnJyMh577DHMnj0bN998M6ZNm4aMjAysWbMG1dXVmDdvHnr37t2x/YIFC7B06VI88cQTmDFjRsffr7vuOgwePBiDBw9Gjx490NDQgE2bNmH//v3o2bMnHn/8cU3fS0RE5BSlceZ0YseRiEgOwUNfg4fkAsCWqnpFw3RFKAmu5KSfufVraGnv+JvR1xFZhoEamZEpmmU5Mcq8hKLncOIgY1YtjtfHAKC6/xHrsycP7h5xdex4vm46jZc3HhReUIejRb4VrX3ig+z4ZJhP1ilULT19/vnn4/XXX8dzzz2HlStXor29HcXFxbj//vsxdepUoc+YNWsWtm/fjvXr16OhoQEpKSno27cv7rrrLtx2220RF5nR43uJiIjsiB1HIiJ7SHJ3XnjEyJtXkSBfdnoynriypGOfzLyOBLLhYi2cY0Y2v2igr1vXFMXBYq3zEsoQqI3Xx9DS/4j12UPPyg6rGyJEF/EBOFokVGj7RPGZMZ9sInH5/Ym6Js8ZtbXOWho9mMsFeDxZjj5GIpmxDhJZj/WQyHqJXA/jrVytx82rGd+hVaRArJnZ/F6fH9MXb4wZ7MpJT0ZasltxsHh1eQ0eWrkn7j48NnUIppQURHzNjHMoaz0MXsyn7mRrp8VotMjrkoL5lxVZXvbJ3kTajh5ZaVg2e2zE1dtDg+/JSS4p66FWgfZFhKpMSCIiIiIiIopOZJjuM2X7og7TFWWHKTuszuYXycgMHqYeIJLppMe8l3Y4h0YJzszz+vx4bfOhuJm9jRHOVai5Fw9w9O9G5lCb6RwtA/7+0kGYKRiscyoGIYmIiIiIiHQmcvNa09yKlzcexOzx/TR9l9VBPhFWDwONFugryEzF6XZfxCBkQKw5HfUaTm2HcxgsUpaX1n0VCRbfMLIQLwrMB1mQlaZpX4gAdfPJxhq+/cDy3cjOTsfonpm67aPdMAhJRERERESkM9Gb10XrD2CAJ0Nz1pbVQT47iBTo8/r8uPutnTHfF2tORz3nvbTLOTRyntN4WaETB+bjnR2HLV/siBKD0kxnkQz4//77brzzozFwu+R8wGA0BiGJiIiIiIh0pmTl6liZdqSv0EDf6vIaoffFCion0nBqoxbpCM2sXPqjsdh5uDFipqUMix1RYlCa6SySAX+4oQXbDkV+qJEIGIQkIiIiIiLSmcjNa0CsTLtEZcRw30j0mNMRiJxleV6vbOw83IjV5TXSD68WIZLlpSagHiuzMtJiPokU9LUTs+qsmZRmOgsP3z6hbEV4J2EQkoiIiIiISGciN6/BRG9eE4GRw31D6TWnI9A5y7KsohbXvrTJlGMwi9pFOmJRm1lp5RyaTgy2aWVmnTWbkqC38EONDPFMeadhEJKIiIiIiMgApUUe3HFBP6GFNJQM33Yyo4b7RqPnnI4BIsdgp0VoAtQs0hEsNHh3Xq9sTZmVVsyh6eRgm1p611kZg7yiQW+Rhxq9ctIxonfizlnKICQREREREZFBZo3ry4U0BBk13DcePYf3ihzD/67Zi2eS3boGsswI3GgZuh4peJebnoz6GKuSA3JNVfDeF0fx4IrysL9HCrbJGEgzgt51VuYgr0jQW+Shxi+vOgdJbhf8fp130CYYhCQiIiIiIjKIEZl2TmXEcF9RoplO8YJLIsfQECHwpiXT06zAjdqh69Ey5eIFIANkmKrgvb1H8Yt/hAcggwWCbesq66QNpOlNzzprdha0UWI91Lhv0kBcPrQXamubLNxDazEISUREREREpFGs4BQX0hCjdbivVvEynUSCfVr3TWmmp5mBGzUBdZFMuXisnqqgrKIWD/49dgASOBNse3njQSyKMP2C3QJpovSqs1ZlQRsl2kON5CT5991oDEISERE5SKIM/yEikolIcMrKhTTsQq+Vqo0gGuzTum9KMj2VBG70Cn4oDaiLZMrFYvVUBUqDqG9srY75uiyBNL36i3rVWSuzoI1ixZyldsAgJBERkUPIPI8OEZFTKclE401pbHquVK0nJcE+kWOIRzS7TEngZnTfXNX7E0pJQF1rZqjVUxUoDaI22mCOSz37i3rVWauzoMk8bqt3gIiIiLQL3ASHdgIDN8FlFbUW7RkRkXOJBqe8vgRdgUChwHDfWJQGpbw+P7ZU1WN1eQ22VNWrOhdKgn0ixxCPaHbZun11QtsZEbgJBNSnlBRgVJ/cqOdE9Fhyu6R0+nePrDTDhi4rKRNG/HZWBtL07i/qVWdlzoImfTETkoiIyOacNo8OGYtD9on048QhhFbTc/5MvTK+lGZpRTuGgsxUnG73RVyYJkA007Osojbu0N8AKwM3oplyb88ag52HGw2/NiktE0b8dladD6P6i3rUWTOyoNn/kQODkERERDbHm2ASxSH7FIw3ZNpxCKEx9Jg/U88FW9RkaUU7hnWVdZpXSlcyT6HVcyqKLmaTmuw2vI+ipkzoMbw+mJXnw8j+otY6q2bRIyXY/5EHh2MTERHZHG+CSQSH7FOwsopaTF+8EXOW7MBDK/dgzpIdmL54I8uBQhxCaBzR4b6R6D1MPhCIiiVScCnSMQSyxkI/T8nwYyXzFFo9pyIAXY5ZK7VlQo/h9cGsPB9G9xe11FnAuHLC/o9cmAlJRERkc7wJpng4ZJ+C6ZkhluhkXUgl0emd8aV3lpbWrDHRINGNIwulqctWrw6vpUxEG24cS26XFNSfauv4t5rpBPQme3/R6/MjJz0Z91x4No6fakNelxQUfNN+qi0n7P/Ih0FIIiIim+NNMMXDIfsUwBsyfRk9hJDUMSLjS8+5KgFtK6WLBokmDspX9flGsXJ1eK1lIhBE3VpVj/krymOugm3mHJdKyNxfjDVcWsvvxv6PfDgcm4iIyOaMWE2UnIVD9ilAyQ0ZiZFhqCl1ZlTGV2mRB8tvH4eFM4fhsalDsHDmMCybPdb0c6x2eHgi06NMJLldGNMvD7+YXBzzM4LnuFQ7NNkIsvYXjRwuzf6PfJgJSURE5AB6Z2iQs8g+BIvMwxsyY1g91JQ6MzLjy8psvuB9YAauMnqWCdn6XEoWGdO673ovaGZ0dj77P/JhEJKIiMgheBNM0YjefJ3XKxtbqupZfhyMN2TGkSE4RWckQpBOtkCY7GSb1zOYlsCemlWf1e67EStMGz1cWuYh6ImKQUgiIiIH4U0wRSJy8zV5cHdc+9ImXW8uSD68IaNEkQhBOj58VEameT0DtAT24i0yduPIQkwclB+xTCjdd6MWNDNjxW6nP5CwG5ff7/fH38y5amub4NRfwOUCPJ4sRx8jkcxYB4msx3rYWaSbnR5ZaZg8uDte3Xwo6vs4r52zRLuZDND7fLMekpX0Hj5qV6yH35KlTGhpi70+P6Yv3ii0WrfWB4oi39UjKw3LZo9V/DtuqarHnCU74m63cOYwTQHfSP2fvC4puLykIGqg1ghOrYeB4xLBTEgiIiKiBBEpa+a8Xtm49qVNMd/H1ZKdJREyxIgCOEKAQomUCaMDlVrnQhQZxhygNVvRyCHTZmXnB/d/1u2rw6ryGhw/1YY3tlbjja3VHPlhIgYhiYiIiBJI6M3Xlqp6Q+djIjlxGCfZjSzZa+R8Rsx9GEprYE/N8GS1DxSNHDJt5nDpJLcLDS3teGNrddhrWgO1JI5BSCIiIqIExtWSExczxJzJicE6M4JCRIBxcx+GUnvtDdTvL+tOKP5OtQ8UjV7QzKzsfKNX4iYxDEISERERJTCulkzkHE4M1pkVFIrHicFd6szMIJWaa2+k+q2UmgeKZgyZNiM73+iVuEkMg5BERERECYyrJRM5gyzBOj3JkrnkxOCuiEQLvJoZpFJ67Y23iI0oNQ8UzRoyrXd2fmj5rWk6LfQ+jvwwFoOQRERERAnMzPmYiMgYsgTr9CZD5pITg7siEjHwaub0JEquvSL1W4SWB4p6DZk2K7AdqfzmpouFvzjyw1gMQhIRERElOK6WTGRvMgTrjGD1nLVODe7Gk6iBV7OnJxG99oquhH1JcT7e31sX9XUtDxS9Pj9y0pNxz4Vn4/ipNuR1SUHBN0FN0c80K7AdrfzWt7THfS9HfhiPQUgiIiIi4mrJlLCcMOTU6mCdUayes9apwd1YEjXwClgzPYnItVe03k4a1B2Th/TQ/YFirOChkgCkGYFtrVmjHPlhPAYhiYiIiAgAV0umxOOUIadWB+uMYvWctU4N7saSiIHXAKumJ4l37VVSv0f1ydX1gaKa4GHog53zemWbFtgWzRrN7ZKC+lNtHf8ODtQ64cGUzBiEJCIiIiKihOOkIadWB+uMYvWctQfrTwltZ7fgbiyiAdVNB447Mjgj4/QkSuu3Xg8U1WTFRpuLMd5QaL0C26Ll996LB6AgKy0s0OiUB1MyYxCSiIiIiIgSitOGnFodrDOSVUGhsopavLj+QNzt7BjcjUU0oPrHjVVY8fnXjgzOmDU9iWjGnVX1W2lWrJa5GAF9MopFy29BVlpYwFPJgylmS6rHICQRERERESUUJw45lTGDSy9mz1mrZF45uwZ3oxHJuguwY9awKKOnJ1GacWdF/VYyHYEeK3jrkVGsNitcyYOpdZV1zJbUgEFIIiIiIiJKKE6d68/JC0yZOWet6Lxyd17QTzjoYJfMKZGsu1B2yhqWgdqpIMyu30rmohStM9HolVGsNmtU9MHUyxsPYlGEDGklc2Re1i1TwRE5D4OQRERERERke0qCPKI311WCcwLKxIhgnbupGu6WY1Ff96V3gy+rUNfvtJJo8LlPbheh7ew2z1y0rLto7JY1bCWtU0GYGYxXklX43hdHNX2XnhnFarJGRev8G1urAQBnoRZ5rqaw1/9RVo1Lss+Fq2s+fFmFEet+r9V7MXfiAEySsO6bgUFIIiIiIiKyNaVBHtEhp4vWH8AAT4aUgSKzuJuq0e21i+Dyno66jT8pDcdu/tAxgUg9Vxu36wJIgay7xZ/sx0sbquJub7esYavYaSoIJVmFonUm1qrUelKaNSq6/40t7TgLtShLuw/prrbwDdoBvHWmTVxxwTuY98/jYZscaWjBAxLXfaO5rd4BIiIiIiIitQJBntAb+0CQp6yiNuw9gZtrEQs+qITX59dlX+3I3XIsZgASAFze0zEzJe0mEKSORWT4qGjWm6zlK8ntwpi+eULbOmmFcCPZbSqIQFZhaH3okZXWKYgmWmf+ccc4LJw5DI9NHYKFM4dh2eyxhgXiAlmjU0oKMKpPbsxMS5H9z04/k8OX52qKHIAM4vKexpL1OyO+FqjtMtd9IzEISUREREREtqQlyFNa5MEdF/SL+x2BrCRKHCJBapHho0qy3mSlV0CWztAzy9YspUUeLL89dvBQtM6kJruFA4NmEtn/G0Yqy/Q+djJ2oFL2um8UBiGJiIiIiMiWtAZ5+grO6SdLVpJTuZuqkXx0Z9T/uZuqTd8n0QywWOyW9RaJXgFZOsOuQV2RrEI96oyV4u3/rHF94547pWSu+0bhnJBERERERGRLWoM8dsxKchqZ55zUuhqxU8qXyEIfibZ4kVpqV2+2C7NW8DaqvMXb//tKB+GVv3+her9DyV73jcAgJBERERER2ZLWII+S1V/JGErmnLQiiKVlNWInla9YwRmZA8kyUrN6s1JWBoWNXsHb6PIWa/9LizzImzgA2BD/c7p1TQFORH/dLnVfbwxCEhERERGRLWkN8jg9K4ms5bTyFS04I3sgWUZGZgzaPSgcL4DqOlFjaXkb0zdXKAh5y5g++Nfa8PmIXTizOI2d6r6eGIQkIiIiIiJb0iPIY0ZWEiUuli8KCA2uJQMYlw4g/cy/fSdOhAXN1GQ02jkoLBRAdaeYuEfqjembi6em9wqr+z1z0jF34gBMStC6zyAkERERERHZlh5BHrPmMbMjX3o3+JPS4mZV+dK7mbhX9sLyRWqyE+2e0RiJLlmOvtirThtNSZsYqe5fNrw3jh9rhj88STIhMAhJRERERES2pkeQx+h5zOKRdWEPX1Yhjt38oZT7ZidWly+ylprsRDtnNEbilCxHpW1icN13uZDwDx8YhCQiIiIiItuzc5BH9ownX1ZhxO8NBE4D/wt7H4OTRPQNoaCqxVmOoqK1iRQfg5BEREREREQWsmPGk+yBUyKKzXWiBsnYGfV1PkQgIzAISURERERERIroFTjlnJNE1shZNTtm5iEfIpARGIQkIiIiIiIiS3DOSftjINme4g19li37WgmWN3kxCElERERERESW4fxq9mbnQLKsC0JpIRQUdqfYZv5FpRovex5tvcba7rwlCgYhiYiIiIiIKCE4MegkAzsGko2c1zS0nCUd36d6P5USCQq7TtQg9x8/NG2flNAaIPXmDbJdWUwkDEISERERERGR43ExHQpm1IJQIuUsktAhxFqGuccLCsdakMZqDVf8Af6MgrC/Jx3fh+x//sSCPSI9MQhJREREREREjifTKuTMyHQukXIGnBk27M0bBNeJGrhbG+FLzYa75VinctFw+YsAEDEoB9irnIgGVb35Q2xzTKQcg5BEREREREQW4sIeiYUZmQR8M2w4vRu6/e2ahCgLdp47lPTDICQREREREZGF7HhzzsCpejJlZJK1Eq0saJk7VOY2h5nN4hiEJCIiIiIispjdFvawY+CUiM6QOaAXjaxtjpLMZn8220MGIYmIiIiIiEgxuwVOiegMWQN68cjY5ijJZvUyCKkuCLljxw48//zz2LZtG9rb21FcXIxbb70VU6dOVbUTDQ0NuPLKK1FTU4MJEybgpZdeCttm8ODBUd9/7bXX4sknn1T13UREREREREREiUTGgB45n+Ig5IYNGzB79mykpqZi2rRpyMjIwJo1azB37lwcOXIEs2bNUrwT//M//4Pm5ua42xUWFuLaa68N+3tJSYni7yQiIiIiIiKixGTHIclEdqcoCNne3o6HH34YLpcLr732Wkfw7+6778b111+PBQsWYMqUKSgsFI+mr169GitWrMAjjzyC//mf/4m5bWFhIX7yk58o2WUiIiIiIiIiBp2oE6OGJCspZ7G+m77FhV+cQ1EQcsOGDTh48CBmzJjRKfswKysLc+bMwfz587F06VLcc889Qp937NgxPProo7j66qsxceJEZXtOREREREREJMiu8+CRcYwYkqyknDEIGZ+ShV9Yd+WnKAi5adMmAMCECRPCXgv87dNPPxX+vF/+8pdISkrCL37xCzQ1NcXdvrGxEW+++SaOHz+OnJwcjBw5MuZckUREREREREQBMsyDx4xM5xMtZywL8SlZ+MXquk3xKQpC7t+/HwDQr1+/sNe6d++Orl274sCBA0KftWzZMqxZswa/+93vkJOTIxSE3LNnDx555JFOf7vwwgvx1FNPIT8/X+h7Q7lcqt5mC4Fjc/IxEsmMdZDIeqyHRNZjPSTqzJ9diOPf/xCuU9Gz4PxdusGfVQi9qg3roZysKAtOJnv5dmo9VHI8ioKQgcVjsrKyIr6emZkpFEz8+uuv8fjjj+PKK6/EpZdeKvTds2bNwuTJk9G/f3+kpKSgoqICL7zwAj788EPceeedePPNN5GUlCR+MN/Iz498LE6SCMdIJDPWQSLrsR4SWY/1kCiIZ4glX8t6KCGLyoJttHYV2iwvtyvgsaB8J/cBktOA9hjZmslpyDurD5B7Zv8SuR4qXh1bDw899BCSk5Pxi1/8Qvg98+bN6/TvESNGYNGiRfjhD3+ITZs24f3338fkyZMV70tdXRP8fsVvswWX60zhdvIxEsmMdZDIeqyHRNZjPSSyHush2VVS/UnkCWx3vP4kvKnxk+L0lwv3zfGzWX3tuXDVNTmyHgbaFxGKgpCZmZkAEDXbsbm5GTk5OTE/Y+nSpfjwww/xm9/8Bt26aZvXwO1247vf/S42bdqErVu3qgpC+v1w1MmPJBGOkUhmrINE1mM9JLIe6yGR9VgPyamSju8L+5tZC015MwuBzDjfE1TvErkeKgpC9u/fHwBw4MABDB06tNNrR48excmTJzFs2LCYn7F7924AwM9+9rOIr3/00UcYPHgwhgwZgmXLlsXdp7y8MzHxkydPxt2WiIiIiIiIiIicJfufPwn7G1fNlo+iIOSYMWOwaNEifPTRR5g2bVqn1z766KOObWIZMWJExIDhyZMnsXLlSvTs2RMTJkxAr169hPbps88+AwD07t1baHsiIiIiIiIiInI2rpotH0VByPHjx///7d17kNV1/fjx11mW67KKqKAILGrDRScdUYksQahmENTAqKa8MqKDhWlTTTJNTk04kremsBEJU8uaATIWKxxRUBFB0CBt0EWrgXK9gIK4C4uycL5/+MVf/Fy5ffmc9/FzHo+/9JyP8DqzPl187ecSffr0iT//+c9x6aWXxqBBgyLi/cuzZ8yYEe3bt4+xY8d+cPyGDRuiqakpevTo8cHDbEaPHh2jR4/+0K/9yiuvxIIFC+ITn/hE3HjjjXu8t3bt2jjhhBOiffv2e7y+atWqmDVrVrRv3z5GjRp1IB8FAAAAoOxUNTVG1faPvsdgqS4z/r86FJ9jV6fuUWzXMQo79/LgFz42DmgJWV1dHVOnTo2JEyfGRRddFGPGjImamppYuHBhNDY2xve///09zki8/fbbY968eXHTTTfFhRdeeNBD3nPPPfH444/H6aefHscee2xUV1fHyy+/HE899VQUCoW44YYbom/fvgf96wMAAACkVtXUGN1/N2yvS7ePw2XGh+pz7Ko9LjZdtORDy8x2m//R5iXYlLcDfjr20KFD4/e//3384he/iAULFkRra2v0798/vvvd77Z5huOh8LnPfS7eeeedaGhoiGXLlsWOHTviqKOOijFjxsRll122z/tQAgAAAJS7qu2b9nnWXxaXGR/qsy8P5efYVXtcJgvXvJxx+nFywEvIiIhTTjklZs2atc/jpk2bFtOmTduvX7N3796xdu3aNt/7whe+EF/4whcOaEYAAAAA9i4vZ18eiEr8zOWgKvUAAAAAAKRxIGct5kUlfuZyYAkJAAAAAGTKEhIAAACAj43dT83em2K7jrGrU/cSTcT+OKh7QgIAAACVzYM9SOWjnpq9xzH+/Ss7lpAAAADAAfFgD1LL6qnZ/63d5n+0/XtbcB4US0gAAADggBzIgz0sa/bf7suM97XcLffLjPPyOQ575Jo2X7dgPziWkAAAAABlIC+XGeflc3wUC/aDYwkJAAAAUCZKcZnxHr9fRmctlvpzHIj9+cwcepaQAAAAABUq72cttmVvn7nd5n985GXY/N9YQgIAAABUsHI+azErlfiZU6tKPQAAAAAAkG/OhAQAAICPuaqmxr1eThvVfSKiW6nGAfgQS0gAAAD4GKtqaozuvxu294dsVHeMqouWxM6uh+by06weZgLklyUkAAAAfIxVbd+076f8tr4bhZZNEYdqCVmBDzOhMliwZ8cSEgAAADhgHuxBHlmwZ8cSEgAAAAD+lwV7NjwdGwAAAADIlCUkAAAAAJApl2MDAAAAkBtVTY3u6ViGLCEBAAAAyIWqpsbo/rth+3y69aaLllhElpjLsQEAAOBjbFen7lFs13HvB1V3jGLn7qUZCBKq2r5prwvIiIjCznf3eqYk2XAmJAAAAHyM7ao9LjZdtGSvS5UjevWJXa3dIoqlmwvgv1lCAgAAwMfcrtrjPvLS0kIhIrrVRrzZVNqhAP6LJSQAAAAA+82DXzgYlpAAAAAA7BcPfuFgeTANAAAAAPvFg184WJaQAAAAAECmLCEBAAAAyIVdnbpHsV3HvR5TbNcxdnXqXqKJ2M09IQEAAADIhV21x8Wmi5Z4cE4ZsoQEAAAAIDd21R5nyViGXI4NAAAAAGTKEhIAAAAAyJQlJAAAAAD7xYNfOFjuCQkAAADAfvHgFw6WJSQAAAAA+82DXzgYLscGAAAAADJlCQkAAAAAZMoSEgAAAADIlCUkAAAAAJApS0gAAAAAIFOWkAAAAABApiwhAQAAAIBMWUICAAAAAJmyhAQAAAAAMmUJCQAAAABkyhISAAAAAMiUJSQAAAAAkClLSAAAAAAgU9WpB0itUEg9QXZ2f7Y8f0YoZxqE9HQI6ekQ0tMhpJfXDg/k8xSKxWIxu1EAAAAAgErncmwAAAAAIFOWkAAAAABApiwhAQAAAIBMWUICAAAAAJmyhAQAAAAAMmUJCQAAAABkyhISAAAAAMiUJSQAAAAAkClLSAAAAAAgU5aQAAAAAECmLCEBAAAAgExVpx6AQ2/lypX7feyQIUMynAQqlw4hPR1CejqEtDQIlJNCsVgsph6CQ2vQoEGxv1/WhoaGjKeByqRDSE+HkJ4OIS0NAuXEmZA5NHny5Da/0WzdujUaGhri6aefjs9+9rNx6qmnJpgOKoMOIT0dQno6hLQ0CJQTZ0JWoMceeyyuu+66uPfee+O0005LPQ5UJB1CejqE9HQIaWkQKCVLyAo1efLk2LJlS/z2t79NPQpULB1CejqE9HQIaWkQKBVPx65Qffv2jTVr1qQeAyqaDiE9HUJ6OoS0NAiUiiVkBWptbY0VK1ZEx44dU48CFUuHkJ4OIT0dQloaBErJg2lyaN68eW2+3traGhs3boyHH3441q5dG1/72tdKPBlUDh1CejqE9HQIaWkQKCfuCZlDgwYNimKxGIVCYY/Xd3+pC4VCnHfeeXHjjTdGhw4dUowIuadDSE+HkJ4OIS0NAuXEmZA5dNNNN7X5enNzczQ0NMTixYtjxIgRvslAhnQI6ekQ0tMhpKVBoJxYQubQ2LFj9/r+mjVr4utf/3p06dIlzjnnnJLMBJVGh5CeDiE9HUJaGgTKiQfTVKCTTz45Pv/5z8eMGTNSjwIVS4eQng4hPR1CWhoESskSskL16NEj1q5dm3oMqGg6hPR0COnpENLSIFAqlpAVaOvWrfH4449Hly5dUo8CFUuHkJ4OIT0dQloaBErJPSFz6I477mjz9dbW1ti4cWM8/vjj8dZbb8UVV1xR4smgcugQ0tMhpKdDSEuDQDkpFIvFYuohOLQGDRq01/cPP/zwuPTSS2PSpElRVeVkWMiCDiE9HUJ6OoS0NAiUE0vIHHrmmWfafL26ujp69OgRxxxzTLRr167EU0Fl0SGkp0NIT4eQlgaBcmIJCQAAAABkyj0hc6y+vj7q6+vjxRdfjObm5ujatWsMHDgwxo0bF2PHjk09HlQEHUJ6OoT0dAhpaRAoB86EzKFdu3bFNddcE4sWLYpCoRCdOnWKI488Mt56663Yvn17FIvFGDlyZEyfPt2p95ARHUJ6OoT0dAhpaRAoJ+48m0O/+c1vYvHixfHpT3865syZE6tXr45HH300Vq9eHXPnzo1PfepTsXjx4vjNb36TelTILR1CejqE9HQIaWkQKCfOhMyhL37xi7Fjx4548MEHo7r6w1fct7a2xvnnnx8dOnSI+fPnJ5gQ8k+HkJ4OIT0dQloaBMqJMyFzaN26dXHOOee0+U0m4v0noY0YMSLWrVtX2sGggugQ0tMhpKdDSEuDQDmxhMyh9u3bx7Zt2/Z6TEtLS7Rv375EE0Hl0SGkp0NIT4eQlgaBcmIJmUMnn3xyPPTQQ/Haa6+1+f7rr78eCxYsiJNOOqnEk0Hl0CGkp0NIT4eQlgaBcmIJmUMTJkyId955Jy688MKYOXNmPPfcc9HY2Bh/+9vf4q677opx48bFli1bYsKECalHhdzSIaSnQ0hPh5CWBoFy4sE0OXXffffFLbfcEq2trXu8XigUol27dvG9730vLrvsskTTQWXQIaSnQ0hPh5CWBoFyYQmZY6+88kr86U9/ihdffDG2bt0aNTU1MWjQoDj//POjd+/eqceDiqBDSE+HkJ4OIS0NAuXAEhIAAAAAyJR7QgIAAAAAmapOPQDZKBaLMW/evJg/f340NDREc3NzdO3aNQYMGBBjx46NcePGRaFQSD0m5JoOIT0dQno6hLQ0CJQLl2Pn0LvvvhtXX311LFu2LAqFQnTu3Dm6d+8emzZtipaWligWi/HpT386ZsyYER07dkw9LuSSDiE9HUJ6OoS0NAiUE5dj59Add9wRy5cvj2HDhsUf//jHWLVqVTz66KOxatWqmDdvXgwfPjyWL18e06dPTz0q5JYOIT0dQno6hLQ0CJQTZ0Lm0DnnnBOHHXZY1NfXR1XVh/fMxWIxxo4dG1u2bInHH3+89ANCBdAhpKdDSE+HkJYGgXLiTMgc2rx5cwwbNqzNbzIREYVCIYYNGxabNm0q8WRQOXQI6ekQ0tMhpKVBoJxYQuZQ37594+23397rMW+//XbU1dWVZiCoQDqE9HQI6ekQ0tIgUE4sIXPosssui7/85S+xdu3aNt9/+eWX4y9/+UtceumlJZ4MKocOIT0dQno6hLQ0CJST6tQDcOj17ds3hg4dGuPHj48LLrggzjzzzDjyyCPjrbfeimeffTbmz58fn/nMZ6Kuri5Wrly5xz87ZMiQRFNDvugQ0tMhpKdDSEuDQDnxYJocGjRoUBSLxSgUChHx/s2Gd9v92v//+m4NDQ3ZDwgVQIeQng4hPR1CWhoEyokzIXNo8uTJbX4TAUpHh5CeDiE9HUJaGgTKiTMhAQAAAIBMeTANAAAAAJApl2Pn2HvvvReLFi2K559/Ppqbm+Pwww+PU089NUaMGBHV1b70UAo6hPR0COnpENLSIFAO/Ncmp/7+97/HtddeG6+++moUCoUPbkZcLBajV69e8bOf/SxOPfXU1GNCrukQ0tMhpKdDSEuDQLlwT8gc2rBhQ3zxi1+MlpaWuPzyy+O0006LSZMmxXnnnRcnnHBC3H333RERUV9fH7179048LeSTDiE9HUJ6OoS0NAiUE/eEzKG77747Nm/eHLNmzYrrrrsuhg8fHhERdXV1cfXVV8f9998f27dvjzvvvDPxpJBfOoT0dAjp6RDS0iBQTiwhc+iJJ56IoUOHxhlnnNHm+wMHDowRI0bEsmXLSjwZVA4dQno6hPR0CGlpECgnlpA59Nprr0X//v33ekxdXV28+eabJZoIKo8OIT0dQno6hLQ0CJQTS8gc6tChQxQKhb0e09jYGIcffniJJoLKo0NIT4eQng4hLQ0C5cQSMod69uwZr7766ke+v2rVqnjkkUfi7LPPLuFUUFl0COnpENLTIaSlQaCcVKcegENv8ODB8cgjj0Rra2tUV/+/L/FTTz0VL730UixatCi6desW11xzTcIpId90COnpENLTIaSlQaCcOBMyh8aMGRNHHHFEPPfccx+8VigU4m9/+1ssWrQohg0bFrNnz45evXolnBLyTYeQng4hPR1CWhoEykmhWCwWUw9B9p599tno3Llz9OvXL2pqalKPAxVJh5CeDiE9HUJaGgRSsYQEAAAAADLlcmwAAAAAIFMeTJNDI0eOPOh/dvHixYdwEqhcOoT0dAjp6RDS0iBQTiwhc6iqqipcZQ9p6RDS0yGkp0NIS4NAOXFPSAAAAAAgU+4JCQAAAABkyhISAAAAAMiUe0LmUGNjY0RE9OzZM6qrfYkhBR1CejqE9HQIaWkQKCfuCZlDgwYNimKxGAsWLIgTTjgh9ThQkXQI6ekQ0tMhpKVBoJz4UUgOHXfccVEsFqN9+/apR4GKpUNIT4eQng4hLQ0C5cSZkAAAAABApjyYBgAAAADIlCUkAAAA5Ny7774bTU1NsXPnztSjABXKPSFzbOfOnbF8+fJ45plnYt26ddHc3BwREV27do1+/frFkCFDYujQodGuXbvEkwLAoTdlypTo1atXTJ48OQqFwl6PffTRR+O5556Liy++OHr27FmiCSH/1qxZE4899lice+65ceKJJ+7z+M2bN0dNTU106NChBNNBZZk6dWrMnTs36urqYtq0aXHaaaelHgmoMM6EzKkVK1bEqFGjYuLEiTFz5sxYuHBhLFu2LJYtWxYLFy6MmTNnxhVXXBGjRo2Kp59+OvW4kDtTpkyJuXPn7texy5cvj5EjR8aDDz6Y8VRQWerr6+OXv/xlTJkyJfZ1C+xu3brFr371q3jooYdKNB1UhtmzZ8c999wTxx133D6PbW1tjREjRsRtt91WgsmgMhUKhaipqYmLLroobrnllnjvvfdSjwRUEEvIHPr73/8eEydOjA0bNsSXvvSluO222+KBBx6IhQsXxsKFC+OBBx6I2267LcaPHx8bNmyIiRMnxvPPP596bMiV+vr6WLVq1X4d269fv3jttdfi2WefzXgqqDw1NTVRX18f119//V4XkWeccUbU1dXFk08+WcLpIP9Wr14dp59+enTq1OlD723bti127Njxwd9XV1fHJz/5yVizZk0pR4SKUiwWY+7cufGtb30rfvvb38aFF16oOaBkLCFzaPr06dGhQ4eYPXt23HjjjTF69Og46aSTok+fPtGnT5846aSTYvTo0TF16tSYM2dOdOrUKaZPn556bKhYxx57bBxxxBH+AAgZuOSSS2L06NExf/78fS4ihwwZEv/6179KOB3k34YNGz7yLMjBgwfHjBkz9njt+OOPj/Xr15diNKhY7dq1i0mTJsW8efOiS5cu8dWvfjWmT58era2tqUcDcs49IXNo9erVMWbMmBg4cOA+jx0wYECMHj3a5WeQgbVr18bMmTP369jOnTvHSy+9lPFEUHmqq6vj1ltvjUKhEPPnz49CoRA33XRTm/eI7N69e2zcuDHBlJBfW7duja5du7b5XlsdHnHEEbFly5asxwIi4sQTT4zZs2fHrFmz4o477ojHHnsspk2bFv379089GpBTlpA51NraGjU1Nft9fE1NjZ96QQZeeOGFePHFF/f7+H3dsw44OFVVVXHrrbdGVVVV1NfXx44dO+KnP/1pVFfv+cegdevWRbdu3dIMCTlVW1sbb7zxxode3/3a22+/vcfr77zzThx22GGlGA2I938YcOWVV8bIkSPjBz/4QYwfPz4mT54cV111VerRgByyhMyh/v37x8MPPxzf+MY3ora2dq/HNjc3x8KFC/20CzIwePDg+MpXvpJ6DCDe/5+sm2++Odq1axfz58+PN954I26++ebo1atXRET89a9/jUWLFsXIkSMTTwr5MmDAgFiyZEls3759j/tCPvbYY1EoFGL58uVRLBajUCjErl27YsmSJdGnT5+EE0O+TJky5YO/3n2/8uuvv77NM5H79u0bL730Utx+++2WkEAmLCFzaMKECfHtb387xo8fH5MmTYqzzz47jjrqqD2OefPNN2Pp0qUxY8aMaGxsjO9+97uJpoX8qquri7Fjx6YeA/hfhUIhpk2bFh07dow5c+bEqFGj4qyzzoodO3bEihUrYteuXTFx4sTUY0KujBkzJlasWBGTJ0+OKVOmRM+ePWPp0qVx++23x4QJE+LXv/51XHXVVTFs2LB45JFHorGxMb75zW+mHhtyo76+fo+/3317EoAULCFzaNSoUfH666/H7bff/sFPvrp06fLB/Xiam5tj27ZtERHRoUOHuP766+Pcc89NNi8AlNKPf/zjOOOMM+LWW2+NJ554IiIiDjvssPjRj34Up5xySuLpIF++9KUvxYMPPhhPPfVUjBkz5oPXBw8eHN/5zndiy5Yt8cADD8STTz4ZhUIhPvvZz8YFF1yQcGLIl9mzZ3/w13fddVcsWrQo5syZk3AioJIVim5ClluvvvpqzJ8/P1auXBnr16+PpqamiHj/3jx1dXVx5plnxrhx4+LYY49NPCnkz5QpU2Lw4MHx5S9/OfUoULHq6+tj4MCBH/mgtmKxGGvXro2dO3dG//79o3379iWeECrDe++9F/fee2888cQT0draGkOGDIlJkyZ9cA/zp59+Ol5++eXo27dvDB8+PPG0kF833HBDzJkzJxoaGlKPAlQoS0gAAADIuYaGhmhsbIzPfe5zqUcBKpQlJAAAAACQKfeEBCiBdevWtXlbhH79+qUdDCqIDiE9HUJaGgRSsoQEyEhLS0vMmjUr5s6dGxs2bGjzmB49esT48ePjyiuvjM6dO5d4Qsg/HUJ6OoS0NAiUC5djA2Sgubk5Lr744mhoaIiampoYPHhw1NXVRW1tbURENDU1xfr162PVqlWxdevWGDhwYNx///0fPMUe+L/TIaSnQ0hLg0A5cSYkQAbuuOOOWLt2bVx11VXxjW98Izp16tTmcdu3b48777wzZs6cGdOnT48pU6aUeFLILx1CejqEtDQIlBNnQgJkYOTIkXH88cfH3XffvV/HX3nllfHPf/4zFi9enPFkUDl0COnpENLSIFBOqlIPAJBHGzdujJNPPnm/jx80aFBs3Lgxw4mg8ugQ0tMhpKVBoJxYQgJk4Oijj441a9bs9/EvvPBCHH300RlOBJVHh5CeDiEtDQLlxBISIAPnnntuPPXUU3HrrbdGS0vLRx7X0tISt912WyxdujRGjRpVwgkh/3QI6ekQ0tIgUE7cExIgA9u2bYsJEybE888/H507d47BgwdHv379oqamJiIitm7dGuvWrYtVq1bFtm3b4pRTTol77703unTpknhyyA8dQno6hLQ0CJQTS0iAjLz33ntx3333xZw5c+KVV15p85g+ffrE+PHj4/LLL48OHTqUeELIPx1CejqEtDQIlAtLSIASeOONN2LdunXR1NQUERG1tbXRr1+/6NmzZ+LJoHLoENLTIaSlQSAlS0gAAAAAIFPVqQcAqBQtLS17/NS5c+fOiSeCyqNDSE+HkJYGgVQsIQEytGLFipgzZ06sXLkyNm7cuMd7Rx99dAwZMiS+/OUvx9ChQxNNCPmnQ0hPh5CWBoFy4HJsgIz88Ic/jLlz50ahUIjOnTtH7969o7a2NiIimpqa4pVXXomWlpYoFosxfvz4+MlPfhKFQiHx1JAvOoT0dAhpaRAoF86EBMjA/fffH3/4wx/izDPPjGuvvTZOP/30D/1hrlgsxqpVq+LnP/95/OEPf4gBAwbEJZdckmhiyB8dQno6hLQ0CJQTZ0ICZOD888+PiIg//vGP0b59+70e29raGuPGjYuIiD/96U+ZzwaVQoeQng4hLQ0C5aQq9QAAefTvf/87hg8fvs8/7EVEVFdXxznnnBP//ve/SzAZVA4dQno6hLQ0CJQTS0iADHTp0iU2bNiw38dv2LAhunTpkuFEUHl0COnpENLSIFBOLCEBMnDWWWfFQw89FE888cQ+j12yZEksWLAgzjrrrBJMBpVDh5CeDiEtDQLlxD0hATLwn//8J77yla/E5s2b48wzz4yzzz47+vXrF127do2IiObm5li3bl0sXbo0Vq5cGd26dYu5c+dGnz59Ek8O+aFDSE+HkJYGgXJiCQmQkfXr18fUqVNj6dKlEfH+kwf/2+4nE37mM5+JG264Ifr27VvyGSHvdAjp6RDS0iBQLiwhATL22muvxTPPPBPr16+PpqamiIiora2Nurq6GDJkSBxzzDGJJ4T80yGkp0NIS4NAapaQAAAAAECmPJgGAAAAAMiUJSQAAAAAkClLSAAAAAAgU5aQAAAAAECmLCEBAAAAgExZQgIAAAAAmbKEBAAAAAAyZQkJAAAAAGTKEhIAAAAAyJQlJAAAAACQqf8BYuZ6HQAgf9oAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -573,10 +573,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:36:26.022785Z",
-     "iopub.status.busy": "2024-11-04T11:36:26.022284Z",
-     "iopub.status.idle": "2024-11-04T11:36:26.141614Z",
-     "shell.execute_reply": "2024-11-04T11:36:26.140873Z"
+     "iopub.execute_input": "2024-11-04T12:32:20.809902Z",
+     "iopub.status.busy": "2024-11-04T12:32:20.809615Z",
+     "iopub.status.idle": "2024-11-04T12:32:20.930379Z",
+     "shell.execute_reply": "2024-11-04T12:32:20.929587Z"
     }
    },
    "outputs": [
@@ -587,35 +587,35 @@
       "MCS P-values\n",
       "            Pvalue\n",
       "Model name        \n",
-      "model.20     0.000\n",
-      "model.60     0.000\n",
-      "model.0      0.000\n",
-      "model.180    0.002\n",
-      "model.140    0.005\n",
-      "model.100    0.016\n",
-      "model.80     0.038\n",
-      "model.40     0.057\n",
-      "model.160    0.061\n",
-      "model.280    0.061\n",
-      "model.220    0.073\n",
-      "model.320    0.139\n",
-      "model.120    0.139\n",
-      "model.300    0.139\n",
-      "model.200    0.152\n",
-      "model.260    0.152\n",
-      "model.360    0.213\n",
-      "model.380    0.275\n",
-      "model.460    0.525\n",
-      "model.340    0.595\n",
-      "model.240    0.780\n",
-      "model.400    0.780\n",
-      "model.480    0.780\n",
-      "model.440    0.780\n",
-      "model.420    1.000\n",
+      "model.40     0.000\n",
+      "model.160    0.002\n",
+      "model.360    0.007\n",
+      "model.20     0.015\n",
+      "model.180    0.017\n",
+      "model.60     0.026\n",
+      "model.200    0.031\n",
+      "model.220    0.039\n",
+      "model.100    0.049\n",
+      "model.80     0.050\n",
+      "model.120    0.110\n",
+      "model.140    0.195\n",
+      "model.0      0.277\n",
+      "model.440    0.590\n",
+      "model.300    0.766\n",
+      "model.240    0.774\n",
+      "model.400    0.774\n",
+      "model.260    0.774\n",
+      "model.280    0.774\n",
+      "model.320    0.774\n",
+      "model.340    0.835\n",
+      "model.420    0.835\n",
+      "model.380    0.835\n",
+      "model.480    0.976\n",
+      "model.460    1.000\n",
       "Included\n",
-      "['120', '200', '240', '260', '300', '320', '340', '360', '380', '400', '420', '440', '460', '480']\n",
+      "['0', '120', '140', '240', '260', '280', '300', '320', '340', '380', '400', '420', '440', '460', '480']\n",
       "Excluded\n",
-      "['0', '100', '140', '160', '180', '20', '220', '280', '40', '60', '80']\n"
+      "['100', '160', '180', '20', '200', '220', '360', '40', '60', '80']\n"
      ]
     }
    ],
@@ -641,16 +641,16 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:36:26.144124Z",
-     "iopub.status.busy": "2024-11-04T11:36:26.143555Z",
-     "iopub.status.idle": "2024-11-04T11:36:26.292992Z",
-     "shell.execute_reply": "2024-11-04T11:36:26.292233Z"
+     "iopub.execute_input": "2024-11-04T12:32:20.932904Z",
+     "iopub.status.busy": "2024-11-04T12:32:20.932671Z",
+     "iopub.status.idle": "2024-11-04T12:32:21.104232Z",
+     "shell.execute_reply": "2024-11-04T12:32:21.103446Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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LctVVP8y8eXdk+fLX8o537JBPfvLkdX7veosQkj5hfM3wjKoctM4l2VtWDc74muE9WBUAAAD0X+Urlq4zgEySsuaVKV+xtM+FkK2uuOLSzJ//UPbbb//stde+ufPOO3LNNf+W1atX57TTzmg39/LLv59f/nJmNt10ePbf/8CMGFGdRYteygMP3JexY8etM4TcEA88cF/OOeeLKS8vz8SJH8zIkVvkwQfvy+mnfzpVVZt2mF8oFHL++V/PH//4h4wevW0++MFDM3DgwNx//5/yne98O88883Q+//l/bJu/YsWKfOELp2Xhwqfy7ne/N+PH75ZFi17KP//zV7PXXvt06710ByEkfUJFeVnOnrhjpt30+FrnnHXwDqkoL+vBqgAAAIC+7Mknn8xPf/qLjBw5MkkyZcrUnHDC0fn1r3+VU045NQMHDkyS3H33nfnlL2dmhx12zGWXXZnhwzdrO0dTU1NefbV796BoaWnJd797YZqbm3PppVdm113HJ3kjaPzWt87Lf/7nrR0+87vf/TZ//OMfMmnSR/OVr3wtAwa8EdutXr06X//6V/KLX/w8hxxyaN71rnFJkpkzf5aFC5/KRz96dKZN+1rbeQ49dFLOPrvvbeJjYxr6jIk7jczFR+ycUW9Zcr1l1eBcfMTOmbjTyF6qDAAAAOiLpkz5dFsAmSSbbbZZ9t//wCxf/lqee+7ZtvFZs25IknzpS+e0CyCTZMCAAamu3rxb63r00fl5/vm67LffhLYAMknKyspy2mlnpKKiosNnfv3rX2WTTTbJ2WdPawsgk2TgwIE59dQ3nur84x//0DZ+662zM3DgwEyd2n55+N5775vdd9+rW++nO3gSkj5l4k4jc+AOm2d+3bLUN67KyMpBGV8z3BOQAAAAQAdjx47rMLbFFqOSJI2NDW1jTzzx5wwaNCjjx+/WI3U99dRfkyS77vq+Dse22mrrjBq1ZV544fm2sRUrVuRvf3sqI0dukZ///GcdPtPU1JQkee65Z5Ikr73WmBdeqMv2278zm2/e8aGtXXcdnwcfvK87bqXbCCHpcyrKy7L7mM16uwwAAACgjxs2bFiHsdanDJubm9vGGhsbs8UWo9a4WU0xNDY2JklGjKhe4/ERI6rbhZANDa+mUChk8eJFmTHj6rWe9/XXX0+SvPbaa//vPCPWOK+6es3X7U1CSAAAAAD6taqqqixduiQtLS0bFES2fubNwWar1sDxzSorK5MkL7+8dI3ne+t4a5g6duy4/OQn1623ntb5L7/88hqPL1265uv2Ju+EBAAAAKBfGzdul6xatSrz5z+0QZ9v3c26vn5xh2MLFjzZYWzHHWuTJI888nCHYy+++EIWLXqp3djQocOy/fbvyLPPPp2GhoYOn3mrYcMqs/XWNamr+3uWLKnvcPyRR+av9xw9TQgJAAAAsBFqGVKdQsXgdc4pVAxOy5C+t7S3q4455vgkyaWXfq/DTthNTU1ZunTJOj+/7bbbZejQYbnrrnntPr906ZL87Gc/6TD/ve8dn623rsk999zVLhAsFAq56qofrvGJyuOO+3hWrFiRiy++oG3Z9Zs9/3xduyXcH/7wpKxevTo//vFV7ebdd9+9fe59kInl2AAAAAAbpZaqmiydPC/lK9a+dLdlSHVaqmp6sKri2HffCfnEJz6Zf//363LCCcfkgAMOyogR1Vm8eFEefPD+fOITJ+VjHztxrZ8fOHBgjj32Y7nuuhk55ZSTMmHCGztw33PPnRk/frfU1f1vu/nl5eX5ylf+KV/+8pdy5pmfy8SJH8zIkVvkwQfvz5Il9dlhh52ycOGCdp858shj8+c/P5Zbbrk5//M/j2SPPfbKyJFb5OWXl+bZZ5/J448/ln/+5wuy9dbbJElOPPEf8l//NTe/+92sPPPMwuy6625ZtOilzJ37n9lvvwm55567uv8b+TYIIQEAAAA2Ui1VNf0iZOyMM874Ut797vfk17/+Ve6447asWrUqm28+Mrvttkf22GPv9X7+M585PQMHDszNN9+YG2/8dbbaaut86lNT8/7375877pjbYf6ee+6dSy/9Ua6++ke5/fY/ZvDgIdl99z3z7W9fnAsu+EaH+WVlZfna176ZffZ5f373u1m555678vrryzNiRHVGjx6TM874Urs6N9lkk/zgB/+WK6/8QebNuyNPPvlk3vGOd+b88y/Ka6819rkQsqxQKBR6u4jeVF/fkP76HSgrS0aOrOrX9wilTp9CadCrUBr0KpQGvdo1q1evypIlL2TzzbfOwIGDerscNiKrV6/Kyy+/lOrqLTNgwJp/9lr7uTO8ExIAAAAAKCohJAAAAABQVN4JCQAUXXNLIfPrlqW+cVVGVg7K+JrhqSgv6+2yAACAHiKEBACKau6C+lwy96ksalzVNjaqclDOnrhjJu40shcrAwAAeorl2ABA0cxdUJ9pNz3eLoBMkkWNqzLtpsczd0F9L1UGAAD0pA16EvLRRx/N5ZdfnocffjhNTU2pra3NlClTMmnSpC6dZ8mSJbnqqqtyxx135IUXXsjQoUOz/fbb58gjj8yJJ57Ybu7YsWPXep6jjz463/nOdzbkVgCAImluKeSSuU+tc8702xfmwB02tzQbAAD6uS6HkPfee2+mTp2aQYMG5bDDDsuwYcMyZ86cnHnmmXnxxRdzyimndOo8TzzxRE455ZS8+uqrOfDAA3PooYdm+fLlWbhwYW6//fYOIWSS1NTU5Oijj+4wPm7cuK7eBgBQZPPrlnV4AvKtXmpYmfl1y7L7mM16pigAgJJV6O0C2Oh0789cl0LIpqamnHfeeSkrK8vMmTPbwr8zzjgjxx13XKZPn55DDz00NTU16zxPY2NjPve5zyVJfv3rX+dd73pXh+usSU1NTb7whS90pWQAoJfUryeA7Oo8AICNUXn5G2/Sa25uzsCBvVwMG5Xm5jfyudafwberS2e5995789xzz+Xwww9v9/RhVVVVPvvZz2b16tWZNWvWes9z/fXX5/nnn8/ZZ5/dIYBMkgED7JcDAKVuZOWgbp0HALAxqqgYkAEDBmX58sYUCp6GpGcUCoUsX/5aNtlkSCoquien69JZ7rvvviTJhAkTOhxrHbv//vvXe57Zs2enrKwshx56aP72t7/l7rvvzooVK/LOd74z+++/fwYNWvN/jLz66qv55S9/mZdffjnDhw/Pbrvtts53RQIAvWd8zfCMqhy0ziXZW1YNzvia4T1YFQBA6Rk2bNMsW1afl19enKFDh/2/UMg7tSmGQpqbm7J8+WtZter1bL31tlnVTQuXuhRCPvPMM0mS7bbbrsOxLbbYIkOHDs2zzz67znOsWrUqf/3rX1NdXZ3rrrsul19+eVpaWtqOjxkzJj/84Q/XGC7+5S9/yTe+8Y12Y/vvv38uvvjibL755l25lTZl/bhnW++tP98jlDp9Sn82oKIs50zcMV+56fG1zjn74B0yoKLvN4BehdKgV6E06NWuGzp0WMrKksbGV/PKK/W9XQ4bgQEDBmXEiJHZdNNNs2RJw1rndaWPuxRCNjY2Jnlj+fWaVFZWpqFh7YUlybJly9Lc3JxXXnklV1xxRb785S/nyCOPTFNTU37xi1/kRz/6UU4//fTccsstGTx4cNvnTjnllHzoQx/K9ttvn4EDB2bBggW54oorMm/evJx22mn55S9/mYqKiq7cTpJk883XfC/9ycZwj1Dq9Cn91cdGVmXTTYfk/N89nheWrWgb33r4kPzzR3fOh9+9dS9W13V6FUqDXoXSoFe7qirJVlm9enWam5t7uxj6sYqKigx80wtIu6tXe/zli61PPTY3N2fy5MntdtP+0pe+lKeffjq33HJLbr311hx55JFtx6ZNm9buPO973/ty1VVX5VOf+lTuu+++3HbbbfnQhz7U5XqWLGlIf32lQlnZGz8o/fkeodTpUzYGe2xVmd9+es88/L/LUv/aqowcNijvGz08FeVlqa9f9y8v+wq9CqVBr0Jp0KvQ161OsqJTvdo6pzO6FEJWVlYmyVqfdmxsbMzw4et+r9Obn6KcOHFih+MTJ07MLbfckscee6xdCLkm5eXlOf7443PffffloYce2qAQslBIv/9Db2O4Ryh1+pT+rrysLLuP2azdWCn+zOtVKA16FUqDXoXS0F292qXdsbfffvskWeN7HxcvXpzly5ev8X2RbzZ06NBsueWWSZJNN920w/HWsZUrV3aqphEjRiRJli9f3qn5AAAAAEDP6lIIueeeeyZJ7rrrrg7HWsda56zLPvvskyR56qmnOhxrHaupqelUTY888kiSZPTo0Z2aDwAAAAD0rC6FkPvuu2/GjBmTm2++OU888UTbeENDQ6688soMHDgwRx11VNv4okWLsnDhwg7Lt0844YQkydVXX51XX321bXzx4sW59tprU15e3m5p9ZNPPpnVq1d3qOehhx7Kj3/84wwcODAf/vCHu3IrAAAAAEAP6dI7IQcMGJALLrggU6dOzeTJk3PYYYdl2LBhmTNnTurq6jJt2rR2TyROnz49s2bNykUXXZRjjjmmbXy33XbLySefnBkzZuSII47IwQcfnKamptx2221ZsmRJzjrrrLzjHe9omz9jxozccccd2X333bP11ltnwIABWbBgQe6+++6UlZXlG9/4Rrbddttu+HYAAAAAAN2ty7tj77PPPrn++utz2WWXZfbs2WlqakptbW3OOeecTJo0qdPnOffcc1NbW5uZM2dm1qxZKSsry7hx43L++efngx/8YLu5H/jAB/Lqq6/mL3/5S+65556sXr06I0eOzGGHHZZPfepTee9739vV2wAAAAAAekhZobBx70VVX7/2bcZLXVlZMnJkVb++Ryh1+hRKg16F0qBXoTToVSgNnenV1jmd0aV3QgIAAAAAdJUQEgAAAAAoKiEkAAAAAFBUQkgAAAAAoKiEkAAAAABAUQkhAQAAAICiEkICAAAAAEUlhAQAAAAAikoICQAAAAAUlRASAAAAACiqAb1dAMDb0dxSyPy6ZalvXJWRlYMyvmZ4KsrLerssAAAA4E2EkEDJmrugPpfMfSqLGle1jY2qHJSzJ+6YiTuN7MXKAAAAgDezHBsoSXMX1GfaTY+3CyCTZFHjqky76fHMXVDfS5UBAAAAbyWEBEpOc0shl8x9ap1zpt++MM0thR6qCAAAAFgXISRQcubXLevwBORbvdSwMvPrlvVQRQAAAMC6CCGBklO/ngCyq/MAAACA4hJCAiVnZOWgbp0HAAAAFJcQEig542uGZ9R6AsYtqwZnfM3wHqoIAAAAWBchJFByKsrLcvbEHdc556yDd0hFeVkPVQQAAACsixASKEkTdxqZi4/YucMTkVtWDc7FR+yciTuN7KXKAAAAgLca0NsFAGyoiTuNzIE7bJ75dctS37gqIysHZXzNcE9AAgAAQB8jhARKWkV5WXYfs1lvlwEAAACsg+XYAAAAAEBReRKyn2puKWR+3bKs/N9XM7ilxRJVAAAAAHqNELIfmrugPpfMfSqLGle1jY2qHJSzJ+5osw4AAAAAepzl2P3M3AX1mXbT4+0CyCRZ1Lgq0256PHMX1PdSZQAAAABsrISQ/UhzSyGXzH1qnXOm374wzS2FHqoIAAAAAISQ/cr8umUdnoB8q5caVmZ+3bIeqggAAAAAvBOyX6lfTwDZ1XkA/VHrxl31jasysnKQjbsAAAB6gBCyHxlZOahb5wH0NzbuAgAA6B2WY/cj42uGZ9R6AsYtqwZnfM3wHqoIoO+wcRcAAEDvEUL2IxXlZTl74o7rnHPWwTtYdghsdGzcBQAA0LuEkP3MxJ1G5uIjdu7wROSWVYNz8RE7W24IbJRs3AUAANC7vBOyH5q408gcuMPmmV+3LCvLyzO4pcXGC8BGzcZdAAAAvUsI2U9VlJdlj203y8iRVamvb0jBCkNgI2bjLgAAgN5lOTYA/Z6NuwAAAHqXEBKAfs/GXQAAAL1LCAnARsHGXQAAAL3HOyEB2Gi8eeOu+sZVGVk5yMZdAAAAPUAICcBGpaK8LLuP2ay3ywAAANioWI4NAAAAABSVEBIAAAAAKCohJAAAAABQVEJIAAAAAKCohJAAAAAAQFEJIQEAAACAohJCAgAAAABFNaC3CwAAOqe5pZD5dctS37gqIysHZXzN8FSUl/V2WQAAAOslhASAEjB3QX0umftUFjWuahsbVTkoZ0/cMRN3GtmLlQEAAKyf5dgA0MfNXVCfaTc93i6ATJJFjasy7abHM3dBfS9VBgAA0DlCSADow5pbCrlk7lPrnDP99oVpbin0UEUAAABdJ4QEgD5sft2yDk9AvtVLDSszv25ZD1UEAADQdd4JCQB9WP16AsiuzmPjYjMjAAD6CiEkAPRhIysHdes8Nh42MwIAoC+xHBsA+rDxNcMzaj0B45ZVgzO+ZngPVUQpsJkRAAB9jRASAPqwivKynD1xx3XOOevgHSyxpY3NjAAA6IuEkADQx03caWQuPmLnDk9Eblk1OBcfsbOltbRjMyMAAPoi74QEgBIwcaeROXCHzW0ywnrZzAgAgL5ICAkAJaKivCy7j9mst8ugj7OZEQAAfZHl2AAA/YjNjAAA6IuEkAAA/YjNjAAA6IuEkAC9pLmlkAeeeyU3zq/LA8+9YqdaoNvYzAgAgL7GOyEBesHcBfW5ZO5T7XawHVU5KGdP3FE4AHQLmxkBANCXCCEBetjcBfWZdtPjHcYXNa7KtJse95QS0G1sZgQAQF9hOTZ0o+aWQh78+yv5wxOL8uDfLa+lo+aWQi6Z+9Q650y/faGfHQAAAPoVT0JCN7G8ls6YX7es3c/ImrzUsDLz65Z5egkAAIB+w5OQ0A1al9e+NVxqXV47d0F9L1VGX1O/ngCyq/MAAACgFAgh4W2yvJauGPmWnWrf7jwAAAAoBUJIeJu6srwWxtcMz6j1BIxbVg3O+JrhPVQRAAAAFJ8QEt4my2vpioryspw9ccd1zjnr4B1SUV7WQxUBAABA8Qkh4W2yvJaumrjTyFx8xM4dnojcsmpwLj5iZxsZAQAA0O/YHRveptbltetakm15LW81caeROXCHzTO/bllWlpdncEtLxtcM9wQk9CHlDXUpX7H0/wZWDU3FK8vbvmwZUp2WqppeqAwAAEqPEBLeptbltdNuenytcyyvZU0qysuyx7abZeTIqtTXN6Rg7yLoM8ob6lI984CUNa9sNz7iTf9cqBicpZPnCSIBAKATLMeGbmB5LUD/Ur5iaYcA8q3Kmle2f1ISAABYK09CQjd58/La+sZVGVk5yPJaAAAAgAghoVtVlJdl9zGb9XYZAAAAAH2K5dgAAAAAQFEJIQEAAACAohJCAgAAAABFJYQEAAAAAIpKCAkA8BYtQ6pTqBi8zjmFisFpGVLdQxUBAEBpszs2AMBbtFTVZOnkeSlfsbRtbMRmQ/PyK8v/b86Q6rRU1fRGeQAAUHKEkAAAa9BSVdMWMpaVJRlZleZBDSkUercuAAAoRRscQj766KO5/PLL8/DDD6epqSm1tbWZMmVKJk2a1KXzLFmyJFdddVXuuOOOvPDCCxk6dGi23377HHnkkTnxxBM7zL/zzjtz1VVX5c9//nPKysqyyy675HOf+1z23XffDb0VAAAAAKCINuidkPfee29OPPHEPPjgg/nIRz6SE044IfX19TnzzDNzzTXXdPo8TzzxRA4//PDMnDkzO+64Y6ZMmZLDDz88m2yySW6//fYO82+88cZMnTo1CxcuzDHHHJOjjz46Tz31VE4++eTceuutG3IrAAAAAECRlRUKXVtU1NTUlI985CN58cUX86tf/Srjxo1LkjQ0NOS4445LXV1d/vCHP6SmZt3vSGpsbMxHP/rRrFixIjNmzMi73vWuDtcZMOD/HtRctmxZDjnkkFRUVOS3v/1tttpqqyTJiy++mKOOOipJ8sc//jGVlZVduZ3U1/ffZVVlZcnIkVX9+h6h1OlTKA16FUqDXoXSoFehNHSmV1vndEaXn4S8995789xzz+Xwww9vCyCTpKqqKp/97GezevXqzJo1a73nuf766/P888/n7LPP7hBAJmkXQCbJrbfemldffTUnnXRSWwCZJFtttVVOOumkvPzyy/njH//Y1dsBAAAAAIqsyyHkfffdlySZMGFCh2OtY/fff/96zzN79uyUlZXl0EMPzd/+9rdcd911ufrqq3Pbbbdl1apVG3Td1jkAAAAAQN/R5Y1pnnnmmSTJdttt1+HYFltskaFDh+bZZ59d5zlWrVqVv/71r6murs51112Xyy+/PC0tLW3Hx4wZkx/+8IcZO3Zsp67bOra+665JWVmXP1IyWu+tP98jlDp9CqVBr0Jp0KtQGvQqlIbO9GpX+rjLIWRjY2OSN5Zfr0llZWUaGhrWeY5ly5alubk5r7zySq644op8+ctfzpFHHpmmpqb84he/yI9+9KOcfvrpueWWWzJ48OD1Xrf1PZDru+6abL5559atl7KN4R6h1OlTKA16FUqDXoXSoFehNHRXr3Y5hOwOrU89Njc3Z/LkyTnllFPajn3pS1/K008/nVtuuSW33nprjjzyyKLWsmRJ/30RblnZGz8o/fkeodTpUygNehVKg16F0qBXoTR0pldb53RGl0PI9T112NjYmOHDh6/zHG9+mnHixIkdjk+cODG33HJLHnvssbYQ8s3XHTFiRIdrvvW8nVUopN//obcx3COUOn0KpUGvQmnQq1Aa9CqUhu7q1S5vTLP99tsnWfP7FxcvXpzly5ev8b2NbzZ06NBsueWWSZJNN920w/HWsZUrV3bquq1j67suAAAAANDzuhxC7rnnnkmSu+66q8Ox1rHWOeuyzz77JEmeeuqpDsdax2pqarp03b322mu91wUAAAAAelaXQ8h99903Y8aMyc0335wnnniibbyhoSFXXnllBg4cmKOOOqptfNGiRVm4cGGH5dsnnHBCkuTqq6/Oq6++2ja+ePHiXHvttSkvL8+HPvShtvGPfOQjqaqqys9//vO8+OKLbeMvvvhifv7zn2fEiBE55JBDuno7AAAAAECRdfmdkAMGDMgFF1yQqVOnZvLkyTnssMMybNiwzJkzJ3V1dZk2bVpGjx7dNn/69OmZNWtWLrroohxzzDFt47vttltOPvnkzJgxI0cccUQOPvjgNDU15bbbbsuSJUty1lln5R3veEfb/OHDh+e8887LV77ylRx99NGZNGlSkmT27Nl55ZVX8v3vf7/tvZEAAAAAQN+xQbtj77PPPrn++utz2WWXZfbs2WlqakptbW3OOeectnCwM84999zU1tZm5syZmTVrVsrKyjJu3Licf/75+eAHP9hh/pFHHpkRI0bkqquuym9+85skybvf/e6cfvrp2W+//TbkVgAAoFuUN9SlfMXStR5vGVKdlqqatR4HAOjPygqFjXsvqvr6tW8zXurKypKRI6v69T1CqdOnUBr0KutT3lCX6pkHpKx55VrnFCoGZ+nkeYLIItKrUBr0KpSGzvRq65zO6PI7IQEAgPbKVyxdZwCZJGXNK9f5pCQAQH8mhAQAAAAAikoICQAAAAAUlRASAAAAACgqISQAAAAAUFRCSAAAAACgqISQAAAAAEBRCSEBAOBtahlSnULF4HXOKVQMTsuQ6h6qCACgbxnQ2wUAAECpa6mqydLJ81K+Yuna5wypTktVTQ9WBQDQdwghAQCgG7RU1QgZAQDWwnJsAAAAAKCohJAAAAAAQFEJIQEAAACAohJCAgAAAABFJYQEAAAAAIrK7tgAAECSpLmlkPl1y1LfuCojKwdlfM3wVJSX9XZZAEA/IIQEAAAyd0F9Lpn7VBY1rmobG1U5KGdP3DETdxrZi5UBAP2B5dgAALCRm7ugPtNuerxdAJkkixpXZdpNj2fugvpeqgwA6C+EkAAAsBFrbinkkrlPrXPO9NsXprml0EMVAQD9kRASAAA2YvPrlnV4AvKtXmpYmfl1y3qoIgCgPxJCAgDARqx+PQFkV+cBAKyJEBIAADZiIysHdes8AIA1EUICAMBGbHzN8IxaT8C4ZdXgjK8Z3kMVAQD9kRASAAA2YhXlZTl74o7rnHPWwTukoryshyoCAPojISQAAGzkJu40MhcfsXOHJyK3rBqci4/YORN3GtlLlQEA/cWA3i4AAADofRN3GpkDd9g88+uWpb5xVUZWDsr4muGegKTfKW+oS/mKpWs93jKkOi1VNT1YEcDGQQgJAAAkeWNp9u5jNuvtMqBoyhvqUj3zgJQ1r1zrnELF4CydPE8QCdDNLMcGAABgo1C+Yuk6A8gkKWteuc4nJQHYMEJIAAAAAKCohJAAAAAAQFF5JyQAneZF7gAAAGwIISQAneJF7gAAAGwoy7EB6BQvcgcAAGBDCSEBAAAAgKISQgIAALBRaBlSnULF4HXOKVQMTsuQ6h6qCGDj4Z2QAAAAbBRaqmqydPI8G+0B9AIhJAAAULLKG+raB0qrhqbileVtXwqUeKuWqho/EwC9QAgJAACUpPKGulTPPKDDxmkj3vTPhYrBWTp5ntAJAHqZd0ICAAAlqXzF0g4B5FuVNa9c59JbAKBnCCEB6BQvcgcAAGBDWY4NQKd4kTsAAAAbSggJQKd5kTsAAAAbwnJsAAAAAKCohJAAAAAAQFEJIQEAAACAohJCAgAAJallSHUKFYPXOadQMTgtQ6p7qCIAYG1sTAMAAJSklqqaLJ08L+UrlraNjdhsaF5+Zfn/zRlSbVM1AOgDhJAAAEDJaqmqaQsZy8qSjKxK86CGFAq9WxcA0J7l2AAAAABAUQkhAQAAAICiEkICAAAAAEUlhAQAAAAAikoICQAAAAAUlRASAAAAACgqISQAAAAAUFRCSAAAAACgqISQAAAAAEBRCSEBAAAAgKISQgIAAAAARSWEBAAAAACKakBvFwAAALAxKm+oS/mKpWs93jKkOi1VNT1YEQAUjxASAACgh5U31KV65gEpa1651jmFisFZOnmeIBKAfsFybAAAgB5WvmLpOgPIJClrXrnOJyUBoJQIIQEAAACAohJCAgAAAABFJYQEAAAAAIpKCAkAAAAAFJUQEgAAAAAoKiEkAAAAAFBUQkgAAIAe1jKkOoWKweucU6gYnJYh1T1UEQAU14DeLgAAAGBj01JVk6WT56V8xdK1zxlSnZaqmh6sCgCKRwgJAADQC1qqaoSMAGw0LMcGAAAAAIpKCAkAAAAAFJUQEgAAAAAoKiEkAAAAAFBUQkgAAAAAoKiEkAAAAABAUQkhAQAAAICiEkICAAAAAEUlhAQAAAAAikoICQAAAAAUlRASAAAAACgqISQAAAAAUFQDNuRDjz76aC6//PI8/PDDaWpqSm1tbaZMmZJJkyZ16vO/+c1v8tWvfnWtx6+99trsvffe7cYmTpyYurq6Nc7fa6+9ct1113X+BgAAAACAHtPlEPLee+/N1KlTM2jQoBx22GEZNmxY5syZkzPPPDMvvvhiTjnllE6f6wMf+EDGjRvXYbympmaN86uqqvKpT32q0/MBAAAAgN7XpRCyqakp5513XsrKyjJz5sy2APGMM87Icccdl+nTp+fQQw/tdCh4yCGH5Jhjjun09TfddNN84Qtf6ErJAAAAAEAv69I7Ie+9994899xzOfzww9s9wVhVVZXPfvazWb16dWbNmtXtRQIAAAAApatLT0Led999SZIJEyZ0ONY6dv/993f6fI8//nheeeWVNDU1ZfTo0dl3330zYsSItc5ftWpVfvOb32TRokWprKzMe97znuy6665duQUAAAAAoId1KYR85plnkiTbbbddh2NbbLFFhg4dmmeffbbT53vrZjJDhgzJGWeckVNPPXWN8xcvXtxhQ5v3vOc9mT59erbddttOX/fNyso26GMlofXe+vM9QqnTp1Aa9CqUBr0KpUGvQmnoTK92pY+7FEI2NjYmeWP59ZpUVlamoaFhvecZPXp0zjvvvEyYMCFbbbVVli1blv/+7//O9OnTc8kll2STTTbJJz/5yXafOeaYY7L77runtrY2Q4cOzTPPPJMZM2bkxhtvzJQpU3LTTTelsrKyK7eTJNl88zXfS3+yMdwjlDp9CqVBr0Jp0KtQGvQqlIbu6tWyQqFQ6OzkU045JXfffXfmzJmzxqch999//yxfvjwPPvjgBhWzYMGCHHvssdlkk01y9913Z8CA9WekX/nKV3LjjTfm3HPPzcknn9zlay5Z0pDOfwdKS1nZGz8o/fkeodTpUygNehVKg16F0qBXoTR0pldb53RGl56EbH3ScG1POzY2Nmb48OFdOWU7O+20U3bffffcc889WbhwYcaOHbvez3z84x/PjTfemIceemiDQshCIf3+D72N4R6h1OlTKA16FUqDXoXSoFehNHRXr3Zpd+ztt98+Sdb43sfFixdn+fLla3xCsitaN6Z5/fXXuzR/+fLlb+u6AAAAAEBxdCmE3HPPPZMkd911V4djrWOtczZEc3NzHnvssSTJNtts06nPPProo0mSmpqaDb4uAAAAAFA8XQoh991334wZMyY333xznnjiibbxhoaGXHnllRk4cGCOOuqotvFFixZl4cKFHZZvtwaNb9bc3Jzvfe97efbZZ7P33ntn1KhRbccWLly4xicjFy5cmO9973tJko9+9KNduRUAAAAAoId06Z2QAwYMyAUXXJCpU6dm8uTJOeywwzJs2LDMmTMndXV1mTZtWkaPHt02f/r06Zk1a1YuuuiiHHPMMW3jxx57bMaOHZuxY8dmyy23zLJly3LfffflmWeeyVZbbZULL7yw3XVnz56dGTNmZM8998w222yTTTbZJM8880zmzZuX1atX57TTTntbT2ACAAAAAMXTpRAySfbZZ59cf/31ueyyyzJ79uw0NTWltrY255xzTiZNmtSpc5xyyimZP39+7rnnnixbtiwDBw7Mtttum9NPPz0nn3xyh81t9t577yxcuDBPPPFEHnjggaxYsSIjRozIAQcckBNPPDETJkzo6m0AAAAAAD2krFDYuPeiqq9f+zbjpa6sLBk5sqpf3yOUOn0KpUGvQmnQq1Aa9CqUhs70auuczujSOyEBAAAAALpKCAkAAAAAFJUQEgAAAAAoKiEkAAAAAFBUQkgAAAAAoKiEkAAAAABAUQ3o7QIAAACA9StvqEv5iqVrPd4ypDotVTU9WBFA5wkhAQAAoI8rb6hL9cwDUta8cq1zChWDs3TyPEEk0CdZjg0AAAB9XPmKpesMIJOkrHnlOp+UBOhNQkgAAAAAoKiEkAAAAABAUQkhAQAAAICiEkICAAAAAEUlhAQAAAAAikoICQAAAAAUlRASAAAA+riWIdUpVAxe55xCxeC0DKnuoYoAumZAbxcAAAAArFtLVU2WTp6X8hVL1z5nSHVaqmp6sCqAzhNCAgAAQAloqaoRMgIly3JsAAAAAKCohJAAAAAAQFEJIQEAAACAovJOSAAAAAD6lPKGOhsx9TNCSAAAAAD6jPKGulTPPCBlzSvXOqdQMThLJ88TRJYQy7EBAAAA6DPKVyxdZwCZJGXNK9f5pCR9jxASAAAAACgqISQAAAAAUFRCSAAAAACgqISQAAAAAEBRCSEBAAAAgKISQgIAAAAARSWEBAAAAKDPaBlSnULF4HXOKVQMTsuQ6h6qiO4woLcLAAAAAIBWLVU1WTp5XspXLF37nCHVaamq6cGqeLuEkAAAAAD0KS1VNULGfsZybAAAAACgqISQAAAAAEBRCSEBAAAAgKISQgIAAAAARSWEBAAAAACKSggJAAAAABSVEBIAAAAAKCohJAAAAABQVEJIAAAAAKCoBvR2AQAA0Kq8oS7lK5au9XjLkOq0VNX0YEUAAHQHISQAAH1CeUNdqmcekLLmlWudU6gYnKWT5wkiAQBKjOXYAAD0CeUrlq4zgEySsuaV63xSEgCAvkkICQAAAAAUlRASAAAAACgqISQAAAAAUFQ2pgEAAKBL7GQPQFcJIQEAAOg0O9kDsCEsxwYAAKDT7GQPwIYQQgIA0Ce0DKlOoWLwOucUKganZUh1D1UEAEB3sRwbAIA+oaWqJksnz/OeOQCAfkgICQBAn9FSVSNkBADohyzHBgAAAACKSggJAAAAABSVEBIAAAAAKCohJAAAAJ1mJ3sANoSNaQAAAOg0O9kDsCGEkAAAAHSJnewB6CrLsQEAAACAohJCAgAAAABFJYQEAAAAAIpKCAkAAAAAFJUQEgAAAAAoKiEkAAAAAFBUQkgAAAAAoKiEkAAAAABAUQkhAQAAAICiGtDbBQAAAAD9W3lDXcpXLP2/gVVDU/HK8rYvW4ZUp6WqphcqA3qKEBIAAAAomvKGulTPPCBlzSvbjY940z8XKgZn6eR5gkjoxyzHBgAAAIqmfMXSDgHkW5U1r2z/pCTQ7wghAQAAAICiEkICAAAAAEUlhAQAAAAAikoICQAAAAAUlRASAAAAACgqISQAAAAAUFQDersAAAAAoP9qGVKdQsXglDWvXOucQsXgtAyp7sGqoHjKG+pSvmLpWo+3DKlOS1VND1bUNwghAQAAgKJpqarJ0snz2oUyIzYbmpdfWf5/czbSUIb+p7yhLtUzD1hv6L508ryN7mdeCAkAAAAUVUtVTVvgUlaWZGRVmgc1pFDo3bqgu5WvWLrOADJJyppXpnzF0o0uhPROSAAAAACgqISQAAAAAEBRbfBy7EcffTSXX355Hn744TQ1NaW2tjZTpkzJpEmTOvX53/zmN/nqV7+61uPXXntt9t57726/LgAAAADQszYohLz33nszderUDBo0KIcddliGDRuWOXPm5Mwzz8yLL76YU045pdPn+sAHPpBx48Z1GK+p6bguvjuvCwAAAAD0jC6HkE1NTTnvvPNSVlaWmTNntgWIZ5xxRo477rhMnz49hx566BpDxDU55JBDcswxx/T4dQEAAACAntHld0Lee++9ee6553L44Ye3e4Kxqqoqn/3sZ7N69erMmjWrW4vszesCAAAAAG9Pl5+EvO+++5IkEyZM6HCsdez+++/v9Pkef/zxvPLKK2lqasro0aOz7777ZsSIEUW/LgAAAAB0p5Yh1SlUDE5Z88q1zilUDE7LkOoerKpv6HII+cwzzyRJtttuuw7HtthiiwwdOjTPPvtsp8933XXXtft6yJAhOeOMM3LqqacW9bqtysq6/JGS0Xpv/fkeodTpUygNehVKg16F0qBX6c8Km9bk5ZPmpez1pWufs0l1ClU16est0Jle7UofdzmEbGxsTPLGMug1qaysTENDw3rPM3r06Jx33nmZMGFCttpqqyxbtiz//d//nenTp+eSSy7JJptskk9+8pPdft232nzzNZ+vP9kY7hFKnT6F0qBXoTToVSgNepV+a+S7eruCbtVdvbpBu2N3h7322it77bVX29dDhgzJUUcdlV122SXHHntsfvCDH+QTn/hEBgwobolLljSkUCjqJXpNWdkbPyj9+R6h1OlTKA16FUqDXoXSoFehNHSmV1vndEaXE77KysokWetTh42NjRk+fHhXT9tmp512yu6775577rknCxcuzNixY4t63UIh/f4PvY3hHqHU6VMoDXoVSoNehdKgV6E0dFevdnl37O233z5J1vj+xcWLF2f58uVrfG9jV7RuTPP666/36HUBAAAAgO7X5RByzz33TJLcddddHY61jrXO2RDNzc157LHHkiTbbLNNj10XAAAAACiOLoeQ++67b8aMGZObb745TzzxRNt4Q0NDrrzyygwcODBHHXVU2/iiRYuycOHCDsuoW4PGN2tubs73vve9PPvss9l7770zatSoDb4uAAAAANA3dPmdkAMGDMgFF1yQqVOnZvLkyTnssMMybNiwzJkzJ3V1dZk2bVpGjx7dNn/69OmZNWtWLrroohxzzDFt48cee2zGjh2bsWPHZsstt8yyZcty33335ZlnnslWW22VCy+88G1dFwAAAADoGzZo6+l99tkn119/fS677LLMnj07TU1Nqa2tzTnnnJNJkyZ16hynnHJK5s+fn3vuuSfLli3LwIEDs+222+b000/PySefvMZNZrrjugAAAABAzyorFDbuvajq69e+zXipKytLRo6s6tf3CKVOn0Jp0KtQGvQqlAa9CqWhM73aOqczuvxOSAAAAACArhBCAgAAAABFJYQEAAAAAIpKCAkAAAAAFJUQEgAAAAAoKiEkAAAAAFBUQkgAAAAAoKiEkAAAAABAUQkhAQAAAICiEkICAAAAAEUlhAQAAAAAimpAbxcAAAAAQPcrb6hL+Yqlaz3eMqQ6LVU1PVgRGzMhJAAAAEA/U95Ql+qZB6SseeVa5xQqBmfp5HmCSHqE5dgAAAAA/Uz5iqXrDCCTpKx55TqflITuJIQEAAAAAIpKCAkAAAAAFJUQEgAAAAAoKiEkAAAAAFBUQkgAAAAAoKiEkAAAAABAUQkhAQAAAPqZliHVKVQMXuecQsXgtAyp7qGK2NgN6O0CAAAAAOheLVU1WTp5XspXLF37nCHVaamq6cGq2JgJIQEAAAD6oZaqGiEjfYbl2AAAAABAUQkhAQAAAICiEkICAAAAAEUlhAQAAAAAikoICQAAAAAUlRASAAAAACgqISQAAAAAUFRCSAAAAACgqISQAAAAAEBRCSEBAAAAgKISQgIAAAAARSWEBAAAAACKSggJAAAAABSVEBIAAAAAKCohJAAAAABQVEJIAAAAAKCohJAAAAAAQFEJIQEAAACAohJCAgAAAABFJYQEAAAAAIpKCAkAAAAAFJUQEgAAAAAoKiEkAAAAAFBUQkgAAAAAoKiEkAAAAABAUQkhAQAAAICiEkICAAAAAEUlhAQAAAAAikoICQAAAAAUlRASAAAAACgqISQAAAAAUFRCSAAAAACgqISQAAAAAEBRCSEBAAAAgKISQgIAAAAARTWgtwsAAAAA6KvKG+pSvmLpWo+3DKlOS1VND1YEpUkICQAAALAG5Q11qZ55QMqaV651TqFicJZOnieIhPWwHBsAAABgDcpXLF1nAJkkZc0r1/mkJPAGISQAAAAAUFRCSAAAAACgqISQAAAAAEBRCSEBAAAAgKISQgIAAAAARSWEBAAAAACKSggJAAAAsAYtQ6pTqBi8zjmFisFpGVLdQxVB6RrQ2wUAAAAA9EUtVTVZOnleylcsXfucIdVpqarpwaqgNAkhAQAAANaipapGyAjdwHJsAAAAAKCohJAAAAAAQFEJIQEAAACAohJCAgAAAABFJYQEAAAAAIpKCAkAAAAAFJUQEgAAAAAoKiEkAAAAAFBUQkgAAAAAoKiEkAAAAABAUQkhAQAAAICiEkICAAAAAEUlhAQAAAAAimpAbxfQ28rKeruC4mm9t/58j1Dq9CmUBr0KpUGvQmnQq1AaOtOrXenjskKhUHh7JQEAAAAArJ3l2AAAAABAUQkhAQAAAICiEkICAAAAAEUlhAQAAAAAikoICQAAAAAUlRASAAAAACgqISQAAAAAUFRCSAAAAACgqISQAAAAAEBRCSEBAAAAgKISQvZjf/rTnzJ27Nhcfvnlb+s8EydOzMSJE7upKujf9B2UBr0KpUGvQmnQq0BnDOjtAuifHn300Vx++eV5+OGH09TUlNra2kyZMiWTJk3q7dKgX7rxxhvz4IMP5rHHHstf//rXrF69OhdddFGOOeaYtX6msbExl19+eebMmZPFixdn1KhROfTQQ/P5z38+w4YN6zC/paUlM2fOzK9+9as8++yzGTp0aPbbb7+ceeaZGTNmTDFvD/qFl156KbfcckvmzZuXv/3tb6mvr8/w4cOz2267ZerUqdl1113X+Dm9Cj1r5cqVmT59eh577LE8++yzWbZsWTbddNOMGTMmxx9/fI444ogMHDiww+f0KvS+f/u3f8sll1ySJPnlL3+Z8ePHd5ijV6H3eBKSbnfvvffmxBNPzIMPPpiPfOQjOeGEE1JfX58zzzwz11xzTW+XB/3SpZdeml/+8pd5/vnnM2rUqPXOX758eU466aT89Kc/zTvf+c5MmTIl73jHO3LNNdfkU5/6VFauXNnhM9/4xjdywQUXpFAo5JOf/GT233//zJkzJ8cdd1yeeeaZItwV9C/XXXddLrroovz973/P+9///px88snZfffdc9ttt+WEE07I7NmzO3xGr0LPe+211/Lv//7vKSsry0EHHZSTTz45hxxySBYtWpR/+qd/ymc/+9m0tLS0+4xehd7317/+NZdffnmGDh261jl6FXqXJyHpVk1NTTnvvPNSVlaWmTNnZty4cUmSM844I8cdd1ymT5+eQw89NDU1Nb1cKfQvF1xwQbbbbrvU1NS0+w3w2vz4xz/OE088kc985jM555xz2sa/973v5eqrr85Pf/rTnHbaaW3j9957b2644YbsueeeueaaazJo0KAkyeGHH55TTz013/72t/OTn/ykODcH/cR73/veXHfdddlrr73ajT/wwAOZMmVKvvnNb+aQQw5p669Er0Jv2GyzzfLAAw+068XkjX/PPfnkk3PXXXdl3rx5Oeigg9qO6VXoXatXr865556bcePGZbvttstNN920xnl6FXqXJyG7yZvfgfHQQw/lk5/8ZN73vvdln332yTe/+c2sWLEiSXLHHXfk4x//eMaPH5/99tsv3/3ud9PU1NTuXE1NTZkxY0aOOOKIvPe9783uu++eT37yk5k7d+4ar71ixYp873vfy4EHHpj3vOc9Ofzww/OrX/1qnfX+/e9/z9e+9rUcdNBBefe7350JEybk3HPPTV1d3dv6Ptx777157rnncvjhh7cFkElSVVWVz372s1m9enVmzZr1tq4BrfTd/9lvv/06He4XCoXccMMNGTp0aD73uc+1O/a5z30uQ4cOzQ033NBuvPXrL33pS+3+o+zAAw/MXnvtlbvuuivPP//827wL+iu9+oYPfehDHQLIJNljjz2y9957Z9myZXnyySfbxvUqPU2vvqG8vLxDAJkkAwYMyAc/+MEkybPPPts2rlfpaXq1oyuvvDILFizI//f//X+pqKhY4xy9Cr1PCNnNHnnkkUyZMiVVVVU54YQTss022+Tf//3f8/Wvfz2zZ8/OF7/4xWyzzTb5+Mc/nqqqqvzkJz/JlVde2fb5QqGQL37xi/nOd76TlStXZvLkyTn88MPz5JNP5vTTT89Pf/rTdtdraWnJ6aefnquvvjqbbrpp/uEf/iG77rprLrroosyYMWOtNR599NH57W9/m1122SX/8A//kN133z2/+93vcvzxx+fvf//7Bt//fffdlySZMGFCh2OtY/fff/8Gnx/WZGPvu6565plnsmjRouy2224dlqsMHTo0u+22W/7+97/nhRdeaBv/05/+1Hbsrfbff/8k/9f/sDZ6de0GDBjQ7v8TvUrv0atr1tLSkjvvvDNJUltb2zauV+ktevUNf/7zn3PllVfm85//fHbccce1ztOr0AcU6Bb33ntvoba2tlBbW1v4z//8z7bxVatWFT760Y8Wxo4dW9h7770LjzzySNuxhoaGwr777lvYa6+9CqtWrSoUCoXCrFmzCrW1tYWTTjqpsHLlyra5dXV1hb333ruw8847F5577rm28V//+teF2trawqc//elCU1NT2/hf/vKXwi677FKora0tXHbZZe3qOfjggwvve9/7Cn/+85/b3cP9999fGDduXOG0005rN37wwQcXDj744E59H77whS8UamtrC//zP/+zxuPjx48vHHjggZ06F6yPvluzq666qlBbW1v49a9/vcbjt99+e6G2trbwrW99a43Hv/WtbxVqa2sL99xzT6FQKBRee+21Qm1tbeHwww9f4/xbb721UFtbW/jXf/3XDaqX/k+vrltdXV3h3e9+d+H9739/uzr1Kj1Nr7a3cuXKwmWXXVa49NJLC+eff37hwx/+cKG2trZw7rnntpunV+lpevX/rFy5snDYYYcVjjnmmLaapk2bVqitrS08/PDD7ebqVeh9noTsZnvvvXcOOeSQtq8HDhyYQw89NIVCIQcffHDe+973th2rrKzMQQcdlFdeeSUvvvhikrQtVf7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       "text/plain": [
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diff --git a/devel/multiple-comparison/multiple-comparisons.html b/devel/multiple-comparison/multiple-comparisons.html
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diff --git a/devel/objects.inv b/devel/objects.inv
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index f58c8571d3..9e65b5a744 100644
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -187,10 +187,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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diff --git a/devel/searchindex.js b/devel/searchindex.js
index 0aa74566dd..f953135766 100644
--- a/devel/searchindex.js
+++ b/devel/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"(G)ARCH-in-mean Models": [[908, "g-arch-in-mean-models"]], "(Nearly) out-of-sample forecasts": [[911, "(Nearly)-out-of-sample-forecasts"]], "API Reference": [[0, null]], "ARCH Modeling": [[914, null]], "Alternative Forecast Generation Schemes": [[913, "Alternative-Forecast-Generation-Schemes"]], "Analytical Forecasts": [[437, "analytical-forecasts"], [913, "Analytical-Forecasts"]], "Array Types": [[273, "array-types"]], "Asymptotic Normal Approximation (norm, cov or var)": [[4, "asymptotic-normal-approximation-norm-cov-or-var"]], "Augmented Dickey-Fuller Testing": [[434, "Augmented-Dickey-Fuller-Testing"]], "Automatic Bandwidth Selection": [[431, "automatic-bandwidth-selection"]], "Base Class": [[99, "base-class"]], "Basic (basic)": [[4, "basic-basic"]], "Basic Example": [[430, "basic-example"]], "Basic Forecasting": [[913, "Basic-Forecasting"]], "Bias-corrected (bc, bias-corrected or debiased)": [[4, "bias-corrected-bc-bias-corrected-or-debiased"]], "Bias-corrected and accelerated (bca)": [[4, "bias-corrected-and-accelerated-bca"]], "Block-length Selection": [[0, "block-length-selection"]], "Bootstrap": [[96, "bootstrap"]], "Bootstrap Examples": [[3, null]], "Bootstrap Forecasts": [[437, "bootstrap-forecasts"], [913, "Bootstrap-Forecasts"]], "Bootstrap Scenarios": [[915, "Bootstrap-Scenarios"]], "Bootstrapping": [[2, null]], "Bootstrapping Uneven Length Samples": [[3, "Bootstrapping-Uneven-Length-Samples"]], "Bootstraps": [[0, "bootstraps"]], "Building a Model From Components": [[914, "Building-a-Model-From-Components"]], "Change Logs": [[91, null]], "Changes Since 5.5": [[96, "changes-since-5-5"]], "Citation": [[248, "citation"]], "Cointegrating Relationship Estimation": [[0, "cointegrating-relationship-estimation"]], "Cointegrating Vector Estimation": [[274, "cointegrating-vector-estimation"]], "Cointegration Analysis": [[432, "cointegration-analysis"]], "Cointegration Testing": [[0, "cointegration-testing"], [433, null]], "Cointegration Tests": [[274, null]], "Common Type Definitions": [[273, null]], "Comparing across the year": [[915, "Comparing-across-the-year"]], "Comparing the paths": [[915, "Comparing-the-paths"]], "Conditional Mean Alignment vs. Forecast Alignment": [[911, "Conditional-Mean-Alignment-vs.-Forecast-Alignment"]], "Conditional Mean uses target alignment": [[911, "Conditional-Mean-uses-target-alignment"]], "Confidence Interval Methods": [[4, "confidence-interval-methods"]], "Confidence Interval Types": [[4, "confidence-interval-types"]], "Confidence Intervals": [[4, null]], "Constructing Parameter Estimates": [[87, "constructing-parameter-estimates"]], "Covariance Estimation": [[88, null]], "Custom Random Generators": [[915, "Custom-Random-Generators"]], "Data": [[912, "Data"], [913, "Data"]], "Deterministic terms": [[434, "Deterministic-terms"]], "Dickey-Fuller GLS Testing": [[434, "Dickey-Fuller-GLS-Testing"]], "Direct Estimation": [[912, "Direct-Estimation"]], "Distributions": [[436, null], [914, "Distributions"]], "Filtered Historical Simulation": [[913, "Filtered-Historical-Simulation"]], "Fitting the model": [[911, "Fitting-the-model"]], "Fitting the model directly": [[3, "Fitting-the-model-directly"]], "Fixed Window Forecasting": [[913, "Fixed-Window-Forecasting"]], "FixedVariance": [[917, "fixedvariance"]], "Fixing Parameters": [[914, "Fixing-Parameters"]], "Forecasting": [[437, null]], "Forecasting Options": [[437, "forecasting-options"]], "Forecasting the X values": [[911, "Forecasting-the-X-values"]], "Forecasting with Exogenous Regressors": [[911, null]], "GARCH (with a Constant Mean)": [[914, "GARCH-(with-a-Constant-Mean)"]], "GJR-GARCH": [[914, "GJR-GARCH"]], "High-level": [[0, "high-level"]], "Independent Samples": [[86, "independent-samples"]], "Independent, Identical Distributed Data (i.i.d.)": [[86, null]], "Index": [[248, "index"]], "Initial Mean Model Estimation": [[912, "Initial-Mean-Model-Estimation"]], "Initial Volatility Model Estimation": [[912, "Initial-Volatility-Model-Estimation"]], "Introduction": [[248, "introduction"], [430, null]], "Introduction to ARCH Models": [[907, null]], "KPSS Testing": [[434, "KPSS-Testing"]], "Long-run Covariance (HAC) Estimation": [[0, "long-run-covariance-hac-estimation"]], "Long-run Covariance Estimation": [[99, null]], "Long-run Covariance Estimators": [[99, "module-arch.covariance.kernel"]], "Low-level Interfaces": [[87, null]], "Mean Models": [[908, null], [914, "Mean-Models"]], "Mean Specification": [[0, "mean-specification"]], "Model Confidence Set (MCS)": [[270, "model-confidence-set-mcs"]], "Model Constructor": [[907, "model-constructor"]], "Model Results": [[909, null]], "Module Reference": [[270, null]], "Multiple Comparison Procedures": [[272, null]], "Multiple Comparisons": [[271, null]], "Nonparametric Confidence Intervals": [[4, "nonparametric-confidence-intervals"]], "Note": [[4, null], [4, null], [4, null], [4, null], [4, null], [12, null], [15, null], [25, null], [27, null], [30, null], [40, null], [42, null], [45, null], [55, null], [57, null], [60, null], [70, null], [72, null], [75, null], [85, null], [88, null], [89, null], [89, null], [89, null], [91, null], [98, null], [248, null]], "Optimal Block Length Estimation": [[3, "Optimal-Block-Length-Estimation"]], "Other Types": [[273, "other-types"]], "Output Classes": [[437, "output-classes"]], "Parameterless Variance Processes": [[917, "parameterless-variance-processes"]], "Parametric Bootstraps": [[89, "parametric-bootstraps"]], "Parametric Confidence Intervals": [[4, "parametric-confidence-intervals"]], "Parametric VaR": [[913, "Parametric-VaR"]], "Past Releases": [[91, "past-releases"]], "Percentile (percentile)": [[4, "percentile-percentile"]], "Phillips-Ouliaris": [[433, "Phillips-Ouliaris"]], "Phillips-Perron Testing": [[434, "Phillips-Perron-Testing"]], "Plotting the data": [[911, "Plotting-the-data"]], "Power": [[271, "Power"]], "Probit (statsmodels)": [[3, "Probit-(statsmodels)"]], "Producing multiple forecasts": [[911, "Producing-multiple-forecasts"]], "Re-estimating the mean with a FixedVariance": [[912, "Re-estimating-the-mean-with-a-FixedVariance"]], "Recursive Forecast Generation": [[913, "Recursive-Forecast-Generation"]], "References": [[1, null], [249, null]], "Regression output": [[434, "Regression-output"]], "Release 4.10": [[95, "release-4-10"]], "Release 4.11": [[95, "release-4-11"]], "Release 4.12": [[95, "release-4-12"]], "Release 4.13": [[95, "release-4-13"]], "Release 4.14": [[95, "release-4-14"]], "Release 4.15": [[95, "release-4-15"]], "Release 4.16": [[95, "release-4-16"]], "Release 4.17": [[95, "release-4-17"]], "Release 4.18": [[95, "release-4-18"]], "Release 4.19": [[95, "release-4-19"]], "Release 4.2": [[95, "release-4-2"]], "Release 4.3": [[95, "release-4-3"]], "Release 4.4": [[95, "release-4-4"]], "Release 4.5": [[95, "release-4-5"]], "Release 4.6": [[95, "release-4-6"]], "Release 4.7": [[95, "release-4-7"]], "Release 4.8": [[95, "release-4-8"]], "Release 4.8.1": [[95, "release-4-8-1"]], "Release 4.9": [[95, "release-4-9"]], "Release 5.0": [[96, "release-5-0"]], "Release 5.1": [[96, "release-5-1"]], "Release 5.2": [[96, "release-5-2"]], "Release 5.3": [[96, "release-5-3"]], "Release 5.4": [[96, "release-5-4"]], "Release 5.5": [[96, "release-5-5"]], "Release 6.0": [[97, "release-6-0"]], "Release 6.1": [[97, "release-6-1"]], "Release 6.2": [[97, "release-6-2"]], "Release 6.3": [[97, "release-6-3"]], "Release 7.0": [[91, "release-7-0"], [98, "release-7-0"]], "Release 7.1": [[91, "release-7-1"], [98, "release-7-1"]], "Reproducibility": [[3, "Reproducibility"]], "Results": [[99, "results"]], "Results Classes": [[274, "results-classes"]], "Rolling Window Forecasting": [[913, "Rolling-Window-Forecasting"]], "See also": [[5, null], [6, null], [7, null], [8, null], [9, null], [10, null], [100, null], [104, null], [105, null], [117, null], [127, null], [141, null], [151, null], [161, null], [171, 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"The-Model-Confidence-Set"]], "The Unit Root Tests": [[431, null]], "The test of Superior Predictive Ability (SPA)": [[271, "The-test-of-Superior-Predictive-Ability-(SPA)"]], "The wrapper function": [[3, "The-wrapper-function"]], "Theoretical Background": [[435, null]], "Time-series Bootstraps": [[90, null]], "True out-of-sample forecasts": [[911, "True-out-of-sample-forecasts"]], "Understanding Forecast Output": [[437, "understanding-forecast-output"]], "Unit Root": [[96, "unit-root"], [96, "id1"]], "Unit Root Testing": [[0, "unit-root-testing"], [432, null], [434, null]], "Univariate Volatility Models": [[910, null]], "Using DataFrame": [[911, "Using-DataFrame"]], "Using a dict": [[911, "Using-a-dict"]], "Using an array": [[911, "Using-an-array"]], "Using partial instead of extra_kwargs": [[89, "using-partial-instead-of-extra-kwargs"]], "Using the Fixed Variance process": [[912, null]], "Utilities": [[916, null]], "Value-at-Risk Forecasting": [[913, "Value-at-Risk-Forecasting"]], "Variance Ratio Testing": [[434, "Variance-Ratio-Testing"]], "Version 1": [[92, null]], "Version 1.1": [[92, "version-1-1"]], "Version 2": [[93, null]], "Version 2.1": [[93, "version-2-1"]], "Version 2.2": [[93, "version-2-2"]], "Version 3": [[94, null]], "Version 4": [[95, null]], "Version 5": [[96, null]], "Version 6": [[97, null]], "Version 7": [[91, "version-7"], [98, null]], "Visualizing the differences": [[915, "Visualizing-the-differences"]], "Volatility Forecasting": [[913, null]], "Volatility Modeling": [[0, "volatility-modeling"], [96, "volatility-modeling"]], "Volatility Process Specification": [[0, "volatility-process-specification"]], "Volatility Processes": [[914, "Volatility-Processes"], [917, null]], "Volatility Scenarios": [[915, null]], "WTI Crude": [[914, "WTI-Crude"]], "Warning": [[3, "Warning"], [89, null]], "Writing New Distributions": [[436, "writing-new-distributions"]], "Writing New Mean Models": [[908, "writing-new-mean-models"]], "Writing New Volatility 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This must be overridden when creating new bootstraps."], [25, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.CircularBlockBootstrap.__init__": [[5, 2, 2, 0, "args", "Positional arguments to bootstrap"], [5, 2, 2, 0, "block_size", "Size of block to use"], [5, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [5, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results."], [5, 2, 2, 0, "seed", "Seed to use to ensure reproducable results."]], "arch.bootstrap.CircularBlockBootstrap.apply": [[11, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func."], [11, 2, 2, 0, "func", "Function the computes parameter values."], [11, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.bootstrap": [[12, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.clone": [[13, 2, 2, 0, "args", "Positional arguments to bootstrap"], [13, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [13, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.CircularBlockBootstrap.conf_int": [[14, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [14, 2, 2, 0, "func", "Function the computes parameter values."], [14, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [14, 2, 2, 0, "reps", "Number of bootstrap replications"], [14, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results."], [14, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'."], [14, 2, 2, 0, "size", "Coverage of confidence interval"], [14, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap."], [14, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap."], [14, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.CircularBlockBootstrap.cov": [[15, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [15, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [15, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [15, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.reset": [[20, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided."]], "arch.bootstrap.CircularBlockBootstrap.seed": [[21, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.CircularBlockBootstrap.set_state": [[22, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.CircularBlockBootstrap.var": [[25, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [25, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [25, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [25, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap": [[26, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [27, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [28, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [29, 3, 1, 0, "conf_int", "Function the computes parameter values.  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This must be overridden when creating new bootstraps."], [40, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.IIDBootstrap.__init__": [[6, 2, 2, 0, "args", "Positional arguments to bootstrap"], [6, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [6, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results."], [6, 2, 2, 0, "seed", "Seed to use to ensure reproducable results."]], "arch.bootstrap.IIDBootstrap.apply": [[26, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func."], [26, 2, 2, 0, "func", "Function the computes parameter values."], [26, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.bootstrap": [[27, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.clone": [[28, 2, 2, 0, "args", "Positional arguments to bootstrap"], [28, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [28, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.IIDBootstrap.conf_int": [[29, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [29, 2, 2, 0, "func", "Function the computes parameter values."], [29, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [29, 2, 2, 0, "reps", "Number of bootstrap replications"], [29, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results."], [29, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'."], [29, 2, 2, 0, "size", "Coverage of confidence interval"], [29, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap."], [29, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap."], [29, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.IIDBootstrap.cov": [[30, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [30, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [30, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [30, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.reset": [[35, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided."]], "arch.bootstrap.IIDBootstrap.seed": [[36, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.IIDBootstrap.set_state": [[37, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.IIDBootstrap.var": [[40, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [40, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [40, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [40, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap": [[41, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [42, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [43, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [44, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [45, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [7, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [46, 5, 1, 0, "generator", "Set or get the instance PRNG"], [47, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [48, 5, 1, 0, "index", "Returns the current index of the bootstrap"], [7, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [7, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [49, 5, 1, 0, "random_state", "Set or get the instance random state"], [50, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [51, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [52, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [53, 5, 1, 0, "state", "Set or get the generator's state"], [54, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [55, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.IndependentSamplesBootstrap.__init__": [[7, 2, 2, 0, "args", "Positional arguments to bootstrap"], [7, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [7, 2, 2, "arch.bootstrap.IndependentSamplesBootstrap", "random_state", "Bootstrap where each input is independently resampled"], [7, 2, 2, "arch.bootstrap.IndependentSamplesBootstrap", "seed", "Bootstrap where each input is independently resampled"]], "arch.bootstrap.IndependentSamplesBootstrap.apply": [[41, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func."], [41, 2, 2, 0, "func", "Function the computes parameter values."], [41, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.bootstrap": [[42, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.clone": [[43, 2, 2, 0, "args", "Positional arguments to bootstrap"], [43, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [43, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.IndependentSamplesBootstrap.conf_int": [[44, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [44, 2, 2, 0, "func", "Function the computes parameter values."], [44, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [44, 2, 2, 0, "reps", "Number of bootstrap replications"], [44, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results."], [44, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'."], [44, 2, 2, 0, "size", "Coverage of confidence interval"], [44, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap."], [44, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap."], [44, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.IndependentSamplesBootstrap.cov": [[45, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [45, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [45, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [45, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.reset": [[50, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided."]], "arch.bootstrap.IndependentSamplesBootstrap.seed": [[51, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.IndependentSamplesBootstrap.set_state": [[52, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.IndependentSamplesBootstrap.var": [[55, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [55, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [55, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [55, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MCS": [[253, 3, 1, 0, "compute", "Compute the set of models in the confidence set."], [254, 5, 1, 0, "excluded", "List of model indices that are excluded from the MCS"], [255, 5, 1, 0, "included", "List of model indices that are included in the MCS"], [256, 5, 1, 0, "pvalues", "Model p-values for inclusion in the MCS"], [257, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [258, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"]], "arch.bootstrap.MCS.__init__": [[250, 2, 2, 0, "block_size", "Length of window to use in the bootstrap."], [250, 2, 2, 0, "bootstrap", "Bootstrap to use."], [250, 2, 2, 0, "losses", "T by k array containing losses from a set of models"], [250, 2, 2, 0, "method", "MCS test and elimination implementation method, either 'max' or 'R'. Default is 'R'."], [250, 2, 2, 0, "reps", "Number of bootstrap replications to uses."], [250, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value."], [250, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the mcs."]], "arch.bootstrap.MCS.seed": [[258, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.MovingBlockBootstrap": [[56, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [57, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [58, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [59, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [60, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [8, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [61, 5, 1, 0, "generator", "Set or get the instance PRNG"], [62, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [63, 5, 1, 0, "index", "The current index of the bootstrap"], [8, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [8, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [64, 5, 1, 0, "random_state", "Set or get the instance random state"], [65, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [66, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [67, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [68, 5, 1, 0, "state", "Set or get the generator's state"], [69, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [70, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.MovingBlockBootstrap.__init__": [[8, 2, 2, 0, "args", "Positional arguments to bootstrap"], [8, 2, 2, 0, "block_size", "Size of block to use"], [8, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [8, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results."], [8, 2, 2, 0, "seed", "Seed to use to ensure reproducable results."]], "arch.bootstrap.MovingBlockBootstrap.apply": [[56, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func."], [56, 2, 2, 0, "func", "Function the computes parameter values."], [56, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.bootstrap": [[57, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.clone": [[58, 2, 2, 0, "args", "Positional arguments to bootstrap"], [58, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [58, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.MovingBlockBootstrap.conf_int": [[59, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [59, 2, 2, 0, "func", "Function the computes parameter values."], [59, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [59, 2, 2, 0, "reps", "Number of bootstrap replications"], [59, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results."], [59, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'."], [59, 2, 2, 0, "size", "Coverage of confidence interval"], [59, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap."], [59, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap."], [59, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.MovingBlockBootstrap.cov": [[60, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [60, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [60, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [60, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.reset": [[65, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided."]], "arch.bootstrap.MovingBlockBootstrap.seed": [[66, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.MovingBlockBootstrap.set_state": [[67, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.MovingBlockBootstrap.var": [[70, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [70, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [70, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [70, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.SPA": [[259, 3, 1, 0, "better_models", "Returns set of models rejected as being equal-or-worse than the benchmark"], [260, 3, 1, 0, "compute", "Compute the bootstrap pvalue."], [261, 3, 1, 0, "critical_values", "Returns data-dependent critical values"], [262, 5, 1, 0, "pvalues", "P-values corresponding to the lower, consistent and upper p-values."], [263, 3, 1, 0, "reset", "Reset the bootstrap to its initial state."], [264, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [265, 3, 1, 0, "subset", "Sets a list of active models to run the SPA on.  Primarily for internal use."]], "arch.bootstrap.SPA.__init__": [[251, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [251, 2, 2, 0, "block_size", "Length of window to use in the bootstrap."], [251, 2, 2, 0, "bootstrap", "Bootstrap to use."], [251, 2, 2, 0, "models", "T  by k element array of alternative model losses"], [251, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization."], [251, 2, 2, 0, "reps", "Number of bootstrap replications to uses."], [251, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value."], [251, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials."]], "arch.bootstrap.SPA.better_models": [[259, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing superior models"], [259, 2, 2, 0, "pvalue_type", "String in 'lower', 'consistent', or 'upper' indicating which critical value to use."]], "arch.bootstrap.SPA.critical_values": [[261, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing the critical values."]], "arch.bootstrap.SPA.seed": [[264, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.SPA.subset": [[265, 2, 2, 0, "selector", "Boolean array indicating which columns to use when computing the p-values."]], "arch.bootstrap.StationaryBootstrap": [[71, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [72, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [73, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [74, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [75, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [9, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [76, 5, 1, 0, "generator", "Set or get the instance PRNG"], [77, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [78, 5, 1, 0, "index", "The current index of the bootstrap"], [9, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [9, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [79, 5, 1, 0, "random_state", "Set or get the instance random state"], [80, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [81, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [82, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [83, 5, 1, 0, "state", "Set or get the generator's state"], [84, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [85, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.StationaryBootstrap.__init__": [[9, 2, 2, 0, "args", "Positional arguments to bootstrap"], [9, 2, 2, 0, "block_size", "Average size of block to use"], [9, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [9, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results."], [9, 2, 2, 0, "seed", "Seed to use to ensure reproducable results."]], "arch.bootstrap.StationaryBootstrap.apply": [[71, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func."], [71, 2, 2, 0, "func", "Function the computes parameter values."], [71, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.bootstrap": [[72, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.clone": [[73, 2, 2, 0, "args", "Positional arguments to bootstrap"], [73, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [73, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.StationaryBootstrap.conf_int": [[74, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [74, 2, 2, 0, "func", "Function the computes parameter values."], [74, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [74, 2, 2, 0, "reps", "Number of bootstrap replications"], [74, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results."], [74, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'."], [74, 2, 2, 0, "size", "Coverage of confidence interval"], [74, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap."], [74, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap."], [74, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.StationaryBootstrap.cov": [[75, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [75, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [75, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [75, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.reset": [[80, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided."]], "arch.bootstrap.StationaryBootstrap.seed": [[81, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.StationaryBootstrap.set_state": [[82, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.StationaryBootstrap.var": [[85, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [85, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [85, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [85, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StepM": [[266, 3, 1, 0, "compute", "Compute the set of superior models."], [267, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [268, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [269, 5, 1, 0, "superior_models", "List of the indices or column names of the superior models"]], "arch.bootstrap.StepM.__init__": [[252, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [252, 2, 2, 0, "block_size", "Length of window to use in the bootstrap."], [252, 2, 2, 0, "bootstrap", "Bootstrap to use."], [252, 2, 2, 0, "models", "T by k element array of alternative model losses"], [252, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization."], [252, 2, 2, 0, "reps", "Number of bootstrap replications to uses."], [252, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value."], [252, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the comparison."], [252, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials."]], "arch.bootstrap.StepM.seed": [[268, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.optimal_block_length": [[10, 2, 2, 0, "x", "A one-dimensional or two-dimensional array-like."]], "arch.covariance": [[99, 0, 0, 1, "kernel", ""]], "arch.covariance.kernel": [[100, 1, 1, 0, "Andrews", "Alternative name of the QuadraticSpectral covariance estimator."], [101, 1, 1, 0, "Bartlett", "Bartlett's (Newey-West) kernel covariance estimation."], [102, 1, 1, 0, "CovarianceEstimate", "Covariance estimate using a long-run covariance estimator"], [103, 1, 1, 0, "CovarianceEstimator", "%(kernel_name)s kernel covariance estimation."], [104, 1, 1, 0, "Gallant", "Alternative name for Parzen covariance estimator."], [105, 1, 1, 0, "NeweyWest", "Alternative name for Bartlett covariance estimator."], [106, 1, 1, 0, "Parzen", "Parzen's kernel covariance estimation."], [107, 1, 1, 0, "ParzenCauchy", "Parzen's Cauchy kernel covariance estimation."], [108, 1, 1, 0, "ParzenGeometric", "Parzen's Geometric kernel covariance estimation."], [109, 1, 1, 0, "ParzenRiesz", "Parzen-Reisz kernel covariance estimation."], [110, 1, 1, 0, "QuadraticSpectral", "Quadratic-Spectral (Andrews') kernel covariance estimation."], [111, 1, 1, 0, "TukeyHamming", "Tukey-Hamming kernel covariance estimation."], [112, 1, 1, 0, "TukeyHanning", "Tukey-Hanning kernel covariance estimation."], [113, 1, 1, 0, "TukeyParzen", "Tukey-Parzen kernel covariance estimation."]], "arch.covariance.kernel.Andrews": [[114, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [115, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [116, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [117, 5, 1, 0, "cov", "The estimated covariances."], [118, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [119, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [120, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [121, 5, 1, 0, "name", "The covariance estimator's name."], [122, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [123, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Andrews.__init__": [[100, 2, 2, "arch.covariance.kernel.Andrews", "bandwidth", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 2, 2, "arch.covariance.kernel.Andrews", "center", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 2, 2, "arch.covariance.kernel.Andrews", "df_adjust", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 2, 2, "arch.covariance.kernel.Andrews", "force_int", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 2, 2, "arch.covariance.kernel.Andrews", "weights", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 2, 2, "arch.covariance.kernel.Andrews", "x", "Alternative name of the QuadraticSpectral covariance estimator."]], "arch.covariance.kernel.Bartlett": [[124, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [125, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [126, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [127, 5, 1, 0, "cov", "The estimated covariances."], [128, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [129, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [130, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [131, 5, 1, 0, "name", "The covariance estimator's name."], [132, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [133, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Bartlett.__init__": [[101, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [101, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [101, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [101, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [101, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [101, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.CovarianceEstimate": [[134, 5, 1, 0, "long_run", "The long-run covariance estimate."], [135, 5, 1, 0, "one_sided", "The one-sided covariance estimate."], [136, 5, 1, 0, "one_sided_strict", "The one-sided strict covariance estimate."], [137, 5, 1, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.CovarianceEstimate.__init__": [[102, 2, 2, 0, "columns", "Column labels to use if covariance estimates are returned as DataFrames."], [102, 2, 2, 0, "long_run", "The long-run covariance estimate."], [102, 2, 2, 0, "one_sided", "The one-sided-strict covariance estimate."], [102, 2, 2, 0, "one_sided_strict", "The one-sided strict covariance estimate."], [102, 2, 2, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.CovarianceEstimator": [[138, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [139, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [140, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [141, 5, 1, 0, "cov", "The estimated covariances."], [142, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [143, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [144, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [145, 5, 1, 0, "name", "The covariance estimator's name."], [146, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [147, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.CovarianceEstimator.__init__": [[103, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [103, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [103, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [103, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [103, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [103, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.Gallant": [[148, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [149, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [150, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [151, 5, 1, 0, "cov", "The estimated covariances."], [152, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [153, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [154, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [155, 5, 1, 0, "name", "The covariance estimator's name."], [156, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [157, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Gallant.__init__": [[104, 2, 2, "arch.covariance.kernel.Gallant", "bandwidth", "Alternative name for Parzen covariance estimator."], [104, 2, 2, "arch.covariance.kernel.Gallant", "center", "Alternative name for Parzen covariance estimator."], [104, 2, 2, "arch.covariance.kernel.Gallant", "df_adjust", "Alternative name for Parzen covariance estimator."], [104, 2, 2, "arch.covariance.kernel.Gallant", "force_int", "Alternative name for Parzen covariance estimator."], [104, 2, 2, "arch.covariance.kernel.Gallant", "weights", "Alternative name for Parzen covariance estimator."], [104, 2, 2, "arch.covariance.kernel.Gallant", "x", "Alternative name for Parzen covariance estimator."]], "arch.covariance.kernel.NeweyWest": [[158, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [159, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [160, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [161, 5, 1, 0, "cov", "The estimated covariances."], [162, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [163, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [164, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [165, 5, 1, 0, "name", "The covariance estimator's name."], [166, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [167, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.NeweyWest.__init__": [[105, 2, 2, "arch.covariance.kernel.NeweyWest", "bandwidth", "Alternative name for Bartlett covariance estimator."], [105, 2, 2, "arch.covariance.kernel.NeweyWest", "center", "Alternative name for Bartlett covariance estimator."], [105, 2, 2, "arch.covariance.kernel.NeweyWest", "df_adjust", "Alternative name for Bartlett covariance estimator."], [105, 2, 2, "arch.covariance.kernel.NeweyWest", "force_int", "Alternative name for Bartlett covariance estimator."], [105, 2, 2, "arch.covariance.kernel.NeweyWest", "weights", "Alternative name for Bartlett covariance estimator."], [105, 2, 2, "arch.covariance.kernel.NeweyWest", "x", "Alternative name for Bartlett covariance estimator."]], "arch.covariance.kernel.Parzen": [[168, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [169, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [170, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [171, 5, 1, 0, "cov", "The estimated covariances."], [172, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [173, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [174, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [175, 5, 1, 0, "name", "The covariance estimator's name."], [176, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [177, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Parzen.__init__": [[106, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [106, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [106, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [106, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [106, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [106, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenCauchy": [[178, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [179, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [180, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [181, 5, 1, 0, "cov", "The estimated covariances."], [182, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [183, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [184, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [185, 5, 1, 0, "name", "The covariance estimator's name."], [186, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [187, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenCauchy.__init__": [[107, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [107, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [107, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [107, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [107, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [107, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenGeometric": [[188, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [189, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [190, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [191, 5, 1, 0, "cov", "The estimated covariances."], [192, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [193, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [194, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [195, 5, 1, 0, "name", "The covariance estimator's name."], [196, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [197, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenGeometric.__init__": [[108, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [108, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [108, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [108, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [108, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [108, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenRiesz": [[198, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [199, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [200, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [201, 5, 1, 0, "cov", "The estimated covariances."], [202, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [203, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [204, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [205, 5, 1, 0, "name", "The covariance estimator's name."], [206, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [207, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenRiesz.__init__": [[109, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [109, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [109, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [109, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [109, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [109, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.QuadraticSpectral": [[208, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [209, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [210, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [211, 5, 1, 0, "cov", "The estimated covariances."], [212, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [213, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [214, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [215, 5, 1, 0, "name", "The covariance estimator's name."], [216, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [217, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.QuadraticSpectral.__init__": [[110, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [110, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [110, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [110, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [110, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [110, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHamming": [[218, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [219, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [220, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [221, 5, 1, 0, "cov", "The estimated covariances."], [222, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [223, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [224, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [225, 5, 1, 0, "name", "The covariance estimator's name."], [226, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [227, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHamming.__init__": [[111, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [111, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [111, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [111, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [111, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [111, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHanning": [[228, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [229, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [230, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [231, 5, 1, 0, "cov", "The estimated covariances."], [232, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [233, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [234, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [235, 5, 1, 0, "name", "The covariance estimator's name."], [236, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [237, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHanning.__init__": [[112, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [112, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [112, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [112, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [112, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [112, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyParzen": [[238, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [239, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [240, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [241, 5, 1, 0, "cov", "The estimated covariances."], [242, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [243, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [244, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [245, 5, 1, 0, "name", "The covariance estimator's name."], [246, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [247, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyParzen.__init__": [[113, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [113, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [113, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [113, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [113, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [113, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.typing": [[273, 4, 1, 0, "AnyArray", "alias of ndarray[Any, Any]"], [273, 4, 1, 0, "AnyPandas", "alias of Series | DataFrame"], [273, 4, 1, 0, "ArrayLike", "alias of ndarray | DataFrame | Series"], [273, 4, 1, 0, "ArrayLike1D", "alias of ndarray | Series"], [273, 4, 1, 0, "ArrayLike2D", "alias of ndarray | DataFrame"], [273, 4, 1, 0, "BoolArray", "alias of ndarray[Any, dtype[bool]]"], [273, 4, 1, 0, "BootstrapIndexT", "alias of ndarray[Any, dtype[longlong]] | tuple[ndarray[Any, dtype[longlong]], ...] | tuple[list[ndarray[Any, dtype[longlong]]], dict[str, ndarray[Any, dtype[longlong]]]]"], [273, 4, 1, 0, "DateLike", "alias of str | datetime | datetime64 | Timestamp"], [273, 4, 1, 0, "Float64Array", "alias of ndarray[Any, dtype[float64]]"], [273, 1, 1, 0, "FloatOrArray", "alias of TypeVar('FloatOrArray', float, ~numpy.ndarray)"], [273, 4, 1, 0, "ForecastingMethod", "alias of Literal['analytic', 'simulation', 'bootstrap']"], [273, 4, 1, 0, "Int32Array", "alias of ndarray[Any, dtype[int32]]"], [273, 4, 1, 0, "Int64Array", "alias of ndarray[Any, dtype[longlong]]"], [273, 4, 1, 0, "IntArray", "alias of ndarray[Any, dtype[int64]]"], [273, 4, 1, 0, "Label", "alias of Hashable | None"], [273, 4, 1, 0, "NDArray", "alias of ndarray"], [273, 1, 1, 0, "NDArrayOrFrame", "alias of TypeVar('NDArrayOrFrame', ~numpy.ndarray[~typing.Any, ~numpy.dtype[~numpy.float64]], ~pandas.core.frame.DataFrame)"], [273, 4, 1, 0, "RNGType", "alias of Callable[[int | tuple[int, ...]], ndarray[Any, dtype[float64]]]"], [273, 4, 1, 0, "RandomStateState", "alias of tuple[str, ndarray[Any, dtype[uint32]], int, int, float]"], [273, 4, 1, 0, "Uint32Array", "alias of ndarray[Any, dtype[uint32]]"], [273, 4, 1, 0, "UnitRootTrend", "alias of Literal['n', 'c', 'ct', 'ctt']"]], "arch.unitroot": [[275, 1, 1, 0, "ADF", "Augmented Dickey-Fuller unit root test"], [276, 1, 1, 0, "DFGLS", "Elliott, Rothenberg and Stock's (ers) GLS detrended Dickey-Fuller"], [277, 1, 1, 0, "KPSS", "Kwiatkowski, Phillips, Schmidt and Shin (KPSS) stationarity test"], [278, 1, 1, 0, "PhillipsPerron", "Phillips-Perron unit root test"], [279, 1, 1, 0, "VarianceRatio", "Variance Ratio test of a random walk."], [280, 1, 1, 0, "ZivotAndrews", "Zivot-Andrews structural-break unit-root test"], [281, 6, 1, 0, "auto_bandwidth", "Automatic bandwidth selection of Andrews (1991) and Newey & West (1994)."], [274, 0, 0, 1, "cointegration", ""]], "arch.unitroot.ADF": [[291, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [292, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [293, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [294, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [295, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [296, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [297, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [298, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [299, 5, 1, 0, "stat", "The test statistic for a unit root"], [300, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [301, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [302, 5, 1, 0, "valid_trends", "List of valid trend terms."], [303, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.ADF.__init__": [[275, 2, 2, 0, "lags", "The number of lags to use in the ADF regression."], [275, 2, 2, 0, "low_memory", "Flag indicating whether to use a low memory implementation of the lag selection algorithm."], [275, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [275, 2, 2, 0, "method", "The method to use when selecting the lag length"], [275, 2, 2, 0, "trend", "The trend component to include in the test"], [275, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.DFGLS": [[304, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [305, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [306, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [307, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [308, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [309, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [310, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [311, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [312, 5, 1, 0, "stat", "The test statistic for a unit root"], [313, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [314, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [315, 5, 1, 0, "valid_trends", "List of valid trend terms."], [316, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.DFGLS.__init__": [[276, 2, 2, 0, "lags", "The number of lags to use in the ADF regression."], [276, 2, 2, "arch.unitroot.DFGLS", "low_memory", "Elliott, Rothenberg and Stock's (ers) GLS detrended Dickey-Fuller"], [276, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length."], [276, 2, 2, 0, "method", "The method to use when selecting the lag length"], [276, 2, 2, 0, "trend", "The trend component to include in the test"], [276, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.KPSS": [[317, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [318, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [319, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [320, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [321, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [322, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [323, 5, 1, 0, "stat", "The test statistic for a unit root"], [324, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [325, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [326, 5, 1, 0, "valid_trends", "List of valid trend terms."], [327, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.KPSS.__init__": [[277, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance."], [277, 2, 2, 0, "trend", "\"c\" - Include a constant (Default) \"ct\" - Include a constant and linear time trend"], [277, 2, 2, 0, "y", "The data to test for stationarity"]], "arch.unitroot.PhillipsPerron": [[328, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [329, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [330, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [331, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [332, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [333, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [334, 5, 1, 0, "regression", "Returns OLS regression results for the specification used in the test"], [335, 5, 1, 0, "stat", "The test statistic for a unit root"], [336, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [337, 5, 1, 0, "test_type", "Gets or sets the test type returned by stat. Valid values are \"tau\" or \"rho\""], [338, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [339, 5, 1, 0, "valid_trends", "List of valid trend terms."], [340, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.PhillipsPerron.__init__": [[278, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance."], [278, 2, 2, 0, "test_type", "The test to use when computing the test statistic."], [278, 2, 2, 0, "trend", "The trend component to include in the test"], [278, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.VarianceRatio": [[341, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [342, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [343, 5, 1, 0, "debiased", "Sets of gets the indicator to use debiased variances in the ratio"], [344, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [345, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [346, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [347, 5, 1, 0, "overlap", "Sets of gets the indicator to use overlapping returns in the long-period variance estimator"], [348, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [349, 5, 1, 0, "robust", "Sets of gets the indicator to use a heteroskedasticity robust variance estimator"], [350, 5, 1, 0, "stat", "The test statistic for a unit root"], [351, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [352, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [353, 5, 1, 0, "valid_trends", "List of valid trend terms."], [354, 5, 1, 0, "vr", "The ratio of the long block lags-period variance to the 1-period variance"], [355, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.VarianceRatio.__init__": [[279, 2, 2, 0, "debiased", "Indicates whether to use a debiased version of the test."], [279, 2, 2, 0, "lags", "The number of periods to used in the multi-period variance, which is the numerator of the test statistic."], [279, 2, 2, 0, "overlap", "Indicates whether to use all overlapping blocks."], [279, 2, 2, 0, "robust", "Indicates whether to use heteroskedasticity robust inference."], [279, 2, 2, 0, "trend", "\"c\" allows for a non-zero drift in the random walk, while \"n\" requires that the increments to y are mean 0"], [279, 2, 2, 0, "y", "The data to test for a random walk"]], "arch.unitroot.ZivotAndrews": [[356, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [357, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [358, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [359, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [360, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [361, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [362, 5, 1, 0, "stat", "The test statistic for a unit root"], [363, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [364, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [365, 5, 1, 0, "valid_trends", "List of valid trend terms."], [366, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.ZivotAndrews.__init__": [[280, 2, 2, 0, "lags", "The number of lags to use in the ADF regression."], [280, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [280, 2, 2, 0, "method", "The method to use when selecting the lag length"], [280, 2, 2, 0, "trend", "The trend component to include in the test"], [280, 2, 2, 0, "trim", "percentage of series at begin/end to exclude from break-period calculation in range [0, 0.333] (default=0.15)"], [280, 2, 2, 0, "y", "data series"]], "arch.unitroot.auto_bandwidth": [[281, 2, 2, 0, "kernel", "The kernel function to use for selecting the bandwidth"], [281, 2, 2, 0, "y", "Data on which to apply the bandwidth selection"]], "arch.unitroot.cointegration": [[282, 1, 1, 0, "CanonicalCointegratingReg", "Canonical Cointegrating Regression cointegrating vector estimation."], [283, 1, 1, 0, "CointegrationAnalysisResults", "summary()"], [284, 1, 1, 0, "DynamicOLS", "Dynamic OLS (DOLS) cointegrating vector estimation"], [285, 1, 1, 0, "DynamicOLSResults", "Estimation results for Dynamic OLS models"], [286, 1, 1, 0, "EngleGrangerTestResults", "Results class for Engle-Granger cointegration tests."], [287, 1, 1, 0, "FullyModifiedOLS", "Fully Modified OLS cointegrating vector estimation."], [288, 1, 1, 0, "PhillipsOuliarisTestResults", "plot([axes, title])"], [289, 6, 1, 0, "engle_granger", "Test for cointegration within a set of time series."], [290, 6, 1, 0, "phillips_ouliaris", "Test for cointegration within a set of time series."]], "arch.unitroot.cointegration.CanonicalCointegratingReg": [[367, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.__init__": [[282, 2, 2, 0, "trend", "Trend to include in the cointegrating regression."], [282, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [282, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression."], [282, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.fit": [[367, 2, 2, 0, "bandwidth", "The bandwidth to use."], [367, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression."], [367, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [367, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [367, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators."]], "arch.unitroot.cointegration.CointegrationAnalysisResults": [[368, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [369, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [370, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [371, 5, 1, 0, "long_run_variance", "Long-run variance estimate used in the parameter covariance estimator"], [372, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [373, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [374, 5, 1, 0, "resid", "The model residuals"], [375, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [376, 5, 1, 0, "rsquared", "The model R\u00b2"], [377, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [378, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [379, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [380, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.CointegrationAnalysisResults.__init__": [[283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "cov", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "df_adjust", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "estimator_type", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "kernel_est", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "num_x", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "omega_112", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "params", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "resid", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "rsquared", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "rsquared_adj", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "trend", "summary()"]], "arch.unitroot.cointegration.DynamicOLS": [[381, 3, 1, 0, "fit", "Estimate the Dynamic OLS regression"]], "arch.unitroot.cointegration.DynamicOLS.__init__": [[284, 2, 2, 0, "common", "Flag indicating that lags and leads should be restricted to the same value."], [284, 2, 2, 0, "lags", "The number of lags to include in the model."], [284, 2, 2, 0, "leads", "The number of leads to include in the model."], [284, 2, 2, 0, "max_lag", "The maximum lag to consider."], [284, 2, 2, 0, "max_lead", "The maximum lead to consider."], [284, 2, 2, 0, "method", "The method used to select lag length when lags or leads is None."], [284, 2, 2, 0, "trend", "Trend to include in the cointegrating regression."], [284, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [284, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.DynamicOLS.fit": [[381, 2, 2, 0, "bandwidth", "The bandwidth to use."], [381, 2, 2, 0, "cov_type", "Either \"unadjusted\" (or is equivalent \"homoskedastic\") or \"robust\" (or its equivalent \"kernel\")."], [381, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression."], [381, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [381, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators."]], "arch.unitroot.cointegration.DynamicOLSResults": [[382, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [383, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [384, 5, 1, 0, "cov_type", "The type of parameter covariance estimator used"], [385, 5, 1, 0, "full_cov", "Parameter covariance of the all model parameters, incl. leads and lags"], [386, 5, 1, 0, "full_params", "The complete set of parameters, including leads and lags"], [387, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [388, 5, 1, 0, "lags", "The number of lags included in the model"], [389, 5, 1, 0, "leads", "The number of leads included in the model"], [390, 5, 1, 0, "long_run_variance", "The long-run variance of the regression residual."], [391, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [392, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [393, 5, 1, 0, "resid", "The model residuals"], [394, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [395, 5, 1, 0, "rsquared", "The model R\u00b2"], [396, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [397, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [398, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [399, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.DynamicOLSResults.__init__": [[285, 2, 2, 0, "cov", "The estimated parameter covariance."], [285, 2, 2, 0, "cov_type", "The type of the parameter covariance estimator used."], [285, 2, 2, 0, "df_adjust", "Whether to degree of freedom adjust the estimator."], [285, 2, 2, 0, "kernel_est", "The covariance estimator instance used to estimate the parameter covariance."], [285, 2, 2, 0, "lags", "The number of lags included in the model."], [285, 2, 2, 0, "leads", "The number of leads included in the model."], [285, 2, 2, "arch.unitroot.cointegration.DynamicOLSResults", "num_x", "Estimation results for Dynamic OLS models"], [285, 2, 2, 0, "params", "The estimated model parameters."], [285, 2, 2, 0, "reg_results", "Regression results from fitting statsmodels OLS."], [285, 2, 2, 0, "resid", "The model residuals."], [285, 2, 2, "arch.unitroot.cointegration.DynamicOLSResults", "trend", "Estimation results for Dynamic OLS models"]], "arch.unitroot.cointegration.DynamicOLSResults.summary": [[398, 2, 2, 0, "full", "Flag indicating whether to include all estimated parameters (True) or only the parameters of the cointegrating vector"]], "arch.unitroot.cointegration.EngleGrangerTestResults": [[400, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [401, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [402, 5, 1, 0, "critical_values", "Critical Values"], [403, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [404, 5, 1, 0, "lags", "The number of lags used in the Augmented Dickey-Fuller regression."], [405, 5, 1, 0, "max_lags", "The maximum number of lags used in the lag-length selection."], [406, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [407, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [408, 3, 1, 0, "plot", "Plot the cointegration residuals."], [409, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [410, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [411, 5, 1, 0, "rho", "The estimated coefficient in the Dickey-Fuller Test"], [412, 5, 1, 0, "stat", "The test statistic."], [413, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [414, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.EngleGrangerTestResults.__init__": [[286, 2, 2, 0, "adf", "The ADF instance used to perform the test and lag selection."], [286, 2, 2, 0, "alternative", "The alternative hypothesis."], [286, 2, 2, 0, "crit_vals", "The critical values of the Engle-Granger specific to the sample size and model dimension."], [286, 2, 2, 0, "null", "The null hypothesis."], [286, 2, 2, 0, "order", "The number of stochastic trends in the null distribution."], [286, 2, 2, 0, "pvalue", "The pvalue of the Engle-Granger test statistic."], [286, 2, 2, 0, "stat", "The Engle-Granger test statistic."], [286, 2, 2, 0, "trend", "The model's trend description."], [286, 2, 2, 0, "xsection", "The OLS results used in the cross-sectional regression."]], "arch.unitroot.cointegration.EngleGrangerTestResults.plot": [[408, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [408, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.FullyModifiedOLS": [[415, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.FullyModifiedOLS.__init__": [[287, 2, 2, 0, "trend", "Trend to include in the cointegrating regression."], [287, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [287, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression."], [287, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.FullyModifiedOLS.fit": [[415, 2, 2, 0, "bandwidth", "The bandwidth to use."], [415, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression."], [415, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [415, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [415, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators."]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults": [[416, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [417, 5, 1, 0, "bandwidth", "Bandwidth used by the long-run covariance estimator"], [418, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [419, 5, 1, 0, "critical_values", "Critical Values"], [420, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [421, 5, 1, 0, "kernel", "Name of the long-run covariance estimator"], [422, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [423, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [424, 3, 1, 0, "plot", "Plot the cointegration residuals."], [425, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [426, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [427, 5, 1, 0, "stat", "The test statistic."], [428, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [429, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults.__init__": [[288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "alternative", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "crit_vals", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "kernel_est", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "null", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "order", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "pvalue", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "rho", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "stat", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "test_type", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "trend", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "xsection", "plot([axes, title])"]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot": [[424, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [424, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.engle_granger": [[289, 2, 2, 0, "lags", "The number of lagged differences to include in the Augmented Dickey-Fuller test used on the residuals of the"], [289, 2, 2, 0, "max_lags", "The maximum number of lags to consider when using automatic lag-length in the Augmented Dickey-Fuller regression."], [289, 2, 2, 0, "method", "The method used to select the number of lags included in the Augmented Dickey-Fuller regression."], [289, 2, 2, 0, "trend", "Trend to include in the cointegrating regression."], [289, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [289, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.phillips_ouliaris": [[290, 2, 2, 0, "bandwidth", "The bandwidth to use."], [290, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [290, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators."], [290, 2, 2, 0, "test_type", "The test statistic to compute."], [290, 2, 2, 0, "trend", "Trend to include in the cointegrating regression."], [290, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [290, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.univariate": [[438, 1, 1, 0, "APARCH", "Asymmetric Power ARCH (APARCH) volatility process"], [439, 1, 1, 0, "ARCH", "ARCH process"], [440, 1, 1, 0, "ARCHInMean", "(G)ARCH-in-mean model and simulation"], [441, 1, 1, 0, "ARX", "Autoregressive model with optional exogenous regressors estimation and simulation"], [442, 1, 1, 0, "ConstantMean", "Constant mean model estimation and simulation."], [443, 1, 1, 0, "ConstantVariance", "Constant volatility process"], [444, 1, 1, 0, "EGARCH", "EGARCH model estimation"], [445, 1, 1, 0, "EWMAVariance", "Exponentially Weighted Moving-Average (RiskMetrics) Variance process"], [446, 1, 1, 0, "FIGARCH", "FIGARCH model"], [447, 1, 1, 0, "FixedVariance", "Fixed volatility process"], [448, 1, 1, 0, "GARCH", "GARCH and related model estimation"], [449, 1, 1, 0, "GeneralizedError", "Generalized Error distribution for use with ARCH models"], [450, 1, 1, 0, "HARCH", "Heterogeneous ARCH process"], [451, 1, 1, 0, "HARX", "Heterogeneous Autoregression (HAR), with optional exogenous regressors, model estimation and simulation"], [452, 1, 1, 0, "LS", "Least squares model estimation and simulation"], [453, 1, 1, 0, "MIDASHyperbolic", "MIDAS Hyperbolic ARCH process"], [454, 1, 1, 0, "Normal", "Standard normal distribution for use with ARCH models"], [455, 1, 1, 0, "RiskMetrics2006", "RiskMetrics 2006 Variance process"], [456, 1, 1, 0, "SkewStudent", "Standardized Skewed Student's distribution for use with ARCH models"], [457, 1, 1, 0, "StudentsT", "Standardized Student's distribution for use with ARCH models"], [458, 1, 1, 0, "ZeroMean", "Model with zero conditional mean estimation and simulation"], [907, 6, 1, 0, "arch_model", "Initialization of common ARCH model specifications"]], "arch.univariate.APARCH": [[468, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [469, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [470, 3, 1, 0, "bounds", "Returns bounds for parameters"], [471, 5, 1, 0, "common_asym", "The value of delta in the model. NaN is delta is estimated."], [472, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [473, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [474, 5, 1, 0, "delta", "The value of delta in the model. NaN is delta is estimated."], [475, 3, 1, 0, "forecast", "Forecast volatility from the model"], [476, 5, 1, 0, "name", "The name of the volatility process"], [477, 5, 1, 0, "num_params", "The number of parameters in the model"], [478, 3, 1, 0, "parameter_names", "Names of model parameters"], [479, 3, 1, 0, "simulate", "Simulate data from the model"], [480, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [481, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [482, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [483, 3, 1, 0, "update", "Compute the variance for a single observation"], [484, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [485, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [486, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.APARCH.__init__": [[438, 2, 2, 0, "common_asym", "Restrict all asymmetry terms to share the same asymmetry parameter."], [438, 2, 2, 0, "delta", "Value to use for a fixed delta in the APARCH model."], [438, 2, 2, 0, "o", "Order of the asymmetric innovation."], [438, 2, 2, 0, "p", "Order of the symmetric innovation."], [438, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.APARCH.backcast": [[468, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.backcast_transform": [[469, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.APARCH.bounds": [[470, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.compute_variance": [[472, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [472, 2, 2, 0, "parameters", "Model parameters"], [472, 2, 2, 0, "resids", "Vector of mean zero residuals"], [472, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [472, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.forecast": [[475, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [475, 2, 2, 0, "horizon", "Forecast horizon."], [475, 2, 2, 0, "method", "Method to use when producing the forecast."], [475, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [475, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [475, 2, 2, 0, "resids", "Residuals to use in the recursion"], [475, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [475, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [475, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [475, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.simulate": [[479, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [479, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [479, 2, 2, 0, "nobs", "Number of data points to simulate"], [479, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [479, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.APARCH.starting_values": [[481, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.APARCH.update": [[483, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [483, 2, 2, 0, "index", "The numerical index of the variance to compute"], [483, 2, 2, 0, "parameters", "The variance model parameters"], [483, 2, 2, 0, "resids", "The residual array."], [483, 2, 2, 0, "sigma2", "The array containing the variances."], [483, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.variance_bounds": [[485, 2, 2, 0, "power", "Power used in the model."], [485, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCH": [[487, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [488, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [489, 3, 1, 0, "bounds", "Returns bounds for parameters"], [490, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [491, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [492, 3, 1, 0, "forecast", "Forecast volatility from the model"], [493, 5, 1, 0, "name", "The name of the volatility process"], [494, 5, 1, 0, "num_params", "The number of parameters in the model"], [495, 3, 1, 0, "parameter_names", "Names of model parameters"], [496, 3, 1, 0, "simulate", "Simulate data from the model"], [497, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [498, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [499, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [500, 3, 1, 0, "update", "Compute the variance for a single observation"], [501, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [502, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [503, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ARCH.__init__": [[439, 2, 2, 0, "p", "Order of the symmetric innovation"]], "arch.univariate.ARCH.backcast": [[487, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.backcast_transform": [[488, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ARCH.bounds": [[489, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.compute_variance": [[490, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [490, 2, 2, 0, "parameters", "Model parameters"], [490, 2, 2, 0, "resids", "Vector of mean zero residuals"], [490, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [490, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.forecast": [[492, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [492, 2, 2, 0, "horizon", "Forecast horizon."], [492, 2, 2, 0, "method", "Method to use when producing the forecast."], [492, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [492, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [492, 2, 2, 0, "resids", "Residuals to use in the recursion"], [492, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [492, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [492, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [492, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.simulate": [[496, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [496, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [496, 2, 2, 0, "nobs", "Number of data points to simulate"], [496, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [496, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ARCH.starting_values": [[498, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ARCH.update": [[500, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [500, 2, 2, 0, "index", "The numerical index of the variance to compute"], [500, 2, 2, 0, "parameters", "The variance model parameters"], [500, 2, 2, 0, "resids", "The residual array."], [500, 2, 2, 0, "sigma2", "The array containing the variances."], [500, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.variance_bounds": [[502, 2, 2, 0, "power", "Power used in the model."], [502, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCHInMean": [[504, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [505, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [506, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [507, 5, 1, 0, "distribution", "Set or gets the error distribution"], [508, 3, 1, 0, "fit", "Estimate model parameters"], [509, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [510, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [511, 5, 1, 0, "form", "The form of the conditional variance in the mean"], [512, 5, 1, 0, "name", "The name of the model."], [513, 5, 1, 0, "num_params", "Returns the number of parameters"], [514, 3, 1, 0, "parameter_names", "List of parameters names"], [515, 3, 1, 0, "resids", "Compute model residuals"], [516, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [517, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [518, 5, 1, 0, "volatility", "Set or gets the volatility process"], [519, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [520, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARCHInMean.__init__": [[440, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [440, 2, 2, 0, "distribution", "Error distribution to use in the model"], [440, 2, 2, 0, "form", "The form of the conditional variance that appears in the mean equation."], [440, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters."], [440, 2, 2, 0, "lags", "Description of lag structure of the HAR."], [440, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation."], [440, 2, 2, 0, "volatility", "Volatility process to use in the model."], [440, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [440, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARCHInMean.compute_param_cov": [[505, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [505, 2, 2, 0, "params", "Model parameters"], [505, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARCHInMean.fit": [[508, 2, 2, 0, "backcast", "Value to use as backcast."], [508, 2, 2, 0, "cov_type", "Estimation method of parameter covariance."], [508, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing."], [508, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [508, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [508, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize."], [508, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [508, 2, 2, 0, "starting_values", "Array of starting values to use."], [508, 2, 2, 0, "tol", "Tolerance for termination."], [508, 2, 2, 0, "update_freq", "Frequency of iteration updates."]], "arch.univariate.ARCHInMean.fix": [[509, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [509, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [509, 2, 2, 0, "params", "User specified parameters to use when generating the result."]], "arch.univariate.ARCHInMean.forecast": [[510, 2, 2, 0, "align", "Either 'origin' or 'target'."], [510, 2, 2, 0, "horizon", "Number of steps to forecast"], [510, 2, 2, 0, "method", "Method to use when producing the forecast."], [510, 2, 2, 0, "params", "Parameters required to forecast."], [510, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [510, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [510, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [510, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [510, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [510, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.ARCHInMean.resids": [[515, 2, 2, 0, "params", "Model parameters"], [515, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [515, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARCHInMean.simulate": [[516, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [516, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model."], [516, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [516, 2, 2, 0, "nobs", "Length of series to simulate"], [516, 2, 2, 0, "params", "Parameters to use when simulating the model."], [516, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ARX": [[521, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [522, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [523, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [524, 5, 1, 0, "distribution", "Set or gets the error distribution"], [525, 3, 1, 0, "fit", "Estimate model parameters"], [526, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [527, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [528, 5, 1, 0, "name", "The name of the model."], [529, 5, 1, 0, "num_params", "Returns the number of parameters"], [530, 3, 1, 0, "parameter_names", "List of parameters names"], [531, 3, 1, 0, "resids", "Compute model residuals"], [532, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [533, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [534, 5, 1, 0, "volatility", "Set or gets the volatility process"], [535, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [536, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARX.__init__": [[441, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [441, 2, 2, 0, "distribution", "Error distribution to use in the model"], [441, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters."], [441, 2, 2, 0, "lags", "Description of lag structure of the HAR."], [441, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation."], [441, 2, 2, 0, "volatility", "Volatility process to use in the model"], [441, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [441, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARX.compute_param_cov": [[522, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [522, 2, 2, 0, "params", "Model parameters"], [522, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARX.fit": [[525, 2, 2, 0, "backcast", "Value to use as backcast."], [525, 2, 2, 0, "cov_type", "Estimation method of parameter covariance."], [525, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing."], [525, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [525, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [525, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize."], [525, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [525, 2, 2, 0, "starting_values", "Array of starting values to use."], [525, 2, 2, 0, "tol", "Tolerance for termination."], [525, 2, 2, 0, "update_freq", "Frequency of iteration updates."]], "arch.univariate.ARX.fix": [[526, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [526, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [526, 2, 2, 0, "params", "User specified parameters to use when generating the result."]], "arch.univariate.ARX.forecast": [[527, 2, 2, 0, "align", "Either 'origin' or 'target'."], [527, 2, 2, 0, "horizon", "Number of steps to forecast"], [527, 2, 2, 0, "method", "Method to use when producing the forecast."], [527, 2, 2, 0, "params", "Parameters required to forecast."], [527, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [527, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [527, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [527, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [527, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [527, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.ARX.resids": [[531, 2, 2, 0, "params", "Model parameters"], [531, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [531, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARX.simulate": [[532, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [532, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model."], [532, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [532, 2, 2, 0, "nobs", "Length of series to simulate"], [532, 2, 2, 0, "params", "Parameters to use when simulating the model."], [532, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ConstantMean": [[537, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [538, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [539, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [540, 5, 1, 0, "distribution", "Set or gets the error distribution"], [541, 3, 1, 0, "fit", "Estimate model parameters"], [542, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [543, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [544, 5, 1, 0, "name", "The name of the model."], [545, 5, 1, 0, "num_params", "Returns the number of parameters"], [546, 3, 1, 0, "parameter_names", "List of parameters names"], [547, 3, 1, 0, "resids", "Compute model residuals"], [548, 3, 1, 0, "simulate", "Simulated data from a constant mean model"], [549, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [550, 5, 1, 0, "volatility", "Set or gets the volatility process"], [551, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [552, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ConstantMean.__init__": [[442, 2, 2, 0, "distribution", "Error distribution to use in the model"], [442, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters."], [442, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation."], [442, 2, 2, 0, "volatility", "Volatility process to use in the model"], [442, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ConstantMean.compute_param_cov": [[538, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [538, 2, 2, 0, "params", "Model parameters"], [538, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ConstantMean.fit": [[541, 2, 2, 0, "backcast", "Value to use as backcast."], [541, 2, 2, 0, "cov_type", "Estimation method of parameter covariance."], [541, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing."], [541, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [541, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [541, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize."], [541, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [541, 2, 2, 0, "starting_values", "Array of starting values to use."], [541, 2, 2, 0, "tol", "Tolerance for termination."], [541, 2, 2, 0, "update_freq", "Frequency of iteration updates."]], "arch.univariate.ConstantMean.fix": [[542, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [542, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [542, 2, 2, 0, "params", "User specified parameters to use when generating the result."]], "arch.univariate.ConstantMean.forecast": [[543, 2, 2, 0, "align", "Either 'origin' or 'target'."], [543, 2, 2, 0, "horizon", "Number of steps to forecast"], [543, 2, 2, 0, "method", "Method to use when producing the forecast."], [543, 2, 2, 0, "params", "Parameters required to forecast."], [543, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [543, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [543, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [543, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [543, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [543, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.ConstantMean.resids": [[547, 2, 2, 0, "params", "Model parameters"], [547, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [547, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ConstantMean.simulate": [[548, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [548, 2, 2, 0, "initial_value", "This value is not used."], [548, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [548, 2, 2, 0, "nobs", "Length of series to simulate"], [548, 2, 2, 0, "params", "Parameters to use when simulating the model."], [548, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.ConstantVariance": [[553, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [554, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [555, 3, 1, 0, "bounds", "Returns bounds for parameters"], [556, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [557, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [558, 3, 1, 0, "forecast", "Forecast volatility from the model"], [559, 5, 1, 0, "name", "The name of the volatility process"], [560, 5, 1, 0, "num_params", "The number of parameters in the model"], [561, 3, 1, 0, "parameter_names", "Names of model parameters"], [562, 3, 1, 0, "simulate", "Simulate data from the model"], [563, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [564, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [565, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [566, 3, 1, 0, "update", "Compute the variance for a single observation"], [567, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [568, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [569, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ConstantVariance.backcast": [[553, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.backcast_transform": [[554, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ConstantVariance.bounds": [[555, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.compute_variance": [[556, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [556, 2, 2, 0, "parameters", "Model parameters"], [556, 2, 2, 0, "resids", "Vector of mean zero residuals"], [556, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [556, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.forecast": [[558, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [558, 2, 2, 0, "horizon", "Forecast horizon."], [558, 2, 2, 0, "method", "Method to use when producing the forecast."], [558, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [558, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [558, 2, 2, 0, "resids", "Residuals to use in the recursion"], [558, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [558, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [558, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [558, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.simulate": [[562, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [562, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [562, 2, 2, 0, "nobs", "Number of data points to simulate"], [562, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [562, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ConstantVariance.starting_values": [[564, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ConstantVariance.update": [[566, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [566, 2, 2, 0, "index", "The numerical index of the variance to compute"], [566, 2, 2, 0, "parameters", "The variance model parameters"], [566, 2, 2, 0, "resids", "The residual array."], [566, 2, 2, 0, "sigma2", "The array containing the variances."], [566, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.variance_bounds": [[568, 2, 2, 0, "power", "Power used in the model."], [568, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EGARCH": [[570, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [571, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [572, 3, 1, 0, "bounds", "Returns bounds for parameters"], [573, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [574, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [575, 3, 1, 0, "forecast", "Forecast volatility from the model"], [576, 5, 1, 0, "name", "The name of the volatility process"], [577, 5, 1, 0, "num_params", "The number of parameters in the model"], [578, 3, 1, 0, "parameter_names", "Names of model parameters"], [579, 3, 1, 0, "simulate", "Simulate data from the model"], [580, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [581, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [582, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [583, 3, 1, 0, "update", "Compute the variance for a single observation"], [584, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [585, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [586, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EGARCH.__init__": [[444, 2, 2, 0, "o", "Order of the asymmetric innovation"], [444, 2, 2, 0, "p", "Order of the symmetric innovation"], [444, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.EGARCH.backcast": [[570, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.backcast_transform": [[571, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EGARCH.bounds": [[572, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.compute_variance": [[573, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [573, 2, 2, 0, "parameters", "Model parameters"], [573, 2, 2, 0, "resids", "Vector of mean zero residuals"], [573, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [573, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.forecast": [[575, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [575, 2, 2, 0, "horizon", "Forecast horizon."], [575, 2, 2, 0, "method", "Method to use when producing the forecast."], [575, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [575, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [575, 2, 2, 0, "resids", "Residuals to use in the recursion"], [575, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [575, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [575, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [575, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.simulate": [[579, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [579, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [579, 2, 2, 0, "nobs", "Number of data points to simulate"], [579, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [579, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EGARCH.starting_values": [[581, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EGARCH.update": [[583, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [583, 2, 2, 0, "index", "The numerical index of the variance to compute"], [583, 2, 2, 0, "parameters", "The variance model parameters"], [583, 2, 2, 0, "resids", "The residual array."], [583, 2, 2, 0, "sigma2", "The array containing the variances."], [583, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.variance_bounds": [[585, 2, 2, 0, "power", "Power used in the model."], [585, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EWMAVariance": [[587, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [588, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [589, 3, 1, 0, "bounds", "Returns bounds for parameters"], [590, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [591, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [592, 3, 1, 0, "forecast", "Forecast volatility from the model"], [593, 5, 1, 0, "name", "The name of the volatility process"], [594, 5, 1, 0, "num_params", "The number of parameters in the model"], [595, 3, 1, 0, "parameter_names", "Names of model parameters"], [596, 3, 1, 0, "simulate", "Simulate data from the model"], [597, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [598, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [599, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [600, 3, 1, 0, "update", "Compute the variance for a single observation"], [601, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [602, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [603, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EWMAVariance.__init__": [[445, 2, 2, 0, "lam", "Smoothing parameter."]], "arch.univariate.EWMAVariance.backcast": [[587, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.backcast_transform": [[588, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EWMAVariance.bounds": [[589, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.compute_variance": [[590, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [590, 2, 2, 0, "parameters", "Model parameters"], [590, 2, 2, 0, "resids", "Vector of mean zero residuals"], [590, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [590, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.forecast": [[592, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [592, 2, 2, 0, "horizon", "Forecast horizon."], [592, 2, 2, 0, "method", "Method to use when producing the forecast."], [592, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [592, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [592, 2, 2, 0, "resids", "Residuals to use in the recursion"], [592, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [592, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [592, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [592, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.simulate": [[596, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [596, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [596, 2, 2, 0, "nobs", "Number of data points to simulate"], [596, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [596, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EWMAVariance.starting_values": [[598, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EWMAVariance.update": [[600, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [600, 2, 2, 0, "index", "The numerical index of the variance to compute"], [600, 2, 2, 0, "parameters", "The variance model parameters"], [600, 2, 2, 0, "resids", "The residual array."], [600, 2, 2, 0, "sigma2", "The array containing the variances."], [600, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.variance_bounds": [[602, 2, 2, 0, "power", "Power used in the model."], [602, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FIGARCH": [[604, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [605, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [606, 3, 1, 0, "bounds", "Returns bounds for parameters"], [607, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [608, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [609, 3, 1, 0, "forecast", "Forecast volatility from the model"], [610, 5, 1, 0, "name", "The name of the volatility process"], [611, 5, 1, 0, "num_params", "The number of parameters in the model"], [612, 3, 1, 0, "parameter_names", "Names of model parameters"], [613, 3, 1, 0, "simulate", "Simulate data from the model"], [614, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [615, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [616, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [617, 5, 1, 0, "truncation", "Truncation lag for the ARCH-infinity approximation"], [618, 3, 1, 0, "update", "Compute the variance for a single observation"], [619, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [620, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [621, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FIGARCH.__init__": [[446, 2, 2, 0, "p", "Order of the symmetric innovation"], [446, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power."], [446, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"], [446, 2, 2, 0, "truncation", "Truncation point to use in ARCH(\\infty) representation. Default is 1000."]], "arch.univariate.FIGARCH.backcast": [[604, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.backcast_transform": [[605, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FIGARCH.bounds": [[606, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.compute_variance": [[607, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [607, 2, 2, 0, "parameters", "Model parameters"], [607, 2, 2, 0, "resids", "Vector of mean zero residuals"], [607, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [607, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.forecast": [[609, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [609, 2, 2, 0, "horizon", "Forecast horizon."], [609, 2, 2, 0, "method", "Method to use when producing the forecast."], [609, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [609, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [609, 2, 2, 0, "resids", "Residuals to use in the recursion"], [609, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [609, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [609, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [609, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.simulate": [[613, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [613, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [613, 2, 2, 0, "nobs", "Number of data points to simulate"], [613, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [613, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FIGARCH.starting_values": [[615, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FIGARCH.update": [[618, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [618, 2, 2, 0, "index", "The numerical index of the variance to compute"], [618, 2, 2, 0, "parameters", "The variance model parameters"], [618, 2, 2, 0, "resids", "The residual array."], [618, 2, 2, 0, "sigma2", "The array containing the variances."], [618, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.variance_bounds": [[620, 2, 2, 0, "power", "Power used in the model."], [620, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FixedVariance": [[622, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [623, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [624, 3, 1, 0, "bounds", "Returns bounds for parameters"], [625, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [626, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [627, 3, 1, 0, "forecast", "Forecast volatility from the model"], [628, 5, 1, 0, "name", "The name of the volatility process"], [629, 5, 1, 0, "num_params", "The number of parameters in the model"], [630, 3, 1, 0, "parameter_names", "Names of model parameters"], [631, 3, 1, 0, "simulate", "Simulate data from the model"], [632, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [633, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [634, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [635, 3, 1, 0, "update", "Compute the variance for a single observation"], [636, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [637, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [638, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FixedVariance.__init__": [[447, 2, 2, 0, "unit_scale", "Flag whether to enforce a unit scale."], [447, 2, 2, 0, "variance", "Array containing the variances to use."]], "arch.univariate.FixedVariance.backcast": [[622, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.backcast_transform": [[623, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FixedVariance.bounds": [[624, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.compute_variance": [[625, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [625, 2, 2, 0, "parameters", "Model parameters"], [625, 2, 2, 0, "resids", "Vector of mean zero residuals"], [625, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [625, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.forecast": [[627, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [627, 2, 2, 0, "horizon", "Forecast horizon."], [627, 2, 2, 0, "method", "Method to use when producing the forecast."], [627, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [627, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [627, 2, 2, 0, "resids", "Residuals to use in the recursion"], [627, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [627, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [627, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [627, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.simulate": [[631, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [631, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [631, 2, 2, 0, "nobs", "Number of data points to simulate"], [631, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [631, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FixedVariance.starting_values": [[633, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FixedVariance.update": [[635, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [635, 2, 2, 0, "index", "The numerical index of the variance to compute"], [635, 2, 2, 0, "parameters", "The variance model parameters"], [635, 2, 2, 0, "resids", "The residual array."], [635, 2, 2, 0, "sigma2", "The array containing the variances."], [635, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.variance_bounds": [[637, 2, 2, 0, "power", "Power used in the model."], [637, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GARCH": [[639, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [640, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [641, 3, 1, 0, "bounds", "Returns bounds for parameters"], [642, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [643, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [644, 3, 1, 0, "forecast", "Forecast volatility from the model"], [645, 5, 1, 0, "name", "The name of the volatility process"], [646, 5, 1, 0, "num_params", "The number of parameters in the model"], [647, 3, 1, 0, "parameter_names", "Names of model parameters"], [648, 3, 1, 0, "simulate", "Simulate data from the model"], [649, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [650, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [651, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [652, 3, 1, 0, "update", "Compute the variance for a single observation"], [653, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [654, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [655, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.GARCH.__init__": [[448, 2, 2, 0, "o", "Order of the asymmetric innovation"], [448, 2, 2, 0, "p", "Order of the symmetric innovation"], [448, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power."], [448, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.GARCH.backcast": [[639, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.backcast_transform": [[640, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.GARCH.bounds": [[641, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.compute_variance": [[642, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [642, 2, 2, 0, "parameters", "Model parameters"], [642, 2, 2, 0, "resids", "Vector of mean zero residuals"], [642, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [642, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.forecast": [[644, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [644, 2, 2, 0, "horizon", "Forecast horizon."], [644, 2, 2, 0, "method", "Method to use when producing the forecast."], [644, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [644, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [644, 2, 2, 0, "resids", "Residuals to use in the recursion"], [644, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [644, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [644, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [644, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.simulate": [[648, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [648, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [648, 2, 2, 0, "nobs", "Number of data points to simulate"], [648, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [648, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.GARCH.starting_values": [[650, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.GARCH.update": [[652, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [652, 2, 2, 0, "index", "The numerical index of the variance to compute"], [652, 2, 2, 0, "parameters", "The variance model parameters"], [652, 2, 2, 0, "resids", "The residual array."], [652, 2, 2, 0, "sigma2", "The array containing the variances."], [652, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.variance_bounds": [[654, 2, 2, 0, "power", "Power used in the model."], [654, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GeneralizedError": [[656, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [657, 3, 1, 0, "cdf", "Cumulative distribution function"], [658, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [659, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [660, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a Generalized Error Distribution, conditional on the variance."], [661, 3, 1, 0, "moment", "Moment of order n"], [662, 5, 1, 0, "name", "The name of the distribution"], [663, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [664, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [665, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [666, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [667, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [668, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.GeneralizedError.__init__": [[449, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated."], [449, 2, 2, 0, "seed", "Random number generator instance or int to use."]], "arch.univariate.GeneralizedError.bounds": [[656, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.GeneralizedError.cdf": [[657, 2, 2, 0, "parameters", "Distribution parameters."], [657, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.GeneralizedError.loglikelihood": [[660, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [660, 2, 2, 0, "parameters", "Shape parameter of the GED distribution"], [660, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [660, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.GeneralizedError.moment": [[661, 2, 2, 0, "n", "Order of moment"], [661, 2, 2, 0, "parameters", "Distribution parameters."]], "arch.univariate.GeneralizedError.partial_moment": [[664, 2, 2, 0, "n", "Order of partial moment"], [664, 2, 2, 0, "parameters", "Distribution parameters."], [664, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.GeneralizedError.ppf": [[665, 2, 2, 0, "parameters", "Distribution parameters."], [665, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.GeneralizedError.simulate": [[667, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.GeneralizedError.starting_values": [[668, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.HARCH": [[669, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [670, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [671, 3, 1, 0, "bounds", "Returns bounds for parameters"], [672, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [673, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [674, 3, 1, 0, "forecast", "Forecast volatility from the model"], [675, 5, 1, 0, "name", "The name of the volatility process"], [676, 5, 1, 0, "num_params", "The number of parameters in the model"], [677, 3, 1, 0, "parameter_names", "Names of model parameters"], [678, 3, 1, 0, "simulate", "Simulate data from the model"], [679, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [680, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [681, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [682, 3, 1, 0, "update", "Compute the variance for a single observation"], [683, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [684, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [685, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.HARCH.__init__": [[450, 2, 2, 0, "lags", "List of lags to include in the model, or if scalar, includes all lags up the value"]], "arch.univariate.HARCH.backcast": [[669, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.backcast_transform": [[670, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.HARCH.bounds": [[671, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.compute_variance": [[672, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [672, 2, 2, 0, "parameters", "Model parameters"], [672, 2, 2, 0, "resids", "Vector of mean zero residuals"], [672, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [672, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.forecast": [[674, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [674, 2, 2, 0, "horizon", "Forecast horizon."], [674, 2, 2, 0, "method", "Method to use when producing the forecast."], [674, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [674, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [674, 2, 2, 0, "resids", "Residuals to use in the recursion"], [674, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [674, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [674, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [674, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.simulate": [[678, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [678, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [678, 2, 2, 0, "nobs", "Number of data points to simulate"], [678, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [678, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.HARCH.starting_values": [[680, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.HARCH.update": [[682, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [682, 2, 2, 0, "index", "The numerical index of the variance to compute"], [682, 2, 2, 0, "parameters", "The variance model parameters"], [682, 2, 2, 0, "resids", "The residual array."], [682, 2, 2, 0, "sigma2", "The array containing the variances."], [682, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.variance_bounds": [[684, 2, 2, 0, "power", "Power used in the model."], [684, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.HARX": [[686, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [687, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [688, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [689, 5, 1, 0, "distribution", "Set or gets the error distribution"], [690, 3, 1, 0, "fit", "Estimate model parameters"], [691, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [692, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [693, 5, 1, 0, "name", "The name of the model."], [694, 5, 1, 0, "num_params", "Returns the number of parameters"], [695, 3, 1, 0, "parameter_names", "List of parameters names"], [696, 3, 1, 0, "resids", "Compute model residuals"], [697, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [698, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [699, 5, 1, 0, "volatility", "Set or gets the volatility process"], [700, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [701, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.HARX.__init__": [[451, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [451, 2, 2, 0, "distribution", "Error distribution to use in the model"], [451, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters."], [451, 2, 2, 0, "lags", "Description of lag structure of the HAR."], [451, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation."], [451, 2, 2, 0, "use_rotated", "Flag indicating to use the alternative rotated form of the HAR where HAR lags do not overlap"], [451, 2, 2, 0, "volatility", "Volatility process to use in the model"], [451, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [451, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.HARX.compute_param_cov": [[687, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [687, 2, 2, 0, "params", "Model parameters"], [687, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.HARX.fit": [[690, 2, 2, 0, "backcast", "Value to use as backcast."], [690, 2, 2, 0, "cov_type", "Estimation method of parameter covariance."], [690, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing."], [690, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [690, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [690, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize."], [690, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [690, 2, 2, 0, "starting_values", "Array of starting values to use."], [690, 2, 2, 0, "tol", "Tolerance for termination."], [690, 2, 2, 0, "update_freq", "Frequency of iteration updates."]], "arch.univariate.HARX.fix": [[691, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [691, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [691, 2, 2, 0, "params", "User specified parameters to use when generating the result."]], "arch.univariate.HARX.forecast": [[692, 2, 2, 0, "align", "Either 'origin' or 'target'."], [692, 2, 2, 0, "horizon", "Number of steps to forecast"], [692, 2, 2, 0, "method", "Method to use when producing the forecast."], [692, 2, 2, 0, "params", "Parameters required to forecast."], [692, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [692, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [692, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [692, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [692, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [692, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.HARX.resids": [[696, 2, 2, 0, "params", "Model parameters"], [696, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [696, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.HARX.simulate": [[697, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [697, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model."], [697, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [697, 2, 2, 0, "nobs", "Length of series to simulate"], [697, 2, 2, 0, "params", "Parameters to use when simulating the model."], [697, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.LS": [[702, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [703, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [704, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [705, 5, 1, 0, "distribution", "Set or gets the error distribution"], [706, 3, 1, 0, "fit", "Estimate model parameters"], [707, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [708, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [709, 5, 1, 0, "name", "The name of the model."], [710, 5, 1, 0, "num_params", "Returns the number of parameters"], [711, 3, 1, 0, "parameter_names", "List of parameters names"], [712, 3, 1, 0, "resids", "Compute model residuals"], [713, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [714, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [715, 5, 1, 0, "volatility", "Set or gets the volatility process"], [716, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [717, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.LS.__init__": [[452, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [452, 2, 2, 0, "distribution", "Error distribution to use in the model"], [452, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters."], [452, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation."], [452, 2, 2, 0, "volatility", "Volatility process to use in the model"], [452, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [452, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.LS.compute_param_cov": [[703, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [703, 2, 2, 0, "params", "Model parameters"], [703, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.LS.fit": [[706, 2, 2, 0, "backcast", "Value to use as backcast."], [706, 2, 2, 0, "cov_type", "Estimation method of parameter covariance."], [706, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing."], [706, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [706, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [706, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize."], [706, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [706, 2, 2, 0, "starting_values", "Array of starting values to use."], [706, 2, 2, 0, "tol", "Tolerance for termination."], [706, 2, 2, 0, "update_freq", "Frequency of iteration updates."]], "arch.univariate.LS.fix": [[707, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [707, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [707, 2, 2, 0, "params", "User specified parameters to use when generating the result."]], "arch.univariate.LS.forecast": [[708, 2, 2, 0, "align", "Either 'origin' or 'target'."], [708, 2, 2, 0, "horizon", "Number of steps to forecast"], [708, 2, 2, 0, "method", "Method to use when producing the forecast."], [708, 2, 2, 0, "params", "Parameters required to forecast."], [708, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [708, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [708, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [708, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [708, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [708, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.LS.resids": [[712, 2, 2, 0, "params", "Model parameters"], [712, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [712, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.LS.simulate": [[713, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [713, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model."], [713, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [713, 2, 2, 0, "nobs", "Length of series to simulate"], [713, 2, 2, 0, "params", "Parameters to use when simulating the model."], [713, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.MIDASHyperbolic": [[718, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [719, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [720, 3, 1, 0, "bounds", "Returns bounds for parameters"], [721, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [722, 3, 1, 0, "constraints", "Constraints"], [723, 3, 1, 0, "forecast", "Forecast volatility from the model"], [724, 5, 1, 0, "name", "The name of the volatility process"], [725, 5, 1, 0, "num_params", "The number of parameters in the model"], [726, 3, 1, 0, "parameter_names", "Names of model parameters"], [727, 3, 1, 0, "simulate", "Simulate data from the model"], [728, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [729, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [730, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [731, 3, 1, 0, "update", "Compute the variance for a single observation"], [732, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [733, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [734, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.MIDASHyperbolic.__init__": [[453, 2, 2, 0, "asym", "Flag indicating whether to include an asymmetric term"], [453, 2, 2, 0, "m", "Length of maximum lag to include in the model"]], "arch.univariate.MIDASHyperbolic.backcast": [[718, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.backcast_transform": [[719, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.MIDASHyperbolic.bounds": [[720, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.compute_variance": [[721, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [721, 2, 2, 0, "parameters", "Model parameters"], [721, 2, 2, 0, "resids", "Vector of mean zero residuals"], [721, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [721, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.forecast": [[723, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [723, 2, 2, 0, "horizon", "Forecast horizon."], [723, 2, 2, 0, "method", "Method to use when producing the forecast."], [723, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [723, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [723, 2, 2, 0, "resids", "Residuals to use in the recursion"], [723, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [723, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [723, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [723, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.simulate": [[727, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [727, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [727, 2, 2, 0, "nobs", "Number of data points to simulate"], [727, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [727, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.MIDASHyperbolic.starting_values": [[729, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.MIDASHyperbolic.update": [[731, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [731, 2, 2, 0, "index", "The numerical index of the variance to compute"], [731, 2, 2, 0, "parameters", "The variance model parameters"], [731, 2, 2, 0, "resids", "The residual array."], [731, 2, 2, 0, "sigma2", "The array containing the variances."], [731, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.variance_bounds": [[733, 2, 2, 0, "power", "Power used in the model."], [733, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.Normal": [[735, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [736, 3, 1, 0, "cdf", "Cumulative distribution function"], [737, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [738, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [739, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are normally distributed, conditional on the variance"], [740, 3, 1, 0, "moment", "Moment of order n"], [741, 5, 1, 0, "name", "The name of the distribution"], [742, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [743, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [744, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [745, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [746, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [747, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.Normal.__init__": [[454, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated."], [454, 2, 2, 0, "seed", "Random number generator instance or int to use."]], "arch.univariate.Normal.bounds": [[735, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.Normal.cdf": [[736, 2, 2, 0, "parameters", "Distribution parameters."], [736, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.Normal.loglikelihood": [[739, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [739, 2, 2, 0, "parameters", "The normal likelihood has no shape parameters."], [739, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [739, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.Normal.moment": [[740, 2, 2, 0, "n", "Order of moment"], [740, 2, 2, 0, "parameters", "Distribution parameters."]], "arch.univariate.Normal.partial_moment": [[743, 2, 2, 0, "n", "Order of partial moment"], [743, 2, 2, 0, "parameters", "Distribution parameters."], [743, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.Normal.ppf": [[744, 2, 2, 0, "parameters", "Distribution parameters."], [744, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.Normal.simulate": [[746, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.Normal.starting_values": [[747, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.RiskMetrics2006": [[748, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [749, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [750, 3, 1, 0, "bounds", "Returns bounds for parameters"], [751, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [752, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [753, 3, 1, 0, "forecast", "Forecast volatility from the model"], [754, 5, 1, 0, "name", "The name of the volatility process"], [755, 5, 1, 0, "num_params", "The number of parameters in the model"], [756, 3, 1, 0, "parameter_names", "Names of model parameters"], [757, 3, 1, 0, "simulate", "Simulate data from the model"], [758, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [759, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [760, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [761, 3, 1, 0, "update", "Compute the variance for a single observation"], [762, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [763, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [764, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.RiskMetrics2006.__init__": [[455, 2, 2, 0, "kmax", "Number of components."], [455, 2, 2, 0, "rho", "Relative scale of adjacent cycles."], [455, 2, 2, 0, "tau0", "Length of long cycle."], [455, 2, 2, 0, "tau1", "Length of short cycle."]], "arch.univariate.RiskMetrics2006.backcast": [[748, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.backcast_transform": [[749, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.RiskMetrics2006.bounds": [[750, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.compute_variance": [[751, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [751, 2, 2, 0, "parameters", "Model parameters"], [751, 2, 2, 0, "resids", "Vector of mean zero residuals"], [751, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [751, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.forecast": [[753, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [753, 2, 2, 0, "horizon", "Forecast horizon."], [753, 2, 2, 0, "method", "Method to use when producing the forecast."], [753, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [753, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [753, 2, 2, 0, "resids", "Residuals to use in the recursion"], [753, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [753, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [753, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [753, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.simulate": [[757, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [757, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [757, 2, 2, 0, "nobs", "Number of data points to simulate"], [757, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [757, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.RiskMetrics2006.starting_values": [[759, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.RiskMetrics2006.update": [[761, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [761, 2, 2, 0, "index", "The numerical index of the variance to compute"], [761, 2, 2, 0, "parameters", "The variance model parameters"], [761, 2, 2, 0, "resids", "The residual array."], [761, 2, 2, 0, "sigma2", "The array containing the variances."], [761, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.variance_bounds": [[763, 2, 2, 0, "power", "Power used in the model."], [763, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.SkewStudent": [[765, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [766, 3, 1, 0, "cdf", "Cumulative distribution function"], [767, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [768, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [769, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Skew Student's t distribution, conditional on the variance."], [770, 3, 1, 0, "moment", "Moment of order n"], [771, 5, 1, 0, "name", "The name of the distribution"], [772, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [773, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [774, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [775, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [776, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [777, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.SkewStudent.__init__": [[456, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated."], [456, 2, 2, 0, "seed", "Random number generator instance or int to use."]], "arch.univariate.SkewStudent.bounds": [[765, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.SkewStudent.cdf": [[766, 2, 2, 0, "parameters", "Distribution parameters."], [766, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.SkewStudent.loglikelihood": [[769, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [769, 2, 2, 0, "parameters", "Shape parameter of the skew-t distribution"], [769, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [769, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.SkewStudent.moment": [[770, 2, 2, 0, "n", "Order of moment"], [770, 2, 2, 0, "parameters", "Distribution parameters."]], "arch.univariate.SkewStudent.partial_moment": [[773, 2, 2, 0, "n", "Order of partial moment"], [773, 2, 2, 0, "parameters", "Distribution parameters."], [773, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.SkewStudent.ppf": [[774, 2, 2, 0, "parameters", "Distribution parameters."], [774, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.SkewStudent.simulate": [[776, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.SkewStudent.starting_values": [[777, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.StudentsT": [[778, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [779, 3, 1, 0, "cdf", "Cumulative distribution function"], [780, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [781, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [782, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Student's t distribution, conditional on the variance."], [783, 3, 1, 0, "moment", "Moment of order n"], [784, 5, 1, 0, "name", "The name of the distribution"], [785, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [786, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [787, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [788, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [789, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [790, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.StudentsT.__init__": [[457, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated."], [457, 2, 2, 0, "seed", "Random number generator instance or int to use."]], "arch.univariate.StudentsT.bounds": [[778, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.StudentsT.cdf": [[779, 2, 2, 0, "parameters", "Distribution parameters."], [779, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.StudentsT.loglikelihood": [[782, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [782, 2, 2, 0, "parameters", "Shape parameter of the t distribution"], [782, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [782, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.StudentsT.moment": [[783, 2, 2, 0, "n", "Order of moment"], [783, 2, 2, 0, "parameters", "Distribution parameters."]], "arch.univariate.StudentsT.partial_moment": [[786, 2, 2, 0, "n", "Order of partial moment"], [786, 2, 2, 0, "parameters", "Distribution parameters."], [786, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.StudentsT.ppf": [[787, 2, 2, 0, "parameters", "Distribution parameters."], [787, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.StudentsT.simulate": [[789, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.StudentsT.starting_values": [[790, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.ZeroMean": [[791, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [792, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [793, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [794, 5, 1, 0, "distribution", "Set or gets the error distribution"], [795, 3, 1, 0, "fit", "Estimate model parameters"], [796, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [797, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [798, 5, 1, 0, "name", "The name of the model."], [799, 5, 1, 0, "num_params", "Returns the number of parameters"], [800, 3, 1, 0, "parameter_names", "List of parameters names"], [801, 3, 1, 0, "resids", "Compute model residuals"], [802, 3, 1, 0, "simulate", "Simulated data from a zero mean model"], [803, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [804, 5, 1, 0, "volatility", "Set or gets the volatility process"], [805, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [806, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ZeroMean.__init__": [[458, 2, 2, 0, "distribution", "Error distribution to use in the model"], [458, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters."], [458, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation."], [458, 2, 2, 0, "volatility", "Volatility process to use in the model"], [458, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ZeroMean.compute_param_cov": [[792, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [792, 2, 2, 0, "params", "Model parameters"], [792, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ZeroMean.fit": [[795, 2, 2, 0, "backcast", "Value to use as backcast."], [795, 2, 2, 0, "cov_type", "Estimation method of parameter covariance."], [795, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing."], [795, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [795, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [795, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize."], [795, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [795, 2, 2, 0, "starting_values", "Array of starting values to use."], [795, 2, 2, 0, "tol", "Tolerance for termination."], [795, 2, 2, 0, "update_freq", "Frequency of iteration updates."]], "arch.univariate.ZeroMean.fix": [[796, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [796, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [796, 2, 2, 0, "params", "User specified parameters to use when generating the result."]], "arch.univariate.ZeroMean.forecast": [[797, 2, 2, 0, "align", "Either 'origin' or 'target'."], [797, 2, 2, 0, "horizon", "Number of steps to forecast"], [797, 2, 2, 0, "method", "Method to use when producing the forecast."], [797, 2, 2, 0, "params", "Parameters required to forecast."], [797, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [797, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [797, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [797, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [797, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [797, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.ZeroMean.resids": [[801, 2, 2, 0, "params", "Model parameters"], [801, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [801, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ZeroMean.simulate": [[802, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [802, 2, 2, 0, "initial_value", "This value is not used."], [802, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [802, 2, 2, 0, "nobs", "Length of series to simulate"], [802, 2, 2, 0, "params", "Parameters to use when simulating the model."], [802, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.arch_model": [[907, 2, 2, 0, "dist", "Name of the error distribution."], [907, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters."], [907, 2, 2, 0, "lags", "Either a scalar integer value indicating lag length or a list of integers specifying lag locations."], [907, 2, 2, 0, "mean", "Name of the mean model."], [907, 2, 2, 0, "o", "Lag order of the asymmetric innovation"], [907, 2, 2, 0, "p", "Lag order of the symmetric innovation"], [907, 2, 2, 0, "power", "Power to use with GARCH and related models"], [907, 2, 2, 0, "q", "Lag order of lagged volatility or equivalent"], [907, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters."], [907, 2, 2, 0, "vol", "Name of the volatility model."], [907, 2, 2, 0, "x", "Exogenous regressors."], [907, 2, 2, 0, "y", "The dependent variable"]], "arch.univariate.base": [[459, 1, 1, 0, "ARCHModel", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [460, 1, 1, 0, "ARCHModelFixedResult", "Results for fixed parameters for an ARCHModel model"], [461, 1, 1, 0, "ARCHModelForecast", "Container for forecasts from an ARCH Model"], [462, 1, 1, 0, "ARCHModelForecastSimulation", "Container for a simulation or bootstrap-based forecasts from an ARCH Model"], [463, 1, 1, 0, "ARCHModelResult", "Results from estimation of an ARCHModel model"]], "arch.univariate.base.ARCHModel": [[807, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [808, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [809, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [810, 5, 1, 0, "distribution", "Set or gets the error distribution"], [811, 3, 1, 0, "fit", "Estimate model parameters"], [812, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [813, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [814, 5, 1, 0, "name", "The name of the model."], [815, 5, 1, 0, "num_params", "Number of parameters in the model"], [816, 3, 1, 0, "parameter_names", "List of parameters names"], [817, 3, 1, 0, "resids", "Compute model residuals"], [818, 3, 1, 0, "simulate", ""], [819, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [820, 5, 1, 0, "volatility", "Set or gets the volatility process"], [821, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.base.ARCHModel.__init__": [[459, 2, 2, "arch.univariate.base.ARCHModel", "distribution", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [459, 2, 2, "arch.univariate.base.ARCHModel", "hold_back", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [459, 2, 2, "arch.univariate.base.ARCHModel", "rescale", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [459, 2, 2, "arch.univariate.base.ARCHModel", "volatility", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [459, 2, 2, "arch.univariate.base.ARCHModel", "y", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."]], "arch.univariate.base.ARCHModel.compute_param_cov": [[808, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [808, 2, 2, 0, "params", "Model parameters"], [808, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.base.ARCHModel.fit": [[811, 2, 2, 0, "backcast", "Value to use as backcast."], [811, 2, 2, 0, "cov_type", "Estimation method of parameter covariance."], [811, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing."], [811, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [811, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [811, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize."], [811, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [811, 2, 2, 0, "starting_values", "Array of starting values to use."], [811, 2, 2, 0, "tol", "Tolerance for termination."], [811, 2, 2, 0, "update_freq", "Frequency of iteration updates."]], "arch.univariate.base.ARCHModel.fix": [[812, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [812, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [812, 2, 2, 0, "params", "User specified parameters to use when generating the result."]], "arch.univariate.base.ARCHModel.forecast": [[813, 2, 2, 0, "align", "Either 'origin' or 'target'."], [813, 2, 2, 0, "horizon", "Number of steps to forecast"], [813, 2, 2, 0, "method", "Method to use when producing the forecast."], [813, 2, 2, 0, "params", "Parameters required to forecast."], [813, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [813, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [813, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [813, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [813, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [813, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.base.ARCHModel.resids": [[817, 2, 2, 0, "params", "Model parameters"], [817, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [817, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.base.ARCHModel.simulate": [[818, 2, 2, "arch.univariate.base.ARCHModel.simulate", "burn", ""], [818, 2, 2, "arch.univariate.base.ARCHModel.simulate", "initial_value", ""], [818, 2, 2, "arch.univariate.base.ARCHModel.simulate", "initial_value_vol", ""], [818, 2, 2, "arch.univariate.base.ARCHModel.simulate", "nobs", ""], [818, 2, 2, "arch.univariate.base.ARCHModel.simulate", "params", ""], [818, 2, 2, "arch.univariate.base.ARCHModel.simulate", "x", ""]], "arch.univariate.base.ARCHModelFixedResult": [[822, 5, 1, 0, "aic", "Akaike Information Criteria"], [823, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [824, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [825, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [826, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [827, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [828, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [829, 5, 1, 0, "model", "Model instance used to produce the fit"], [830, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [831, 5, 1, 0, "num_params", "Number of parameters in model"], [832, 5, 1, 0, "params", "Model Parameters"], [833, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [834, 5, 1, 0, "resid", "Model residuals"], [835, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [836, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."]], "arch.univariate.base.ARCHModelFixedResult.__init__": [[460, 2, 2, 0, "dep_var", "Dependent variable"], [460, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [460, 2, 2, 0, "loglikelihood", "Loglikelihood at specified parameters"], [460, 2, 2, 0, "model", "The model object used to estimate the parameters"], [460, 2, 2, 0, "names", "Model parameter names"], [460, 2, 2, 0, "params", "Estimated parameters"], [460, 2, 2, 0, "resid", "Residuals from model."], [460, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelFixedResult.arch_lm_test": [[823, 2, 2, 0, "lags", "Number of lags to include in the model."], [823, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations."]], "arch.univariate.base.ARCHModelFixedResult.forecast": [[826, 2, 2, 0, "align", "Either 'origin' or 'target'."], [826, 2, 2, 0, "horizon", "Number of steps to forecast"], [826, 2, 2, 0, "method", "Method to use when producing the forecast."], [826, 2, 2, 0, "params", "Alternative parameters to use."], [826, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [826, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [826, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [826, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [826, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [826, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.base.ARCHModelFixedResult.hedgehog_plot": [[827, 2, 2, 0, "horizon", "Number of steps to forecast"], [827, 2, 2, 0, "method", "Method to use when producing the forecast."], [827, 2, 2, 0, "params", "Alternative parameters to use."], [827, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [827, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [827, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [827, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelFixedResult.plot": [[833, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility."], [833, 2, 2, 0, "scale", "Value to use when scaling returns to annualize."]], "arch.univariate.base.ARCHModelForecast": [[837, 5, 1, 0, "mean", "Forecast values for the conditional mean of the process"], [838, 5, 1, 0, "residual_variance", "Forecast values for the conditional variance of the residuals"], [839, 5, 1, 0, "simulations", "Detailed simulation results if using a simulation-based method"], [840, 5, 1, 0, "variance", "Forecast values for the conditional variance of the process"]], "arch.univariate.base.ARCHModelForecast.__init__": [[461, 2, 2, 0, "align", ""], [461, 2, 2, 0, "index", ""], [461, 2, 2, 0, "mean", ""], [461, 2, 2, "arch.univariate.base.ARCHModelForecast", "reindex", "Container for forecasts from an ARCH Model"], [461, 2, 2, 0, "residual_variance", ""], [461, 2, 2, 0, "simulated_paths", ""], [461, 2, 2, 0, "simulated_residual_variances", ""], [461, 2, 2, 0, "simulated_residuals", ""], [461, 2, 2, 0, "simulated_variances", ""], [461, 2, 2, "arch.univariate.base.ARCHModelForecast", "start_index", "Container for forecasts from an ARCH Model"], [461, 2, 2, 0, "variance", ""]], "arch.univariate.base.ARCHModelForecastSimulation": [[841, 5, 1, 0, "index", "The index aligned to dimension 0 of the simulation paths"], [842, 5, 1, 0, "residual_variances", "Simulated variance of the residuals"], [843, 5, 1, 0, "residuals", "Simulated residuals used to produce the values"], [844, 5, 1, 0, "values", "The values of the process"], [845, 5, 1, 0, "variances", "Simulated variances of the values"]], "arch.univariate.base.ARCHModelForecastSimulation.__init__": [[462, 2, 2, 0, "index", ""], [462, 2, 2, 0, "residual_variances", ""], [462, 2, 2, 0, "residuals", ""], [462, 2, 2, 0, "values", ""], [462, 2, 2, 0, "variances", ""]], "arch.univariate.base.ARCHModelResult": [[846, 5, 1, 0, "aic", "Akaike Information Criteria"], [847, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [848, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [849, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [850, 3, 1, 0, "conf_int", "Parameter confidence intervals"], [851, 5, 1, 0, "convergence_flag", "scipy.optimize.minimize result flag"], [852, 5, 1, 0, "fit_start", "Start of sample used to estimate parameters"], [853, 5, 1, 0, "fit_stop", "End of sample used to estimate parameters"], [854, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [855, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [856, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [857, 5, 1, 0, "model", "Model instance used to produce the fit"], [858, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [859, 5, 1, 0, "num_params", "Number of parameters in model"], [860, 5, 1, 0, "optimization_result", "Information about the convergence of the loglikelihood optimization"], [861, 5, 1, 0, "param_cov", "Parameter covariance"], [862, 5, 1, 0, "params", "Model Parameters"], [863, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [864, 5, 1, 0, "pvalues", "Array of p-values for the t-statistics"], [865, 5, 1, 0, "resid", "Model residuals"], [866, 5, 1, 0, "rsquared", "R-squared"], [867, 5, 1, 0, "rsquared_adj", "Degree of freedom adjusted R-squared"], [868, 5, 1, 0, "scale", "The scale applied to the original data before estimating the model."], [869, 5, 1, 0, "std_err", "Array of parameter standard errors"], [870, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [871, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."], [872, 5, 1, 0, "tvalues", "Array of t-statistics testing the null that the coefficient are 0"]], "arch.univariate.base.ARCHModelResult.__init__": [[463, 2, 2, 0, "cov_type", "String describing the covariance estimator used"], [463, 2, 2, 0, "dep_var", "Dependent variable"], [463, 2, 2, 0, "fit_start", "Integer index of the first observation used to fit the model"], [463, 2, 2, 0, "fit_stop", "Integer index of the last observation used to fit the model using slice notation fit_start:fit_stop"], [463, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [463, 2, 2, 0, "loglikelihood", "Loglikelihood at estimated parameters"], [463, 2, 2, 0, "model", "The model object used to estimate the parameters"], [463, 2, 2, 0, "names", "Model parameter names"], [463, 2, 2, 0, "optim_output", "Result of log-likelihood optimization"], [463, 2, 2, 0, "param_cov", "Estimated variance-covariance matrix of params."], [463, 2, 2, 0, "params", "Estimated parameters"], [463, 2, 2, 0, "r2", "Model R-squared"], [463, 2, 2, 0, "resid", "Residuals from model."], [463, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelResult.arch_lm_test": [[847, 2, 2, 0, "lags", "Number of lags to include in the model."], [847, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations."]], "arch.univariate.base.ARCHModelResult.conf_int": [[850, 2, 2, 0, "alpha", "Size (prob.) to use when constructing the confidence interval."]], "arch.univariate.base.ARCHModelResult.forecast": [[854, 2, 2, 0, "align", "Either 'origin' or 'target'."], [854, 2, 2, 0, "horizon", "Number of steps to forecast"], [854, 2, 2, 0, "method", "Method to use when producing the forecast."], [854, 2, 2, 0, "params", "Alternative parameters to use."], [854, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [854, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [854, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [854, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [854, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [854, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.base.ARCHModelResult.hedgehog_plot": [[855, 2, 2, 0, "horizon", "Number of steps to forecast"], [855, 2, 2, 0, "method", "Method to use when producing the forecast."], [855, 2, 2, 0, "params", "Alternative parameters to use."], [855, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [855, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [855, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [855, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelResult.plot": [[863, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility."], [863, 2, 2, 0, "scale", "Value to use when scaling returns to annualize."]], "arch.univariate.distribution": [[464, 1, 1, 0, "Distribution", "Template for subclassing only"]], "arch.univariate.distribution.Distribution": [[873, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [874, 3, 1, 0, "cdf", "Cumulative distribution function"], [875, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [876, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [877, 3, 1, 0, "loglikelihood", "Loglikelihood evaluation."], [878, 3, 1, 0, "moment", "Moment of order n"], [879, 5, 1, 0, "name", "The name of the distribution"], [880, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [881, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [882, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [883, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [884, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [885, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.distribution.Distribution.__init__": [[464, 2, 2, "arch.univariate.distribution.Distribution", "random_state", "Template for subclassing only"], [464, 2, 2, "arch.univariate.distribution.Distribution", "seed", "Template for subclassing only"]], "arch.univariate.distribution.Distribution.bounds": [[873, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.distribution.Distribution.cdf": [[874, 2, 2, 0, "parameters", "Distribution parameters."], [874, 2, 2, 0, "resids", 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"VolatilityProcess", "Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."]], "arch.univariate.volatility.VarianceForecast": [[887, 5, 1, 0, "forecast_paths", "The variance forecast paths"], [888, 5, 1, 0, "forecasts", "The variance forecasts"], [889, 5, 1, 0, "shocks", "The shocks used to construct the variance forecast paths"]], "arch.univariate.volatility.VarianceForecast.__init__": [[466, 2, 2, 0, "forecast_paths", "Array containing the forecast paths if using simulation or bootstrap"], [466, 2, 2, 0, "forecasts", "Array containing the forecasts"], [466, 2, 2, 0, "shocks", "Array containing the shocks used to generate the forecast paths if using simulation or bootstrap"]], "arch.univariate.volatility.VolatilityProcess": [[890, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [891, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [892, 3, 1, 0, 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911, 912, 913, 914, 915, 916, 917], "Then": 3, "There": [271, 434, 548, 802, 908, 911, 914], "These": [3, 89, 272, 433, 434, 437, 485, 502, 568, 585, 602, 620, 637, 654, 684, 733, 763, 905, 911, 912, 913, 914, 915], "To": [5, 6, 7, 8, 9, 433, 435], "Will": 95, "_": [4, 282, 284, 287, 290, 375, 381, 394, 437, 451, 911], "_1": 287, "_2": 282, "__future__": [95, 97], "__init__": [465, 915], "_b": 4, "_i": 10, "_r": 915, "_shock": 915, "_t": [122, 132, 146, 156, 166, 176, 186, 196, 206, 216, 226, 236, 246, 282, 287, 290, 375, 394, 437], "_u": 290, "_z": 290, "aaa": [430, 434], "ab": [446, 448, 911], "abd": 275, "abil": [95, 249, 251], "about": [14, 29, 44, 59, 74, 277, 278, 279, 510, 527, 543, 692, 708, 797, 813, 860, 913, 914], "abov": [3, 89, 271, 275, 276, 277, 278, 290, 433, 915], "absolut": [271, 914], "abstract": [139, 143, 147, 459, 465, 467, 813, 816, 817, 818, 873, 874, 875, 877, 878, 880, 881, 882, 884, 885, 892, 893, 894, 898, 899, 901], "acceler": [14, 29, 44, 59, 74], "accept": [3, 89, 94, 96, 510, 527, 543, 692, 708, 797, 813, 826, 854, 911], "access": [5, 6, 7, 8, 9, 260, 271, 434, 914], "accessibli": [5, 6, 7, 8, 9], "accommod": [510, 527, 543, 692, 708, 797, 813, 826, 854], "accord": 453, "account": [89, 270, 278, 295, 308, 320, 331, 345, 359, 367, 375, 381, 394, 415, 911], "accur": [4, 911], "accuraci": 4, "acf": 434, "across": [3, 7, 14, 29, 44, 59, 74, 271, 434, 437], "act": 915, "activ": 265, "actual": [89, 271, 437, 911, 913], "actual_x_oo": 911, "ad": [14, 29, 44, 59, 74, 92, 93, 94, 95, 96, 97, 510, 527, 543, 692, 708, 797, 813, 826, 854, 914, 917], "add": [93, 96, 271, 914], "addit": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 248, 278, 434, 479, 496, 562, 579, 596, 613, 631, 648, 678, 727, 757, 899, 913, 914], "addition": [434, 437], "adequ": 914, "adf": [95, 276, 277, 278, 280, 286, 289, 290, 311, 411, 430, 432, 433, 434], "adj": [4, 87, 88, 434, 437, 440, 907, 911, 912, 913, 914, 915], "adjac": 455, "adjust": [101, 103, 106, 107, 108, 109, 110, 111, 112, 113, 285, 367, 377, 381, 396, 415, 867], "administr": 433, "admiss": 3, "admit": 3, "adopt": 97, "advanc": 287, "advantag": [437, 915], "affect": [95, 96, 97, 282, 287, 290, 433, 510, 527, 543, 692, 708, 797, 813, 826, 827, 854, 855], "after": [5, 6, 7, 8, 9, 95, 96, 437, 510, 527, 543, 692, 708, 797, 813, 911, 913], "against": [91, 98, 277, 280, 509, 526, 542, 691, 707, 796, 812], "agre": [433, 434], "ahead": [437, 510, 527, 543, 692, 708, 797, 813, 826, 854, 911, 913], "aic": [275, 276, 280, 284, 289, 434, 907, 911, 912, 914, 915], "aim": 908, "akaik": [275, 276, 280, 284, 822, 846], "al": [277, 664, 743, 773, 786, 881], "algorithm": [10, 275, 280, 917], "alia": 273, "align": [437, 461, 510, 527, 543, 692, 708, 797, 813, 825, 826, 841, 849, 854, 913], "all": [2, 3, 4, 7, 10, 14, 29, 44, 59, 74, 87, 88, 95, 96, 99, 251, 271, 279, 280, 284, 381, 385, 398, 430, 432, 433, 434, 436, 437, 438, 440, 441, 443, 450, 451, 459, 465, 475, 492, 510, 527, 543, 558, 575, 592, 609, 627, 644, 674, 692, 708, 723, 753, 797, 813, 826, 827, 854, 855, 895, 908, 909, 910, 911, 913, 914, 915, 917], "allow": [3, 4, 7, 14, 15, 25, 29, 30, 40, 44, 45, 55, 59, 60, 70, 74, 75, 85, 94, 95, 96, 270, 271, 272, 275, 279, 434, 447, 467, 509, 526, 542, 691, 707, 796, 812, 908, 911, 914, 915, 917], "almost": 915, "along": [913, 914], "alpha": [4, 122, 132, 146, 156, 166, 176, 186, 196, 206, 216, 226, 236, 246, 290, 433, 437, 453, 722, 850, 907, 911, 912, 913, 914, 915], "alpha_": [438, 439, 444, 448, 450], "alreadi": 913, "also": [3, 89, 90, 96, 270, 271, 277, 280, 434, 911, 913, 914], "alter": 434, "altern": [3, 14, 29, 44, 59, 74, 88, 94, 100, 104, 105, 251, 252, 270, 275, 276, 277, 278, 286, 288, 291, 304, 317, 328, 341, 356, 400, 416, 430, 432, 433, 434, 451, 510, 515, 527, 531, 543, 547, 692, 696, 708, 712, 797, 801, 813, 817, 826, 827, 854, 855, 907, 911, 914, 916], "although": [4, 89, 271, 433, 434, 437, 446, 448, 911, 914], "alwai": [87, 259, 271, 434, 437, 911, 915], "am": [437, 510, 527, 543, 692, 708, 797, 813, 827, 833, 855, 863, 907, 913, 914], "american": 1, "amount": [271, 437], "an": [1, 3, 4, 5, 6, 7, 8, 9, 12, 14, 27, 29, 42, 44, 57, 59, 72, 74, 88, 89, 90, 95, 96, 101, 103, 106, 107, 108, 109, 110, 111, 112, 113, 118, 128, 142, 152, 162, 172, 182, 192, 202, 212, 222, 232, 242, 250, 251, 252, 269, 270, 271, 276, 277, 279, 280, 284, 289, 290, 367, 381, 415, 432, 433, 434, 436, 437, 441, 449, 450, 453, 454, 456, 457, 460, 461, 462, 463, 472, 490, 509, 510, 516, 526, 527, 532, 542, 543, 548, 556, 573, 590, 607, 625, 642, 672, 691, 692, 697, 707, 708, 713, 721, 751, 796, 797, 802, 812, 813, 826, 827, 833, 854, 855, 863, 893, 907, 912, 913, 914, 915], "analys": 432, "analysi": [3, 248, 275, 278], "analyt": [4, 14, 29, 44, 59, 74, 88, 273, 475, 492, 510, 527, 543, 558, 575, 592, 609, 627, 644, 674, 692, 708, 723, 753, 797, 813, 826, 827, 854, 855, 895, 910, 915], "andrew": [10, 95, 110, 277, 279, 280, 281, 432], "ani": [3, 5, 6, 7, 8, 9, 11, 12, 14, 15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 36, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 51, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 66, 67, 68, 69, 70, 71, 72, 74, 75, 77, 78, 81, 82, 83, 84, 85, 102, 120, 130, 134, 135, 136, 137, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 244, 258, 259, 264, 265, 268, 271, 273, 278, 284, 290, 367, 375, 381, 394, 415, 430, 434, 437, 440, 441, 447, 451, 459, 460, 461, 462, 463, 466, 468, 469, 470, 472, 473, 475, 479, 481, 483, 485, 487, 488, 489, 490, 491, 492, 496, 498, 500, 502, 505, 506, 508, 510, 515, 516, 517, 522, 523, 525, 527, 531, 532, 533, 538, 539, 541, 543, 547, 548, 549, 553, 554, 555, 556, 557, 558, 562, 564, 566, 568, 570, 571, 572, 573, 574, 575, 579, 581, 583, 585, 587, 588, 589, 590, 591, 592, 596, 598, 600, 602, 604, 605, 606, 607, 608, 609, 613, 615, 618, 620, 622, 623, 624, 625, 626, 627, 631, 633, 635, 637, 639, 640, 641, 642, 643, 644, 648, 650, 652, 654, 656, 657, 658, 660, 665, 667, 668, 669, 670, 671, 672, 673, 674, 678, 680, 682, 684, 687, 688, 690, 692, 696, 697, 698, 703, 704, 706, 708, 712, 713, 714, 718, 719, 720, 721, 722, 723, 727, 729, 731, 733, 735, 736, 737, 739, 744, 746, 747, 748, 749, 750, 751, 752, 753, 757, 759, 761, 763, 765, 766, 767, 769, 774, 776, 777, 778, 779, 780, 782, 787, 789, 790, 792, 793, 795, 797, 801, 802, 803, 808, 809, 811, 813, 817, 819, 825, 826, 834, 835, 842, 843, 844, 845, 849, 854, 865, 870, 873, 874, 875, 877, 882, 884, 885, 887, 888, 889, 890, 891, 892, 893, 894, 895, 899, 901, 903, 905, 907, 911, 913, 914, 915], "ann_infl": 914, "annual": [3, 4, 87, 833, 863, 914, 915], "anoth": 915, "anyarrai": 273, "anypanda": 273, "anyth": 290, "aparch": [95, 907], "api": [3, 271, 275, 276, 277, 278, 434], "appear": [3, 95, 275, 276, 277, 278, 282, 287, 432, 433, 434, 440, 911, 914], "append": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 271, 911, 913], "appli": [3, 87, 95, 281, 469, 488, 508, 525, 541, 554, 571, 588, 605, 623, 640, 670, 690, 706, 719, 749, 795, 811, 868, 891], "applic": [1, 88, 89, 271, 279, 459, 907, 908, 915], "approach": [433, 911, 915], "appropri": [3, 4, 7, 14, 29, 44, 59, 74, 86, 250, 251, 252, 469, 488, 554, 571, 588, 605, 623, 640, 670, 719, 749, 891], "approxim": [14, 29, 44, 59, 74, 275, 278, 280, 284, 468, 469, 470, 481, 485, 487, 488, 489, 498, 502, 553, 554, 555, 564, 568, 570, 571, 572, 581, 585, 587, 588, 589, 598, 602, 604, 605, 606, 615, 617, 620, 622, 623, 624, 633, 637, 639, 640, 641, 650, 654, 669, 670, 671, 680, 684, 718, 719, 720, 729, 733, 748, 749, 750, 759, 763, 890, 891, 892, 901, 905], "ar": [3, 4, 7, 10, 11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 89, 90, 95, 96, 97, 101, 102, 103, 106, 107, 108, 109, 110, 111, 112, 113, 116, 126, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 248, 250, 251, 252, 254, 255, 259, 270, 271, 272, 275, 276, 277, 278, 279, 280, 282, 284, 287, 289, 290, 334, 337, 367, 381, 415, 430, 432, 433, 434, 437, 438, 440, 441, 444, 446, 448, 450, 451, 455, 459, 465, 467, 473, 475, 485, 491, 492, 502, 508, 509, 510, 516, 525, 526, 527, 532, 541, 542, 543, 557, 558, 568, 574, 575, 585, 591, 592, 602, 608, 609, 620, 626, 627, 637, 643, 644, 654, 660, 673, 674, 684, 690, 691, 692, 697, 706, 707, 708, 713, 722, 723, 733, 739, 752, 753, 763, 769, 782, 795, 796, 797, 802, 811, 812, 813, 825, 826, 827, 833, 849, 854, 855, 863, 872, 877, 894, 895, 905, 907, 908, 909, 911, 912, 913, 914, 915, 917], "arang": [12, 27, 42, 57, 72, 89, 911, 913, 915], "arbitrag": 433, "arbitrari": [3, 448], "arch": [3, 4, 87, 88, 89, 92, 93, 94, 95, 96, 97, 248, 270, 271, 273, 430, 433, 434, 436, 437, 910, 911, 912, 913, 915, 916], "arch_lm_test": 95, "arch_model": [95, 96, 437, 510, 527, 543, 692, 708, 797, 813, 827, 833, 855, 863, 907, 910, 911, 913, 914, 915], "arch_no_binari": 96, "archinmean": [96, 917], "archmodel": [460, 463, 829, 857, 907, 908], "archmodelfixedresult": [509, 526, 542, 691, 707, 796, 812, 909], "archmodelforecast": [437, 510, 527, 543, 692, 708, 797, 813, 826, 854, 913], "archmodelforecastsimul": [839, 913], "archmodelresult": [508, 525, 541, 690, 706, 795, 811, 909, 915], "area": 1, "arg": [4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 25, 26, 28, 29, 30, 40, 41, 43, 44, 45, 55, 56, 58, 59, 60, 70, 71, 73, 74, 75, 85], "argument": [3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 25, 26, 27, 28, 29, 30, 40, 41, 42, 43, 44, 45, 48, 55, 56, 57, 58, 59, 60, 70, 71, 72, 73, 74, 75, 85, 87, 88, 89, 95, 96, 250, 251, 252, 449, 454, 456, 457, 667, 746, 776, 789, 884, 914], "aris": 86, "around": [5, 8, 9, 10, 90, 270, 433], "arrai": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 15, 25, 26, 30, 40, 41, 45, 55, 56, 60, 70, 71, 75, 85, 87, 88, 89, 96, 101, 103, 106, 107, 108, 109, 110, 111, 112, 113, 250, 251, 252, 265, 269, 271, 282, 287, 290, 430, 440, 441, 447, 451, 452, 466, 472, 473, 475, 479, 481, 483, 485, 490, 491, 492, 496, 498, 500, 502, 506, 508, 510, 516, 523, 525, 527, 532, 539, 541, 543, 548, 556, 557, 558, 562, 564, 566, 568, 573, 574, 575, 579, 581, 583, 585, 590, 591, 592, 596, 598, 600, 602, 607, 608, 609, 613, 615, 618, 620, 625, 626, 627, 631, 633, 635, 637, 642, 643, 644, 648, 650, 652, 654, 658, 668, 672, 673, 674, 678, 680, 682, 684, 688, 690, 692, 697, 704, 706, 708, 713, 721, 723, 727, 729, 731, 733, 737, 751, 752, 753, 757, 759, 761, 763, 767, 777, 780, 790, 793, 795, 797, 802, 809, 811, 813, 825, 826, 849, 850, 854, 864, 869, 872, 875, 877, 893, 894, 895, 899, 901, 903, 905, 913], "array_forecast": 911, "array_multi_forecast": 911, "arraylik": 273, "arraylike1d": 273, "arraylike2d": 273, "articl": [1, 249], "arx": [907, 911, 914], "as_text": 434, "asarrai": 915, "ask": 271, "aspect": 271, "assembl": [907, 914], "assert": 96, "asset": [833, 863, 914], "associ": [1, 486, 503, 569, 586, 603, 621, 638, 655, 685, 734, 764, 906], "assum": [89, 95, 289, 433, 434, 437, 508, 525, 541, 660, 690, 706, 739, 769, 782, 795, 811, 911, 913, 914, 915], "assumpt": [433, 914, 915], "asym": 453, "asymmetr": [438, 444, 448, 453, 907, 914], "asymmetri": 438, "asymptot": [89, 275, 278, 290], "attach": [437, 659, 666, 738, 745, 768, 775, 781, 788, 876, 883, 913], "attempt": [270, 280, 475, 492, 558, 575, 592, 609, 627, 644, 674, 723, 753, 895, 913], "attribut": [5, 6, 7, 8, 9, 437, 913, 915], "augment": [93, 275, 289, 290, 404, 430, 432, 433], "auto_bandwidth": [95, 432], "autocorrel": [3, 10, 277, 278, 375, 381, 394, 434], "autocovari": [10, 290], "autolag": [275, 280], "automat": [10, 95, 96, 97, 275, 276, 277, 278, 280, 281, 289, 294, 307, 430, 432, 434, 440, 441, 442, 451, 452, 458, 907], "autoregress": [276, 284, 441, 451, 456, 914], "avail": [5, 6, 7, 8, 9, 271, 275, 277, 278, 434, 437, 447, 465, 475, 492, 510, 527, 543, 558, 575, 592, 609, 627, 644, 674, 692, 708, 723, 753, 797, 813, 826, 854, 895, 911, 913, 914, 915], "avar": 4, "avarch": 448, "averag": [3, 9, 15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 88, 271, 437, 445, 451, 455, 914, 915], "avg_model_loss": 271, 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823, 826, 847, 854, 911, 914], "inclus": 256, "incorrect": 95, "incorrectli": 95, "increas": [91, 98, 271, 914, 915], "increment": 279, "independ": [2, 3, 7, 95, 437, 911], "independentsamplesbootstrap": [3, 6, 86, 95], "index": [3, 4, 87, 88, 102, 256, 271, 433, 434, 437, 451, 461, 462, 463, 475, 480, 482, 483, 492, 497, 499, 500, 510, 527, 543, 558, 563, 565, 566, 575, 580, 582, 583, 592, 597, 599, 600, 609, 614, 616, 618, 627, 632, 634, 635, 644, 649, 651, 652, 674, 679, 681, 682, 692, 708, 723, 728, 730, 731, 753, 758, 760, 761, 797, 813, 826, 827, 854, 855, 895, 900, 902, 903, 911, 912, 913, 914, 915], "indic": [14, 20, 24, 29, 35, 39, 44, 48, 50, 54, 59, 65, 69, 74, 80, 84, 101, 103, 106, 107, 108, 109, 110, 111, 112, 113, 116, 118, 126, 128, 140, 142, 150, 152, 160, 162, 170, 172, 180, 182, 190, 192, 200, 202, 210, 212, 220, 222, 230, 232, 240, 242, 251, 252, 254, 255, 259, 265, 269, 271, 275, 279, 284, 343, 347, 349, 398, 433, 434, 437, 440, 441, 442, 451, 452, 453, 458, 484, 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381, 433, 434, 914], "j": [1, 249, 250, 252, 271, 275, 276, 278, 282, 284, 444, 446, 448, 450, 451, 453, 510, 516, 527, 532, 543, 692, 697, 708, 713, 797, 813, 826, 854, 911], "jackknif": [4, 95], "jarqu": 434, "jb": 434, "jersei": 275, "jim": 95, "jit": 92, "john": [1, 279], "join": 911, "jointli": [95, 438, 445, 467], "journal": [1, 249, 251, 275, 277, 278, 280, 282, 284, 290], "jth": [516, 532, 697, 713], "judg": [275, 278], "just": [271, 911], "k": [10, 250, 251, 252, 271, 277, 278, 280, 284, 367, 381, 415, 438, 440, 441, 444, 448, 451, 452, 516, 532, 697, 713, 915], "k_n": 10, "kappa": [282, 290, 440, 914], "kappa_i": 284, "kde": 914, "kei": [12, 27, 42, 57, 72, 402, 419, 430, 510, 527, 543, 692, 708, 797, 813, 826, 854, 911], "ken": 434, "kernel": [95, 281, 290, 367, 381, 390, 415, 433], "kernel_est": [283, 285, 288], "kernel_nam": 103, "kevin": [248, 453], "keyword": [3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 25, 26, 27, 28, 29, 30, 40, 41, 42, 43, 44, 45, 48, 55, 56, 57, 58, 59, 60, 70, 71, 72, 73, 74, 75, 85, 87, 88, 89, 95, 96], "khrapov": 94, "kind": [434, 914], "kmax": 455, "known": [251, 271, 290, 367, 381, 415, 475, 492, 558, 575, 592, 609, 627, 644, 674, 723, 753, 895, 914], "kpss": [93, 95, 432], "kurtosi": [434, 777, 790], "kw": 87, "kw_data": [5, 6, 7, 8, 9], "kwarg": [4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 25, 26, 28, 29, 30, 40, 41, 43, 44, 45, 55, 56, 58, 59, 60, 70, 71, 73, 74, 75, 85], "kwdata": [12, 27, 42, 57, 72], "kwiatkowski": 277, "l": [4, 446, 907, 911, 914], "l1": 434, "l2": 434, "l3": 434, "l4": 434, "l5": 434, "l_": [270, 271, 440, 441, 450, 451], "l_i": [270, 271], "label": [102, 273, 913, 915], "lack": 909, "lag": [95, 96, 97, 123, 133, 147, 157, 167, 177, 187, 197, 207, 217, 227, 237, 247, 275, 276, 277, 278, 279, 280, 284, 285, 286, 289, 294, 295, 307, 308, 320, 331, 334, 345, 354, 359, 381, 385, 386, 405, 430, 433, 438, 440, 441, 442, 444, 446, 448, 450, 451, 452, 453, 458, 510, 516, 527, 532, 543, 617, 692, 697, 708, 713, 797, 813, 823, 827, 847, 855, 907, 911, 912, 914], "lam": 445, "lambda": [282, 287, 445, 448, 456, 769, 914], "lambda_": 446, "lambda_0": [102, 282, 287], "lambda_1": [102, 446], "lambda_i": 446, "lambda_j": [284, 446], "larg": [1, 95, 97, 271, 282, 287, 430, 433, 434, 914], "larger": [271, 437, 446, 448, 475, 492, 558, 575, 592, 609, 627, 644, 674, 723, 753, 895, 911, 913, 915], "last": [20, 35, 50, 65, 80, 279, 282, 287, 437, 463, 508, 509, 525, 526, 541, 542, 690, 691, 706, 707, 795, 796, 811, 812, 911], "last_ob": [94, 95, 437, 508, 509, 525, 526, 541, 542, 690, 691, 706, 707, 795, 796, 811, 812, 911, 913], "later": [91, 97, 98, 914], "latest": [94, 248], "latex": [379, 398, 836, 871], "lceil": 10, "ldot": [101, 103, 106, 107, 108, 109, 110, 111, 112, 113, 437, 911], "lead": [1, 271, 284, 285, 381, 385, 386], "least": [86, 279, 282, 287, 381, 434, 452], "leav": [4, 95], "left": [4, 10, 101, 106, 110, 122, 132, 146, 156, 166, 176, 186, 196, 206, 216, 226, 236, 246, 282, 284, 287, 289, 290, 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"arch.covariance.kernel.CovarianceEstimator.bandwidth_scale", "arch.covariance.kernel.CovarianceEstimator.centered", "arch.covariance.kernel.CovarianceEstimator.cov", "arch.covariance.kernel.CovarianceEstimator.force_int", "arch.covariance.kernel.CovarianceEstimator.kernel_const", "arch.covariance.kernel.CovarianceEstimator.kernel_weights", "arch.covariance.kernel.CovarianceEstimator.name", "arch.covariance.kernel.CovarianceEstimator.opt_bandwidth", "arch.covariance.kernel.CovarianceEstimator.rate", "arch.covariance.kernel.Gallant.bandwidth", "arch.covariance.kernel.Gallant.bandwidth_scale", "arch.covariance.kernel.Gallant.centered", "arch.covariance.kernel.Gallant.cov", "arch.covariance.kernel.Gallant.force_int", "arch.covariance.kernel.Gallant.kernel_const", "arch.covariance.kernel.Gallant.kernel_weights", "arch.covariance.kernel.Gallant.name", "arch.covariance.kernel.Gallant.opt_bandwidth", "arch.covariance.kernel.Gallant.rate", "arch.covariance.kernel.NeweyWest.bandwidth", "arch.covariance.kernel.NeweyWest.bandwidth_scale", "arch.covariance.kernel.NeweyWest.centered", "arch.covariance.kernel.NeweyWest.cov", "arch.covariance.kernel.NeweyWest.force_int", "arch.covariance.kernel.NeweyWest.kernel_const", "arch.covariance.kernel.NeweyWest.kernel_weights", "arch.covariance.kernel.NeweyWest.name", "arch.covariance.kernel.NeweyWest.opt_bandwidth", "arch.covariance.kernel.NeweyWest.rate", "arch.covariance.kernel.Parzen.bandwidth", "arch.covariance.kernel.Parzen.bandwidth_scale", "arch.covariance.kernel.Parzen.centered", "arch.covariance.kernel.Parzen.cov", "arch.covariance.kernel.Parzen.force_int", "arch.covariance.kernel.Parzen.kernel_const", "arch.covariance.kernel.Parzen.kernel_weights", "arch.covariance.kernel.Parzen.name", "arch.covariance.kernel.Parzen.opt_bandwidth", "arch.covariance.kernel.Parzen.rate", "arch.covariance.kernel.ParzenCauchy.bandwidth", "arch.covariance.kernel.ParzenCauchy.bandwidth_scale", "arch.covariance.kernel.ParzenCauchy.centered", "arch.covariance.kernel.ParzenCauchy.cov", "arch.covariance.kernel.ParzenCauchy.force_int", "arch.covariance.kernel.ParzenCauchy.kernel_const", "arch.covariance.kernel.ParzenCauchy.kernel_weights", "arch.covariance.kernel.ParzenCauchy.name", "arch.covariance.kernel.ParzenCauchy.opt_bandwidth", "arch.covariance.kernel.ParzenCauchy.rate", "arch.covariance.kernel.ParzenGeometric.bandwidth", "arch.covariance.kernel.ParzenGeometric.bandwidth_scale", "arch.covariance.kernel.ParzenGeometric.centered", "arch.covariance.kernel.ParzenGeometric.cov", "arch.covariance.kernel.ParzenGeometric.force_int", "arch.covariance.kernel.ParzenGeometric.kernel_const", "arch.covariance.kernel.ParzenGeometric.kernel_weights", "arch.covariance.kernel.ParzenGeometric.name", "arch.covariance.kernel.ParzenGeometric.opt_bandwidth", "arch.covariance.kernel.ParzenGeometric.rate", "arch.covariance.kernel.ParzenRiesz.bandwidth", "arch.covariance.kernel.ParzenRiesz.bandwidth_scale", "arch.covariance.kernel.ParzenRiesz.centered", "arch.covariance.kernel.ParzenRiesz.cov", "arch.covariance.kernel.ParzenRiesz.force_int", "arch.covariance.kernel.ParzenRiesz.kernel_const", "arch.covariance.kernel.ParzenRiesz.kernel_weights", "arch.covariance.kernel.ParzenRiesz.name", "arch.covariance.kernel.ParzenRiesz.opt_bandwidth", "arch.covariance.kernel.ParzenRiesz.rate", "arch.covariance.kernel.QuadraticSpectral.bandwidth", "arch.covariance.kernel.QuadraticSpectral.bandwidth_scale", "arch.covariance.kernel.QuadraticSpectral.centered", "arch.covariance.kernel.QuadraticSpectral.cov", "arch.covariance.kernel.QuadraticSpectral.force_int", "arch.covariance.kernel.QuadraticSpectral.kernel_const", "arch.covariance.kernel.QuadraticSpectral.kernel_weights", "arch.covariance.kernel.QuadraticSpectral.name", "arch.covariance.kernel.QuadraticSpectral.opt_bandwidth", "arch.covariance.kernel.QuadraticSpectral.rate", "arch.covariance.kernel.TukeyHamming.bandwidth", "arch.covariance.kernel.TukeyHamming.bandwidth_scale", "arch.covariance.kernel.TukeyHamming.centered", "arch.covariance.kernel.TukeyHamming.cov", "arch.covariance.kernel.TukeyHamming.force_int", "arch.covariance.kernel.TukeyHamming.kernel_const", "arch.covariance.kernel.TukeyHamming.kernel_weights", "arch.covariance.kernel.TukeyHamming.name", "arch.covariance.kernel.TukeyHamming.opt_bandwidth", "arch.covariance.kernel.TukeyHamming.rate", "arch.covariance.kernel.TukeyHanning.bandwidth", "arch.covariance.kernel.TukeyHanning.bandwidth_scale", "arch.covariance.kernel.TukeyHanning.centered", "arch.covariance.kernel.TukeyHanning.cov", "arch.covariance.kernel.TukeyHanning.force_int", "arch.covariance.kernel.TukeyHanning.kernel_const", "arch.covariance.kernel.TukeyHanning.kernel_weights", "arch.covariance.kernel.TukeyHanning.name", "arch.covariance.kernel.TukeyHanning.opt_bandwidth", "arch.covariance.kernel.TukeyHanning.rate", "arch.covariance.kernel.TukeyParzen.bandwidth", "arch.covariance.kernel.TukeyParzen.bandwidth_scale", "arch.covariance.kernel.TukeyParzen.centered", "arch.covariance.kernel.TukeyParzen.cov", "arch.covariance.kernel.TukeyParzen.force_int", "arch.covariance.kernel.TukeyParzen.kernel_const", "arch.covariance.kernel.TukeyParzen.kernel_weights", "arch.covariance.kernel.TukeyParzen.name", "arch.covariance.kernel.TukeyParzen.opt_bandwidth", "arch.covariance.kernel.TukeyParzen.rate", "Introduction", "References", "arch.bootstrap.MCS", "arch.bootstrap.SPA", "arch.bootstrap.StepM", "arch.bootstrap.MCS.compute", "arch.bootstrap.MCS.excluded", "arch.bootstrap.MCS.included", "arch.bootstrap.MCS.pvalues", "arch.bootstrap.MCS.reset", "arch.bootstrap.MCS.seed", "arch.bootstrap.SPA.better_models", "arch.bootstrap.SPA.compute", "arch.bootstrap.SPA.critical_values", "arch.bootstrap.SPA.pvalues", "arch.bootstrap.SPA.reset", "arch.bootstrap.SPA.seed", "arch.bootstrap.SPA.subset", "arch.bootstrap.StepM.compute", "arch.bootstrap.StepM.reset", "arch.bootstrap.StepM.seed", "arch.bootstrap.StepM.superior_models", "Module Reference", "Multiple Comparisons", "Multiple Comparison Procedures", "Common Type Definitions", "Cointegration Tests", "arch.unitroot.ADF", "arch.unitroot.DFGLS", "arch.unitroot.KPSS", "arch.unitroot.PhillipsPerron", "arch.unitroot.VarianceRatio", "arch.unitroot.ZivotAndrews", "arch.unitroot.auto_bandwidth", "arch.unitroot.cointegration.CanonicalCointegratingReg", "arch.unitroot.cointegration.CointegrationAnalysisResults", "arch.unitroot.cointegration.DynamicOLS", "arch.unitroot.cointegration.DynamicOLSResults", "arch.unitroot.cointegration.EngleGrangerTestResults", "arch.unitroot.cointegration.FullyModifiedOLS", "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "arch.unitroot.cointegration.engle_granger", "arch.unitroot.cointegration.phillips_ouliaris", "arch.unitroot.ADF.alternative_hypothesis", "arch.unitroot.ADF.critical_values", "arch.unitroot.ADF.lags", "arch.unitroot.ADF.max_lags", "arch.unitroot.ADF.nobs", "arch.unitroot.ADF.null_hypothesis", "arch.unitroot.ADF.pvalue", "arch.unitroot.ADF.regression", "arch.unitroot.ADF.stat", "arch.unitroot.ADF.summary", "arch.unitroot.ADF.trend", "arch.unitroot.ADF.valid_trends", "arch.unitroot.ADF.y", "arch.unitroot.DFGLS.alternative_hypothesis", "arch.unitroot.DFGLS.critical_values", "arch.unitroot.DFGLS.lags", "arch.unitroot.DFGLS.max_lags", "arch.unitroot.DFGLS.nobs", "arch.unitroot.DFGLS.null_hypothesis", "arch.unitroot.DFGLS.pvalue", "arch.unitroot.DFGLS.regression", "arch.unitroot.DFGLS.stat", "arch.unitroot.DFGLS.summary", "arch.unitroot.DFGLS.trend", "arch.unitroot.DFGLS.valid_trends", "arch.unitroot.DFGLS.y", "arch.unitroot.KPSS.alternative_hypothesis", "arch.unitroot.KPSS.critical_values", "arch.unitroot.KPSS.lags", "arch.unitroot.KPSS.nobs", "arch.unitroot.KPSS.null_hypothesis", "arch.unitroot.KPSS.pvalue", "arch.unitroot.KPSS.stat", "arch.unitroot.KPSS.summary", "arch.unitroot.KPSS.trend", "arch.unitroot.KPSS.valid_trends", "arch.unitroot.KPSS.y", "arch.unitroot.PhillipsPerron.alternative_hypothesis", "arch.unitroot.PhillipsPerron.critical_values", "arch.unitroot.PhillipsPerron.lags", "arch.unitroot.PhillipsPerron.nobs", "arch.unitroot.PhillipsPerron.null_hypothesis", "arch.unitroot.PhillipsPerron.pvalue", "arch.unitroot.PhillipsPerron.regression", "arch.unitroot.PhillipsPerron.stat", "arch.unitroot.PhillipsPerron.summary", "arch.unitroot.PhillipsPerron.test_type", "arch.unitroot.PhillipsPerron.trend", "arch.unitroot.PhillipsPerron.valid_trends", "arch.unitroot.PhillipsPerron.y", "arch.unitroot.VarianceRatio.alternative_hypothesis", "arch.unitroot.VarianceRatio.critical_values", "arch.unitroot.VarianceRatio.debiased", "arch.unitroot.VarianceRatio.lags", "arch.unitroot.VarianceRatio.nobs", "arch.unitroot.VarianceRatio.null_hypothesis", "arch.unitroot.VarianceRatio.overlap", "arch.unitroot.VarianceRatio.pvalue", "arch.unitroot.VarianceRatio.robust", "arch.unitroot.VarianceRatio.stat", "arch.unitroot.VarianceRatio.summary", "arch.unitroot.VarianceRatio.trend", "arch.unitroot.VarianceRatio.valid_trends", "arch.unitroot.VarianceRatio.vr", "arch.unitroot.VarianceRatio.y", "arch.unitroot.ZivotAndrews.alternative_hypothesis", "arch.unitroot.ZivotAndrews.critical_values", "arch.unitroot.ZivotAndrews.lags", "arch.unitroot.ZivotAndrews.nobs", "arch.unitroot.ZivotAndrews.null_hypothesis", "arch.unitroot.ZivotAndrews.pvalue", "arch.unitroot.ZivotAndrews.stat", "arch.unitroot.ZivotAndrews.summary", "arch.unitroot.ZivotAndrews.trend", "arch.unitroot.ZivotAndrews.valid_trends", "arch.unitroot.ZivotAndrews.y", "arch.unitroot.cointegration.CanonicalCointegratingReg.fit", "arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth", "arch.unitroot.cointegration.CointegrationAnalysisResults.cov", "arch.unitroot.cointegration.CointegrationAnalysisResults.kernel", "arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance", "arch.unitroot.cointegration.CointegrationAnalysisResults.params", "arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues", "arch.unitroot.cointegration.CointegrationAnalysisResults.resid", "arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance", "arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared", "arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj", "arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors", "arch.unitroot.cointegration.CointegrationAnalysisResults.summary", "arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues", "arch.unitroot.cointegration.DynamicOLS.fit", "arch.unitroot.cointegration.DynamicOLSResults.bandwidth", "arch.unitroot.cointegration.DynamicOLSResults.cov", "arch.unitroot.cointegration.DynamicOLSResults.cov_type", "arch.unitroot.cointegration.DynamicOLSResults.full_cov", "arch.unitroot.cointegration.DynamicOLSResults.full_params", "arch.unitroot.cointegration.DynamicOLSResults.kernel", "arch.unitroot.cointegration.DynamicOLSResults.lags", "arch.unitroot.cointegration.DynamicOLSResults.leads", "arch.unitroot.cointegration.DynamicOLSResults.long_run_variance", "arch.unitroot.cointegration.DynamicOLSResults.params", "arch.unitroot.cointegration.DynamicOLSResults.pvalues", "arch.unitroot.cointegration.DynamicOLSResults.resid", "arch.unitroot.cointegration.DynamicOLSResults.residual_variance", "arch.unitroot.cointegration.DynamicOLSResults.rsquared", "arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj", "arch.unitroot.cointegration.DynamicOLSResults.std_errors", "arch.unitroot.cointegration.DynamicOLSResults.summary", "arch.unitroot.cointegration.DynamicOLSResults.tvalues", "arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis", "arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector", "arch.unitroot.cointegration.EngleGrangerTestResults.critical_values", "arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order", "arch.unitroot.cointegration.EngleGrangerTestResults.lags", "arch.unitroot.cointegration.EngleGrangerTestResults.max_lags", "arch.unitroot.cointegration.EngleGrangerTestResults.name", "arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis", "arch.unitroot.cointegration.EngleGrangerTestResults.plot", "arch.unitroot.cointegration.EngleGrangerTestResults.pvalue", "arch.unitroot.cointegration.EngleGrangerTestResults.resid", "arch.unitroot.cointegration.EngleGrangerTestResults.rho", "arch.unitroot.cointegration.EngleGrangerTestResults.stat", "arch.unitroot.cointegration.EngleGrangerTestResults.summary", "arch.unitroot.cointegration.EngleGrangerTestResults.trend", "arch.unitroot.cointegration.FullyModifiedOLS.fit", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.name", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend", "Introduction", "The Unit Root Tests", "Unit Root Testing", "Cointegration Testing", "Unit Root Testing", "Theoretical Background", "Distributions", "Forecasting", "arch.univariate.APARCH", "arch.univariate.ARCH", "arch.univariate.ARCHInMean", "arch.univariate.ARX", "arch.univariate.ConstantMean", "arch.univariate.ConstantVariance", "arch.univariate.EGARCH", "arch.univariate.EWMAVariance", "arch.univariate.FIGARCH", "arch.univariate.FixedVariance", "arch.univariate.GARCH", "arch.univariate.GeneralizedError", "arch.univariate.HARCH", "arch.univariate.HARX", "arch.univariate.LS", "arch.univariate.MIDASHyperbolic", "arch.univariate.Normal", "arch.univariate.RiskMetrics2006", "arch.univariate.SkewStudent", "arch.univariate.StudentsT", "arch.univariate.ZeroMean", "arch.univariate.base.ARCHModel", "arch.univariate.base.ARCHModelFixedResult", "arch.univariate.base.ARCHModelForecast", "arch.univariate.base.ARCHModelForecastSimulation", "arch.univariate.base.ARCHModelResult", "arch.univariate.distribution.Distribution", "arch.univariate.recursions.VolatilityUpdater", "arch.univariate.volatility.VarianceForecast", "arch.univariate.volatility.VolatilityProcess", "arch.univariate.APARCH.backcast", "arch.univariate.APARCH.backcast_transform", "arch.univariate.APARCH.bounds", "arch.univariate.APARCH.common_asym", "arch.univariate.APARCH.compute_variance", "arch.univariate.APARCH.constraints", "arch.univariate.APARCH.delta", "arch.univariate.APARCH.forecast", "arch.univariate.APARCH.name", "arch.univariate.APARCH.num_params", "arch.univariate.APARCH.parameter_names", "arch.univariate.APARCH.simulate", "arch.univariate.APARCH.start", "arch.univariate.APARCH.starting_values", "arch.univariate.APARCH.stop", "arch.univariate.APARCH.update", "arch.univariate.APARCH.updateable", "arch.univariate.APARCH.variance_bounds", "arch.univariate.APARCH.volatility_updater", "arch.univariate.ARCH.backcast", "arch.univariate.ARCH.backcast_transform", "arch.univariate.ARCH.bounds", "arch.univariate.ARCH.compute_variance", "arch.univariate.ARCH.constraints", "arch.univariate.ARCH.forecast", "arch.univariate.ARCH.name", "arch.univariate.ARCH.num_params", "arch.univariate.ARCH.parameter_names", "arch.univariate.ARCH.simulate", "arch.univariate.ARCH.start", "arch.univariate.ARCH.starting_values", "arch.univariate.ARCH.stop", "arch.univariate.ARCH.update", "arch.univariate.ARCH.updateable", "arch.univariate.ARCH.variance_bounds", "arch.univariate.ARCH.volatility_updater", "arch.univariate.ARCHInMean.bounds", "arch.univariate.ARCHInMean.compute_param_cov", "arch.univariate.ARCHInMean.constraints", "arch.univariate.ARCHInMean.distribution", "arch.univariate.ARCHInMean.fit", "arch.univariate.ARCHInMean.fix", "arch.univariate.ARCHInMean.forecast", "arch.univariate.ARCHInMean.form", "arch.univariate.ARCHInMean.name", "arch.univariate.ARCHInMean.num_params", "arch.univariate.ARCHInMean.parameter_names", "arch.univariate.ARCHInMean.resids", "arch.univariate.ARCHInMean.simulate", "arch.univariate.ARCHInMean.starting_values", "arch.univariate.ARCHInMean.volatility", "arch.univariate.ARCHInMean.x", "arch.univariate.ARCHInMean.y", "arch.univariate.ARX.bounds", "arch.univariate.ARX.compute_param_cov", "arch.univariate.ARX.constraints", "arch.univariate.ARX.distribution", "arch.univariate.ARX.fit", "arch.univariate.ARX.fix", "arch.univariate.ARX.forecast", "arch.univariate.ARX.name", "arch.univariate.ARX.num_params", "arch.univariate.ARX.parameter_names", "arch.univariate.ARX.resids", "arch.univariate.ARX.simulate", "arch.univariate.ARX.starting_values", "arch.univariate.ARX.volatility", "arch.univariate.ARX.x", "arch.univariate.ARX.y", "arch.univariate.ConstantMean.bounds", "arch.univariate.ConstantMean.compute_param_cov", "arch.univariate.ConstantMean.constraints", "arch.univariate.ConstantMean.distribution", "arch.univariate.ConstantMean.fit", "arch.univariate.ConstantMean.fix", "arch.univariate.ConstantMean.forecast", "arch.univariate.ConstantMean.name", "arch.univariate.ConstantMean.num_params", "arch.univariate.ConstantMean.parameter_names", "arch.univariate.ConstantMean.resids", "arch.univariate.ConstantMean.simulate", "arch.univariate.ConstantMean.starting_values", "arch.univariate.ConstantMean.volatility", "arch.univariate.ConstantMean.x", "arch.univariate.ConstantMean.y", "arch.univariate.ConstantVariance.backcast", "arch.univariate.ConstantVariance.backcast_transform", "arch.univariate.ConstantVariance.bounds", "arch.univariate.ConstantVariance.compute_variance", "arch.univariate.ConstantVariance.constraints", "arch.univariate.ConstantVariance.forecast", "arch.univariate.ConstantVariance.name", "arch.univariate.ConstantVariance.num_params", "arch.univariate.ConstantVariance.parameter_names", "arch.univariate.ConstantVariance.simulate", "arch.univariate.ConstantVariance.start", "arch.univariate.ConstantVariance.starting_values", "arch.univariate.ConstantVariance.stop", "arch.univariate.ConstantVariance.update", "arch.univariate.ConstantVariance.updateable", "arch.univariate.ConstantVariance.variance_bounds", "arch.univariate.ConstantVariance.volatility_updater", "arch.univariate.EGARCH.backcast", "arch.univariate.EGARCH.backcast_transform", "arch.univariate.EGARCH.bounds", "arch.univariate.EGARCH.compute_variance", "arch.univariate.EGARCH.constraints", "arch.univariate.EGARCH.forecast", "arch.univariate.EGARCH.name", "arch.univariate.EGARCH.num_params", "arch.univariate.EGARCH.parameter_names", "arch.univariate.EGARCH.simulate", "arch.univariate.EGARCH.start", "arch.univariate.EGARCH.starting_values", "arch.univariate.EGARCH.stop", "arch.univariate.EGARCH.update", "arch.univariate.EGARCH.updateable", "arch.univariate.EGARCH.variance_bounds", "arch.univariate.EGARCH.volatility_updater", "arch.univariate.EWMAVariance.backcast", "arch.univariate.EWMAVariance.backcast_transform", "arch.univariate.EWMAVariance.bounds", "arch.univariate.EWMAVariance.compute_variance", "arch.univariate.EWMAVariance.constraints", "arch.univariate.EWMAVariance.forecast", "arch.univariate.EWMAVariance.name", "arch.univariate.EWMAVariance.num_params", "arch.univariate.EWMAVariance.parameter_names", "arch.univariate.EWMAVariance.simulate", "arch.univariate.EWMAVariance.start", "arch.univariate.EWMAVariance.starting_values", "arch.univariate.EWMAVariance.stop", "arch.univariate.EWMAVariance.update", "arch.univariate.EWMAVariance.updateable", "arch.univariate.EWMAVariance.variance_bounds", "arch.univariate.EWMAVariance.volatility_updater", "arch.univariate.FIGARCH.backcast", "arch.univariate.FIGARCH.backcast_transform", "arch.univariate.FIGARCH.bounds", "arch.univariate.FIGARCH.compute_variance", "arch.univariate.FIGARCH.constraints", "arch.univariate.FIGARCH.forecast", "arch.univariate.FIGARCH.name", "arch.univariate.FIGARCH.num_params", "arch.univariate.FIGARCH.parameter_names", "arch.univariate.FIGARCH.simulate", "arch.univariate.FIGARCH.start", "arch.univariate.FIGARCH.starting_values", "arch.univariate.FIGARCH.stop", "arch.univariate.FIGARCH.truncation", "arch.univariate.FIGARCH.update", "arch.univariate.FIGARCH.updateable", "arch.univariate.FIGARCH.variance_bounds", "arch.univariate.FIGARCH.volatility_updater", "arch.univariate.FixedVariance.backcast", "arch.univariate.FixedVariance.backcast_transform", "arch.univariate.FixedVariance.bounds", "arch.univariate.FixedVariance.compute_variance", "arch.univariate.FixedVariance.constraints", "arch.univariate.FixedVariance.forecast", "arch.univariate.FixedVariance.name", "arch.univariate.FixedVariance.num_params", "arch.univariate.FixedVariance.parameter_names", "arch.univariate.FixedVariance.simulate", "arch.univariate.FixedVariance.start", "arch.univariate.FixedVariance.starting_values", "arch.univariate.FixedVariance.stop", "arch.univariate.FixedVariance.update", "arch.univariate.FixedVariance.updateable", "arch.univariate.FixedVariance.variance_bounds", "arch.univariate.FixedVariance.volatility_updater", "arch.univariate.GARCH.backcast", "arch.univariate.GARCH.backcast_transform", "arch.univariate.GARCH.bounds", "arch.univariate.GARCH.compute_variance", "arch.univariate.GARCH.constraints", "arch.univariate.GARCH.forecast", "arch.univariate.GARCH.name", "arch.univariate.GARCH.num_params", "arch.univariate.GARCH.parameter_names", "arch.univariate.GARCH.simulate", "arch.univariate.GARCH.start", "arch.univariate.GARCH.starting_values", "arch.univariate.GARCH.stop", "arch.univariate.GARCH.update", "arch.univariate.GARCH.updateable", "arch.univariate.GARCH.variance_bounds", "arch.univariate.GARCH.volatility_updater", "arch.univariate.GeneralizedError.bounds", "arch.univariate.GeneralizedError.cdf", "arch.univariate.GeneralizedError.constraints", "arch.univariate.GeneralizedError.generator", "arch.univariate.GeneralizedError.loglikelihood", "arch.univariate.GeneralizedError.moment", "arch.univariate.GeneralizedError.name", "arch.univariate.GeneralizedError.parameter_names", "arch.univariate.GeneralizedError.partial_moment", "arch.univariate.GeneralizedError.ppf", 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\ No newline at end of file
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See Notes for requirements"], [15, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [5, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [16, 5, 1, 0, "generator", "Set or get the instance PRNG"], [17, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [18, 5, 1, 0, "index", "The current index of the bootstrap"], [5, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [5, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [19, 5, 1, 0, "random_state", "Set or get the instance random state"], [20, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [21, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [22, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [23, 5, 1, 0, "state", "Set or get the generator's state"], [24, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [25, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.CircularBlockBootstrap.__init__": [[5, 2, 2, 0, "args", "Positional arguments to bootstrap"], [5, 2, 2, 0, "block_size", "Size of block to use"], [5, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [5, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results."], [5, 2, 2, 0, "seed", "Seed to use to ensure reproducable results."]], "arch.bootstrap.CircularBlockBootstrap.apply": [[11, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func."], [11, 2, 2, 0, "func", "Function the computes parameter values."], [11, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.bootstrap": [[12, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.clone": [[13, 2, 2, 0, "args", "Positional arguments to bootstrap"], [13, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [13, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.CircularBlockBootstrap.conf_int": [[14, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [14, 2, 2, 0, "func", "Function the computes parameter values."], [14, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [14, 2, 2, 0, "reps", "Number of bootstrap replications"], [14, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results."], [14, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'."], [14, 2, 2, 0, "size", "Coverage of confidence interval"], [14, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap."], [14, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap."], [14, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.CircularBlockBootstrap.cov": [[15, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [15, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [15, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [15, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.reset": [[20, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided."]], "arch.bootstrap.CircularBlockBootstrap.seed": [[21, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.CircularBlockBootstrap.set_state": [[22, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.CircularBlockBootstrap.var": [[25, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [25, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [25, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [25, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap": [[26, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [27, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [28, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [29, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [30, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [6, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [31, 5, 1, 0, "generator", "Set or get the instance PRNG"], [32, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [33, 5, 1, 0, "index", "The current index of the bootstrap"], [6, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [6, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [34, 5, 1, 0, "random_state", "Set or get the instance random state"], [35, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [36, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [37, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [38, 5, 1, 0, "state", "Set or get the generator's state"], [39, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [40, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.IIDBootstrap.__init__": [[6, 2, 2, 0, "args", "Positional arguments to bootstrap"], [6, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [6, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results."], [6, 2, 2, 0, "seed", "Seed to use to ensure reproducable results."]], "arch.bootstrap.IIDBootstrap.apply": [[26, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func."], [26, 2, 2, 0, "func", "Function the computes parameter values."], [26, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.bootstrap": [[27, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.clone": [[28, 2, 2, 0, "args", "Positional arguments to bootstrap"], [28, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [28, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.IIDBootstrap.conf_int": [[29, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [29, 2, 2, 0, "func", "Function the computes parameter values."], [29, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [29, 2, 2, 0, "reps", "Number of bootstrap replications"], [29, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results."], [29, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'."], [29, 2, 2, 0, "size", "Coverage of confidence interval"], [29, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap."], [29, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap."], [29, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.IIDBootstrap.cov": [[30, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [30, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [30, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [30, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.reset": [[35, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided."]], "arch.bootstrap.IIDBootstrap.seed": [[36, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.IIDBootstrap.set_state": [[37, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.IIDBootstrap.var": [[40, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [40, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [40, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [40, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap": [[41, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [42, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [43, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [44, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [45, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [7, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [46, 5, 1, 0, "generator", "Set or get the instance PRNG"], [47, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [48, 5, 1, 0, "index", "Returns the current index of the bootstrap"], [7, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [7, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [49, 5, 1, 0, "random_state", "Set or get the instance random state"], [50, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [51, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [52, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [53, 5, 1, 0, "state", "Set or get the generator's state"], [54, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [55, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.IndependentSamplesBootstrap.__init__": [[7, 2, 2, 0, "args", "Positional arguments to bootstrap"], [7, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [7, 2, 2, "arch.bootstrap.IndependentSamplesBootstrap", "random_state", "Bootstrap where each input is independently resampled"], [7, 2, 2, "arch.bootstrap.IndependentSamplesBootstrap", "seed", "Bootstrap where each input is independently resampled"]], "arch.bootstrap.IndependentSamplesBootstrap.apply": [[41, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func."], [41, 2, 2, 0, "func", "Function the computes parameter values."], [41, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.bootstrap": [[42, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.clone": [[43, 2, 2, 0, "args", "Positional arguments to bootstrap"], [43, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [43, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.IndependentSamplesBootstrap.conf_int": [[44, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [44, 2, 2, 0, "func", "Function the computes parameter values."], [44, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [44, 2, 2, 0, "reps", "Number of bootstrap replications"], [44, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results."], [44, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'."], [44, 2, 2, 0, "size", "Coverage of confidence interval"], [44, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap."], [44, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap."], [44, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.IndependentSamplesBootstrap.cov": [[45, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [45, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [45, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [45, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.reset": [[50, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided."]], "arch.bootstrap.IndependentSamplesBootstrap.seed": [[51, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.IndependentSamplesBootstrap.set_state": [[52, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.IndependentSamplesBootstrap.var": [[55, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [55, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [55, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [55, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MCS": [[253, 3, 1, 0, "compute", "Compute the set of models in the confidence set."], [254, 5, 1, 0, "excluded", "List of model indices that are excluded from the MCS"], [255, 5, 1, 0, "included", "List of model indices that are included in the MCS"], [256, 5, 1, 0, "pvalues", "Model p-values for inclusion in the MCS"], [257, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [258, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"]], "arch.bootstrap.MCS.__init__": [[250, 2, 2, 0, "block_size", "Length of window to use in the bootstrap."], [250, 2, 2, 0, "bootstrap", "Bootstrap to use."], [250, 2, 2, 0, "losses", "T by k array containing losses from a set of models"], [250, 2, 2, 0, "method", "MCS test and elimination implementation method, either 'max' or 'R'. Default is 'R'."], [250, 2, 2, 0, "reps", "Number of bootstrap replications to uses."], [250, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value."], [250, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the mcs."]], "arch.bootstrap.MCS.seed": [[258, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.MovingBlockBootstrap": [[56, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [57, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [58, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [59, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [60, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [8, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [61, 5, 1, 0, "generator", "Set or get the instance PRNG"], [62, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [63, 5, 1, 0, "index", "The current index of the bootstrap"], [8, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [8, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [64, 5, 1, 0, "random_state", "Set or get the instance random state"], [65, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [66, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [67, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [68, 5, 1, 0, "state", "Set or get the generator's state"], [69, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [70, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.MovingBlockBootstrap.__init__": [[8, 2, 2, 0, "args", "Positional arguments to bootstrap"], [8, 2, 2, 0, "block_size", "Size of block to use"], [8, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [8, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results."], [8, 2, 2, 0, "seed", "Seed to use to ensure reproducable results."]], "arch.bootstrap.MovingBlockBootstrap.apply": [[56, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func."], [56, 2, 2, 0, "func", "Function the computes parameter values."], [56, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.bootstrap": [[57, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.clone": [[58, 2, 2, 0, "args", "Positional arguments to bootstrap"], [58, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [58, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.MovingBlockBootstrap.conf_int": [[59, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [59, 2, 2, 0, "func", "Function the computes parameter values."], [59, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [59, 2, 2, 0, "reps", "Number of bootstrap replications"], [59, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results."], [59, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'."], [59, 2, 2, 0, "size", "Coverage of confidence interval"], [59, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap."], [59, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap."], [59, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.MovingBlockBootstrap.cov": [[60, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [60, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [60, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [60, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.reset": [[65, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided."]], "arch.bootstrap.MovingBlockBootstrap.seed": [[66, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.MovingBlockBootstrap.set_state": [[67, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.MovingBlockBootstrap.var": [[70, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [70, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [70, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [70, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.SPA": [[259, 3, 1, 0, "better_models", "Returns set of models rejected as being equal-or-worse than the benchmark"], [260, 3, 1, 0, "compute", "Compute the bootstrap pvalue."], [261, 3, 1, 0, "critical_values", "Returns data-dependent critical values"], [262, 5, 1, 0, "pvalues", "P-values corresponding to the lower, consistent and upper p-values."], [263, 3, 1, 0, "reset", "Reset the bootstrap to its initial state."], [264, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [265, 3, 1, 0, "subset", "Sets a list of active models to run the SPA on.  Primarily for internal use."]], "arch.bootstrap.SPA.__init__": [[251, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [251, 2, 2, 0, "block_size", "Length of window to use in the bootstrap."], [251, 2, 2, 0, "bootstrap", "Bootstrap to use."], [251, 2, 2, 0, "models", "T  by k element array of alternative model losses"], [251, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization."], [251, 2, 2, 0, "reps", "Number of bootstrap replications to uses."], [251, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value."], [251, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials."]], "arch.bootstrap.SPA.better_models": [[259, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing superior models"], [259, 2, 2, 0, "pvalue_type", "String in 'lower', 'consistent', or 'upper' indicating which critical value to use."]], "arch.bootstrap.SPA.critical_values": [[261, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing the critical values."]], "arch.bootstrap.SPA.seed": [[264, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.SPA.subset": [[265, 2, 2, 0, "selector", "Boolean array indicating which columns to use when computing the p-values."]], "arch.bootstrap.StationaryBootstrap": [[71, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [72, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [73, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [74, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [75, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [9, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [76, 5, 1, 0, "generator", "Set or get the instance PRNG"], [77, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [78, 5, 1, 0, "index", "The current index of the bootstrap"], [9, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [9, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [79, 5, 1, 0, "random_state", "Set or get the instance random state"], [80, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [81, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [82, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [83, 5, 1, 0, "state", "Set or get the generator's state"], [84, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [85, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.StationaryBootstrap.__init__": [[9, 2, 2, 0, "args", "Positional arguments to bootstrap"], [9, 2, 2, 0, "block_size", "Average size of block to use"], [9, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [9, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results."], [9, 2, 2, 0, "seed", "Seed to use to ensure reproducable results."]], "arch.bootstrap.StationaryBootstrap.apply": [[71, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func."], [71, 2, 2, 0, "func", "Function the computes parameter values."], [71, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.bootstrap": [[72, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.clone": [[73, 2, 2, 0, "args", "Positional arguments to bootstrap"], [73, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [73, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.StationaryBootstrap.conf_int": [[74, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [74, 2, 2, 0, "func", "Function the computes parameter values."], [74, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [74, 2, 2, 0, "reps", "Number of bootstrap replications"], [74, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results."], [74, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'."], [74, 2, 2, 0, "size", "Coverage of confidence interval"], [74, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap."], [74, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap."], [74, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.StationaryBootstrap.cov": [[75, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [75, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [75, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [75, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.reset": [[80, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided."]], "arch.bootstrap.StationaryBootstrap.seed": [[81, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.StationaryBootstrap.set_state": [[82, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.StationaryBootstrap.var": [[85, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [85, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [85, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False)."], [85, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StepM": [[266, 3, 1, 0, "compute", "Compute the set of superior models."], [267, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [268, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [269, 5, 1, 0, "superior_models", "List of the indices or column names of the superior models"]], "arch.bootstrap.StepM.__init__": [[252, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [252, 2, 2, 0, "block_size", "Length of window to use in the bootstrap."], [252, 2, 2, 0, "bootstrap", "Bootstrap to use."], [252, 2, 2, 0, "models", "T by k element array of alternative model losses"], [252, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization."], [252, 2, 2, 0, "reps", "Number of bootstrap replications to uses."], [252, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value."], [252, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the comparison."], [252, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials."]], "arch.bootstrap.StepM.seed": [[268, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.optimal_block_length": [[10, 2, 2, 0, "x", "A one-dimensional or two-dimensional array-like."]], "arch.covariance": [[99, 0, 0, 1, "kernel", ""]], "arch.covariance.kernel": [[100, 1, 1, 0, "Andrews", "Alternative name of the QuadraticSpectral covariance estimator."], [101, 1, 1, 0, "Bartlett", "Bartlett's (Newey-West) kernel covariance estimation."], [102, 1, 1, 0, "CovarianceEstimate", "Covariance estimate using a long-run covariance estimator"], [103, 1, 1, 0, "CovarianceEstimator", "%(kernel_name)s kernel covariance estimation."], [104, 1, 1, 0, "Gallant", "Alternative name for Parzen covariance estimator."], [105, 1, 1, 0, "NeweyWest", "Alternative name for Bartlett covariance estimator."], [106, 1, 1, 0, "Parzen", "Parzen's kernel covariance estimation."], [107, 1, 1, 0, "ParzenCauchy", "Parzen's Cauchy kernel covariance estimation."], [108, 1, 1, 0, "ParzenGeometric", "Parzen's Geometric kernel covariance estimation."], [109, 1, 1, 0, "ParzenRiesz", "Parzen-Reisz kernel covariance estimation."], [110, 1, 1, 0, "QuadraticSpectral", "Quadratic-Spectral (Andrews') kernel covariance estimation."], [111, 1, 1, 0, "TukeyHamming", "Tukey-Hamming kernel covariance estimation."], [112, 1, 1, 0, "TukeyHanning", "Tukey-Hanning kernel covariance estimation."], [113, 1, 1, 0, "TukeyParzen", "Tukey-Parzen kernel covariance estimation."]], "arch.covariance.kernel.Andrews": [[114, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [115, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [116, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [117, 5, 1, 0, "cov", "The estimated covariances."], [118, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [119, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [120, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [121, 5, 1, 0, "name", "The covariance estimator's name."], [122, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [123, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Andrews.__init__": [[100, 2, 2, "arch.covariance.kernel.Andrews", "bandwidth", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 2, 2, "arch.covariance.kernel.Andrews", "center", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 2, 2, "arch.covariance.kernel.Andrews", "df_adjust", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 2, 2, "arch.covariance.kernel.Andrews", "force_int", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 2, 2, "arch.covariance.kernel.Andrews", "weights", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 2, 2, "arch.covariance.kernel.Andrews", "x", "Alternative name of the QuadraticSpectral covariance estimator."]], "arch.covariance.kernel.Bartlett": [[124, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [125, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [126, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [127, 5, 1, 0, "cov", "The estimated covariances."], [128, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [129, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [130, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [131, 5, 1, 0, "name", "The covariance estimator's name."], [132, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [133, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Bartlett.__init__": [[101, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [101, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [101, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [101, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [101, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [101, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.CovarianceEstimate": [[134, 5, 1, 0, "long_run", "The long-run covariance estimate."], [135, 5, 1, 0, "one_sided", "The one-sided covariance estimate."], [136, 5, 1, 0, "one_sided_strict", "The one-sided strict covariance estimate."], [137, 5, 1, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.CovarianceEstimate.__init__": [[102, 2, 2, 0, "columns", "Column labels to use if covariance estimates are returned as DataFrames."], [102, 2, 2, 0, "long_run", "The long-run covariance estimate."], [102, 2, 2, 0, "one_sided", "The one-sided-strict covariance estimate."], [102, 2, 2, 0, "one_sided_strict", "The one-sided strict covariance estimate."], [102, 2, 2, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.CovarianceEstimator": [[138, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [139, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [140, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [141, 5, 1, 0, "cov", "The estimated covariances."], [142, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [143, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [144, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [145, 5, 1, 0, "name", "The covariance estimator's name."], [146, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [147, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.CovarianceEstimator.__init__": [[103, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [103, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [103, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [103, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [103, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [103, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.Gallant": [[148, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [149, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [150, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [151, 5, 1, 0, "cov", "The estimated covariances."], [152, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [153, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [154, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [155, 5, 1, 0, "name", "The covariance estimator's name."], [156, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [157, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Gallant.__init__": [[104, 2, 2, "arch.covariance.kernel.Gallant", "bandwidth", "Alternative name for Parzen covariance estimator."], [104, 2, 2, "arch.covariance.kernel.Gallant", "center", "Alternative name for Parzen covariance estimator."], [104, 2, 2, "arch.covariance.kernel.Gallant", "df_adjust", "Alternative name for Parzen covariance estimator."], [104, 2, 2, "arch.covariance.kernel.Gallant", "force_int", "Alternative name for Parzen covariance estimator."], [104, 2, 2, "arch.covariance.kernel.Gallant", "weights", "Alternative name for Parzen covariance estimator."], [104, 2, 2, "arch.covariance.kernel.Gallant", "x", "Alternative name for Parzen covariance estimator."]], "arch.covariance.kernel.NeweyWest": [[158, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [159, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [160, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [161, 5, 1, 0, "cov", "The estimated covariances."], [162, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [163, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [164, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [165, 5, 1, 0, "name", "The covariance estimator's name."], [166, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [167, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.NeweyWest.__init__": [[105, 2, 2, "arch.covariance.kernel.NeweyWest", "bandwidth", "Alternative name for Bartlett covariance estimator."], [105, 2, 2, "arch.covariance.kernel.NeweyWest", "center", "Alternative name for Bartlett covariance estimator."], [105, 2, 2, "arch.covariance.kernel.NeweyWest", "df_adjust", "Alternative name for Bartlett covariance estimator."], [105, 2, 2, "arch.covariance.kernel.NeweyWest", "force_int", "Alternative name for Bartlett covariance estimator."], [105, 2, 2, "arch.covariance.kernel.NeweyWest", "weights", "Alternative name for Bartlett covariance estimator."], [105, 2, 2, "arch.covariance.kernel.NeweyWest", "x", "Alternative name for Bartlett covariance estimator."]], "arch.covariance.kernel.Parzen": [[168, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [169, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [170, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [171, 5, 1, 0, "cov", "The estimated covariances."], [172, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [173, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [174, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [175, 5, 1, 0, "name", "The covariance estimator's name."], [176, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [177, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Parzen.__init__": [[106, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [106, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [106, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [106, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [106, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [106, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenCauchy": [[178, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [179, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [180, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [181, 5, 1, 0, "cov", "The estimated covariances."], [182, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [183, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [184, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [185, 5, 1, 0, "name", "The covariance estimator's name."], [186, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [187, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenCauchy.__init__": [[107, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [107, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [107, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [107, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [107, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [107, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenGeometric": [[188, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [189, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [190, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [191, 5, 1, 0, "cov", "The estimated covariances."], [192, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [193, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [194, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [195, 5, 1, 0, "name", "The covariance estimator's name."], [196, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [197, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenGeometric.__init__": [[108, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [108, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [108, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [108, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [108, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [108, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenRiesz": [[198, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [199, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [200, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [201, 5, 1, 0, "cov", "The estimated covariances."], [202, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [203, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [204, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [205, 5, 1, 0, "name", "The covariance estimator's name."], [206, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [207, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenRiesz.__init__": [[109, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [109, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [109, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [109, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [109, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [109, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.QuadraticSpectral": [[208, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [209, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [210, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [211, 5, 1, 0, "cov", "The estimated covariances."], [212, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [213, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [214, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [215, 5, 1, 0, "name", "The covariance estimator's name."], [216, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [217, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.QuadraticSpectral.__init__": [[110, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [110, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [110, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [110, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [110, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [110, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHamming": [[218, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [219, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [220, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [221, 5, 1, 0, "cov", "The estimated covariances."], [222, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [223, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [224, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [225, 5, 1, 0, "name", "The covariance estimator's name."], [226, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [227, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHamming.__init__": [[111, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [111, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [111, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [111, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [111, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [111, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHanning": [[228, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [229, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [230, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [231, 5, 1, 0, "cov", "The estimated covariances."], [232, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [233, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [234, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [235, 5, 1, 0, "name", "The covariance estimator's name."], [236, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [237, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHanning.__init__": [[112, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [112, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [112, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [112, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [112, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [112, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyParzen": [[238, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [239, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [240, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [241, 5, 1, 0, "cov", "The estimated covariances."], [242, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [243, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [244, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [245, 5, 1, 0, "name", "The covariance estimator's name."], [246, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [247, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyParzen.__init__": [[113, 2, 2, 0, "bandwidth", "The kernel's bandwidth."], [113, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [113, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance."], [113, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [113, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used."], [113, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.typing": [[273, 4, 1, 0, "AnyArray", "alias of ndarray[Any, Any]"], [273, 4, 1, 0, "AnyPandas", "alias of Series | DataFrame"], [273, 4, 1, 0, "ArrayLike", "alias of ndarray | DataFrame | Series"], [273, 4, 1, 0, "ArrayLike1D", "alias of ndarray | Series"], [273, 4, 1, 0, "ArrayLike2D", "alias of ndarray | DataFrame"], [273, 4, 1, 0, "BoolArray", "alias of ndarray[Any, dtype[bool]]"], [273, 4, 1, 0, "BootstrapIndexT", "alias of ndarray[Any, dtype[longlong]] | tuple[ndarray[Any, dtype[longlong]], ...] | tuple[list[ndarray[Any, dtype[longlong]]], dict[str, ndarray[Any, dtype[longlong]]]]"], [273, 4, 1, 0, "DateLike", "alias of str | datetime | datetime64 | Timestamp"], [273, 4, 1, 0, "Float64Array", "alias of ndarray[Any, dtype[float64]]"], [273, 1, 1, 0, "FloatOrArray", "alias of TypeVar('FloatOrArray', float, ~numpy.ndarray)"], [273, 4, 1, 0, "ForecastingMethod", "alias of Literal['analytic', 'simulation', 'bootstrap']"], [273, 4, 1, 0, "Int32Array", "alias of ndarray[Any, dtype[int32]]"], [273, 4, 1, 0, "Int64Array", "alias of ndarray[Any, dtype[longlong]]"], [273, 4, 1, 0, "IntArray", "alias of ndarray[Any, dtype[int64]]"], [273, 4, 1, 0, "Label", "alias of Hashable | None"], [273, 4, 1, 0, "NDArray", "alias of ndarray"], [273, 1, 1, 0, "NDArrayOrFrame", "alias of TypeVar('NDArrayOrFrame', ~numpy.ndarray[~typing.Any, ~numpy.dtype[~numpy.float64]], ~pandas.core.frame.DataFrame)"], [273, 4, 1, 0, "RNGType", "alias of Callable[[int | tuple[int, ...]], ndarray[Any, dtype[float64]]]"], [273, 4, 1, 0, "RandomStateState", "alias of tuple[str, ndarray[Any, dtype[uint32]], int, int, float]"], [273, 4, 1, 0, "Uint32Array", "alias of ndarray[Any, dtype[uint32]]"], [273, 4, 1, 0, "UnitRootTrend", "alias of Literal['n', 'c', 'ct', 'ctt']"]], "arch.unitroot": [[275, 1, 1, 0, "ADF", "Augmented Dickey-Fuller unit root test"], [276, 1, 1, 0, "DFGLS", "Elliott, Rothenberg and Stock's (ers) GLS detrended Dickey-Fuller"], [277, 1, 1, 0, "KPSS", "Kwiatkowski, Phillips, Schmidt and Shin (KPSS) stationarity test"], [278, 1, 1, 0, "PhillipsPerron", "Phillips-Perron unit root test"], [279, 1, 1, 0, "VarianceRatio", "Variance Ratio test of a random walk."], [280, 1, 1, 0, "ZivotAndrews", "Zivot-Andrews structural-break unit-root test"], [281, 6, 1, 0, "auto_bandwidth", "Automatic bandwidth selection of Andrews (1991) and Newey & West (1994)."], [274, 0, 0, 1, "cointegration", ""]], "arch.unitroot.ADF": [[291, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [292, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [293, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [294, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [295, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [296, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [297, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [298, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [299, 5, 1, 0, "stat", "The test statistic for a unit root"], [300, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [301, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [302, 5, 1, 0, "valid_trends", "List of valid trend terms."], [303, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.ADF.__init__": [[275, 2, 2, 0, "lags", "The number of lags to use in the ADF regression."], [275, 2, 2, 0, "low_memory", "Flag indicating whether to use a low memory implementation of the lag selection algorithm."], [275, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [275, 2, 2, 0, "method", "The method to use when selecting the lag length"], [275, 2, 2, 0, "trend", "The trend component to include in the test"], [275, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.DFGLS": [[304, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [305, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [306, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [307, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [308, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [309, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [310, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [311, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [312, 5, 1, 0, "stat", "The test statistic for a unit root"], [313, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [314, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [315, 5, 1, 0, "valid_trends", "List of valid trend terms."], [316, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.DFGLS.__init__": [[276, 2, 2, 0, "lags", "The number of lags to use in the ADF regression."], [276, 2, 2, "arch.unitroot.DFGLS", "low_memory", "Elliott, Rothenberg and Stock's (ers) GLS detrended Dickey-Fuller"], [276, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length."], [276, 2, 2, 0, "method", "The method to use when selecting the lag length"], [276, 2, 2, 0, "trend", "The trend component to include in the test"], [276, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.KPSS": [[317, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [318, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [319, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [320, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [321, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [322, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [323, 5, 1, 0, "stat", "The test statistic for a unit root"], [324, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [325, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [326, 5, 1, 0, "valid_trends", "List of valid trend terms."], [327, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.KPSS.__init__": [[277, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance."], [277, 2, 2, 0, "trend", "\"c\" - Include a constant (Default) \"ct\" - Include a constant and linear time trend"], [277, 2, 2, 0, "y", "The data to test for stationarity"]], "arch.unitroot.PhillipsPerron": [[328, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [329, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [330, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [331, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [332, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [333, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [334, 5, 1, 0, "regression", "Returns OLS regression results for the specification used in the test"], [335, 5, 1, 0, "stat", "The test statistic for a unit root"], [336, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [337, 5, 1, 0, "test_type", "Gets or sets the test type returned by stat. Valid values are \"tau\" or \"rho\""], [338, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [339, 5, 1, 0, "valid_trends", "List of valid trend terms."], [340, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.PhillipsPerron.__init__": [[278, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance."], [278, 2, 2, 0, "test_type", "The test to use when computing the test statistic."], [278, 2, 2, 0, "trend", "The trend component to include in the test"], [278, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.VarianceRatio": [[341, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [342, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [343, 5, 1, 0, "debiased", "Sets of gets the indicator to use debiased variances in the ratio"], [344, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [345, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [346, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [347, 5, 1, 0, "overlap", "Sets of gets the indicator to use overlapping returns in the long-period variance estimator"], [348, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [349, 5, 1, 0, "robust", "Sets of gets the indicator to use a heteroskedasticity robust variance estimator"], [350, 5, 1, 0, "stat", "The test statistic for a unit root"], [351, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [352, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [353, 5, 1, 0, "valid_trends", "List of valid trend terms."], [354, 5, 1, 0, "vr", "The ratio of the long block lags-period variance to the 1-period variance"], [355, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.VarianceRatio.__init__": [[279, 2, 2, 0, "debiased", "Indicates whether to use a debiased version of the test."], [279, 2, 2, 0, "lags", "The number of periods to used in the multi-period variance, which is the numerator of the test statistic."], [279, 2, 2, 0, "overlap", "Indicates whether to use all overlapping blocks."], [279, 2, 2, 0, "robust", "Indicates whether to use heteroskedasticity robust inference."], [279, 2, 2, 0, "trend", "\"c\" allows for a non-zero drift in the random walk, while \"n\" requires that the increments to y are mean 0"], [279, 2, 2, 0, "y", "The data to test for a random walk"]], "arch.unitroot.ZivotAndrews": [[356, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [357, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [358, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [359, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [360, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [361, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [362, 5, 1, 0, "stat", "The test statistic for a unit root"], [363, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [364, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [365, 5, 1, 0, "valid_trends", "List of valid trend terms."], [366, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.ZivotAndrews.__init__": [[280, 2, 2, 0, "lags", "The number of lags to use in the ADF regression."], [280, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [280, 2, 2, 0, "method", "The method to use when selecting the lag length"], [280, 2, 2, 0, "trend", "The trend component to include in the test"], [280, 2, 2, 0, "trim", "percentage of series at begin/end to exclude from break-period calculation in range [0, 0.333] (default=0.15)"], [280, 2, 2, 0, "y", "data series"]], "arch.unitroot.auto_bandwidth": [[281, 2, 2, 0, "kernel", "The kernel function to use for selecting the bandwidth"], [281, 2, 2, 0, "y", "Data on which to apply the bandwidth selection"]], "arch.unitroot.cointegration": [[282, 1, 1, 0, "CanonicalCointegratingReg", "Canonical Cointegrating Regression cointegrating vector estimation."], [283, 1, 1, 0, "CointegrationAnalysisResults", "summary()"], [284, 1, 1, 0, "DynamicOLS", "Dynamic OLS (DOLS) cointegrating vector estimation"], [285, 1, 1, 0, "DynamicOLSResults", "Estimation results for Dynamic OLS models"], [286, 1, 1, 0, "EngleGrangerTestResults", "Results class for Engle-Granger cointegration tests."], [287, 1, 1, 0, "FullyModifiedOLS", "Fully Modified OLS cointegrating vector estimation."], [288, 1, 1, 0, "PhillipsOuliarisTestResults", "plot([axes, title])"], [289, 6, 1, 0, "engle_granger", "Test for cointegration within a set of time series."], [290, 6, 1, 0, "phillips_ouliaris", "Test for cointegration within a set of time series."]], "arch.unitroot.cointegration.CanonicalCointegratingReg": [[367, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.__init__": [[282, 2, 2, 0, "trend", "Trend to include in the cointegrating regression."], [282, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [282, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression."], [282, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.fit": [[367, 2, 2, 0, "bandwidth", "The bandwidth to use."], [367, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression."], [367, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [367, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [367, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators."]], "arch.unitroot.cointegration.CointegrationAnalysisResults": [[368, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [369, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [370, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [371, 5, 1, 0, "long_run_variance", "Long-run variance estimate used in the parameter covariance estimator"], [372, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [373, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [374, 5, 1, 0, "resid", "The model residuals"], [375, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [376, 5, 1, 0, "rsquared", "The model R\u00b2"], [377, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [378, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [379, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [380, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.CointegrationAnalysisResults.__init__": [[283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "cov", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "df_adjust", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "estimator_type", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "kernel_est", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "num_x", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "omega_112", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "params", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "resid", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "rsquared", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "rsquared_adj", "summary()"], [283, 2, 2, "arch.unitroot.cointegration.CointegrationAnalysisResults", "trend", "summary()"]], "arch.unitroot.cointegration.DynamicOLS": [[381, 3, 1, 0, "fit", "Estimate the Dynamic OLS regression"]], "arch.unitroot.cointegration.DynamicOLS.__init__": [[284, 2, 2, 0, "common", "Flag indicating that lags and leads should be restricted to the same value."], [284, 2, 2, 0, "lags", "The number of lags to include in the model."], [284, 2, 2, 0, "leads", "The number of leads to include in the model."], [284, 2, 2, 0, "max_lag", "The maximum lag to consider."], [284, 2, 2, 0, "max_lead", "The maximum lead to consider."], [284, 2, 2, 0, "method", "The method used to select lag length when lags or leads is None."], [284, 2, 2, 0, "trend", "Trend to include in the cointegrating regression."], [284, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [284, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.DynamicOLS.fit": [[381, 2, 2, 0, "bandwidth", "The bandwidth to use."], [381, 2, 2, 0, "cov_type", "Either \"unadjusted\" (or is equivalent \"homoskedastic\") or \"robust\" (or its equivalent \"kernel\")."], [381, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression."], [381, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [381, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators."]], "arch.unitroot.cointegration.DynamicOLSResults": [[382, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [383, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [384, 5, 1, 0, "cov_type", "The type of parameter covariance estimator used"], [385, 5, 1, 0, "full_cov", "Parameter covariance of the all model parameters, incl. leads and lags"], [386, 5, 1, 0, "full_params", "The complete set of parameters, including leads and lags"], [387, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [388, 5, 1, 0, "lags", "The number of lags included in the model"], [389, 5, 1, 0, "leads", "The number of leads included in the model"], [390, 5, 1, 0, "long_run_variance", "The long-run variance of the regression residual."], [391, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [392, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [393, 5, 1, 0, "resid", "The model residuals"], [394, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [395, 5, 1, 0, "rsquared", "The model R\u00b2"], [396, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [397, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [398, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [399, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.DynamicOLSResults.__init__": [[285, 2, 2, 0, "cov", "The estimated parameter covariance."], [285, 2, 2, 0, "cov_type", "The type of the parameter covariance estimator used."], [285, 2, 2, 0, "df_adjust", "Whether to degree of freedom adjust the estimator."], [285, 2, 2, 0, "kernel_est", "The covariance estimator instance used to estimate the parameter covariance."], [285, 2, 2, 0, "lags", "The number of lags included in the model."], [285, 2, 2, 0, "leads", "The number of leads included in the model."], [285, 2, 2, "arch.unitroot.cointegration.DynamicOLSResults", "num_x", "Estimation results for Dynamic OLS models"], [285, 2, 2, 0, "params", "The estimated model parameters."], [285, 2, 2, 0, "reg_results", "Regression results from fitting statsmodels OLS."], [285, 2, 2, 0, "resid", "The model residuals."], [285, 2, 2, "arch.unitroot.cointegration.DynamicOLSResults", "trend", "Estimation results for Dynamic OLS models"]], "arch.unitroot.cointegration.DynamicOLSResults.summary": [[398, 2, 2, 0, "full", "Flag indicating whether to include all estimated parameters (True) or only the parameters of the cointegrating vector"]], "arch.unitroot.cointegration.EngleGrangerTestResults": [[400, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [401, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [402, 5, 1, 0, "critical_values", "Critical Values"], [403, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [404, 5, 1, 0, "lags", "The number of lags used in the Augmented Dickey-Fuller regression."], [405, 5, 1, 0, "max_lags", "The maximum number of lags used in the lag-length selection."], [406, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [407, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [408, 3, 1, 0, "plot", "Plot the cointegration residuals."], [409, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [410, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [411, 5, 1, 0, "rho", "The estimated coefficient in the Dickey-Fuller Test"], [412, 5, 1, 0, "stat", "The test statistic."], [413, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [414, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.EngleGrangerTestResults.__init__": [[286, 2, 2, 0, "adf", "The ADF instance used to perform the test and lag selection."], [286, 2, 2, 0, "alternative", "The alternative hypothesis."], [286, 2, 2, 0, "crit_vals", "The critical values of the Engle-Granger specific to the sample size and model dimension."], [286, 2, 2, 0, "null", "The null hypothesis."], [286, 2, 2, 0, "order", "The number of stochastic trends in the null distribution."], [286, 2, 2, 0, "pvalue", "The pvalue of the Engle-Granger test statistic."], [286, 2, 2, 0, "stat", "The Engle-Granger test statistic."], [286, 2, 2, 0, "trend", "The model's trend description."], [286, 2, 2, 0, "xsection", "The OLS results used in the cross-sectional regression."]], "arch.unitroot.cointegration.EngleGrangerTestResults.plot": [[408, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [408, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.FullyModifiedOLS": [[415, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.FullyModifiedOLS.__init__": [[287, 2, 2, 0, "trend", "Trend to include in the cointegrating regression."], [287, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [287, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression."], [287, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.FullyModifiedOLS.fit": [[415, 2, 2, 0, "bandwidth", "The bandwidth to use."], [415, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression."], [415, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [415, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [415, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators."]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults": [[416, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [417, 5, 1, 0, "bandwidth", "Bandwidth used by the long-run covariance estimator"], [418, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [419, 5, 1, 0, "critical_values", "Critical Values"], [420, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [421, 5, 1, 0, "kernel", "Name of the long-run covariance estimator"], [422, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [423, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [424, 3, 1, 0, "plot", "Plot the cointegration residuals."], [425, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [426, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [427, 5, 1, 0, "stat", "The test statistic."], [428, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [429, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults.__init__": [[288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "alternative", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "crit_vals", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "kernel_est", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "null", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "order", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "pvalue", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "rho", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "stat", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "test_type", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "trend", "plot([axes, title])"], [288, 2, 2, "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "xsection", "plot([axes, title])"]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot": [[424, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [424, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.engle_granger": [[289, 2, 2, 0, "lags", "The number of lagged differences to include in the Augmented Dickey-Fuller test used on the residuals of the"], [289, 2, 2, 0, "max_lags", "The maximum number of lags to consider when using automatic lag-length in the Augmented Dickey-Fuller regression."], [289, 2, 2, 0, "method", "The method used to select the number of lags included in the Augmented Dickey-Fuller regression."], [289, 2, 2, 0, "trend", "Trend to include in the cointegrating regression."], [289, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [289, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.phillips_ouliaris": [[290, 2, 2, 0, "bandwidth", "The bandwidth to use."], [290, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [290, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators."], [290, 2, 2, 0, "test_type", "The test statistic to compute."], [290, 2, 2, 0, "trend", "Trend to include in the cointegrating regression."], [290, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [290, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.univariate": [[438, 1, 1, 0, "APARCH", "Asymmetric Power ARCH (APARCH) volatility process"], [439, 1, 1, 0, "ARCH", "ARCH process"], [440, 1, 1, 0, "ARCHInMean", "(G)ARCH-in-mean model and simulation"], [441, 1, 1, 0, "ARX", "Autoregressive model with optional exogenous regressors estimation and simulation"], [442, 1, 1, 0, "ConstantMean", "Constant mean model estimation and simulation."], [443, 1, 1, 0, "ConstantVariance", "Constant volatility process"], [444, 1, 1, 0, "EGARCH", "EGARCH model estimation"], [445, 1, 1, 0, "EWMAVariance", "Exponentially Weighted Moving-Average (RiskMetrics) Variance process"], [446, 1, 1, 0, "FIGARCH", "FIGARCH model"], [447, 1, 1, 0, "FixedVariance", "Fixed volatility process"], [448, 1, 1, 0, "GARCH", "GARCH and related model estimation"], [449, 1, 1, 0, "GeneralizedError", "Generalized Error distribution for use with ARCH models"], [450, 1, 1, 0, "HARCH", "Heterogeneous ARCH process"], [451, 1, 1, 0, "HARX", "Heterogeneous Autoregression (HAR), with optional exogenous regressors, model estimation and simulation"], [452, 1, 1, 0, "LS", "Least squares model estimation and simulation"], [453, 1, 1, 0, "MIDASHyperbolic", "MIDAS Hyperbolic ARCH process"], [454, 1, 1, 0, "Normal", "Standard normal distribution for use with ARCH models"], [455, 1, 1, 0, "RiskMetrics2006", "RiskMetrics 2006 Variance process"], [456, 1, 1, 0, "SkewStudent", "Standardized Skewed Student's distribution for use with ARCH models"], [457, 1, 1, 0, "StudentsT", "Standardized Student's distribution for use with ARCH models"], [458, 1, 1, 0, "ZeroMean", "Model with zero conditional mean estimation and simulation"], [907, 6, 1, 0, "arch_model", "Initialization of common ARCH model specifications"]], "arch.univariate.APARCH": [[468, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [469, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [470, 3, 1, 0, "bounds", "Returns bounds for parameters"], [471, 5, 1, 0, "common_asym", "The value of delta in the model. NaN is delta is estimated."], [472, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [473, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [474, 5, 1, 0, "delta", "The value of delta in the model. NaN is delta is estimated."], [475, 3, 1, 0, "forecast", "Forecast volatility from the model"], [476, 5, 1, 0, "name", "The name of the volatility process"], [477, 5, 1, 0, "num_params", "The number of parameters in the model"], [478, 3, 1, 0, "parameter_names", "Names of model parameters"], [479, 3, 1, 0, "simulate", "Simulate data from the model"], [480, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [481, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [482, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [483, 3, 1, 0, "update", "Compute the variance for a single observation"], [484, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [485, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [486, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.APARCH.__init__": [[438, 2, 2, 0, "common_asym", "Restrict all asymmetry terms to share the same asymmetry parameter."], [438, 2, 2, 0, "delta", "Value to use for a fixed delta in the APARCH model."], [438, 2, 2, 0, "o", "Order of the asymmetric innovation."], [438, 2, 2, 0, "p", "Order of the symmetric innovation."], [438, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.APARCH.backcast": [[468, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.backcast_transform": [[469, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.APARCH.bounds": [[470, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.compute_variance": [[472, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [472, 2, 2, 0, "parameters", "Model parameters"], [472, 2, 2, 0, "resids", "Vector of mean zero residuals"], [472, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [472, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.forecast": [[475, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [475, 2, 2, 0, "horizon", "Forecast horizon."], [475, 2, 2, 0, "method", "Method to use when producing the forecast."], [475, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [475, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [475, 2, 2, 0, "resids", "Residuals to use in the recursion"], [475, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [475, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [475, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [475, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.simulate": [[479, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [479, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [479, 2, 2, 0, "nobs", "Number of data points to simulate"], [479, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [479, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.APARCH.starting_values": [[481, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.APARCH.update": [[483, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [483, 2, 2, 0, "index", "The numerical index of the variance to compute"], [483, 2, 2, 0, "parameters", "The variance model parameters"], [483, 2, 2, 0, "resids", "The residual array."], [483, 2, 2, 0, "sigma2", "The array containing the variances."], [483, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.variance_bounds": [[485, 2, 2, 0, "power", "Power used in the model."], [485, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCH": [[487, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [488, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [489, 3, 1, 0, "bounds", "Returns bounds for parameters"], [490, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [491, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [492, 3, 1, 0, "forecast", "Forecast volatility from the model"], [493, 5, 1, 0, "name", "The name of the volatility process"], [494, 5, 1, 0, "num_params", "The number of parameters in the model"], [495, 3, 1, 0, "parameter_names", "Names of model parameters"], [496, 3, 1, 0, "simulate", "Simulate data from the model"], [497, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [498, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [499, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [500, 3, 1, 0, "update", "Compute the variance for a single observation"], [501, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [502, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [503, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ARCH.__init__": [[439, 2, 2, 0, "p", "Order of the symmetric innovation"]], "arch.univariate.ARCH.backcast": [[487, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.backcast_transform": [[488, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ARCH.bounds": [[489, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.compute_variance": [[490, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [490, 2, 2, 0, "parameters", "Model parameters"], [490, 2, 2, 0, "resids", "Vector of mean zero residuals"], [490, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [490, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.forecast": [[492, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [492, 2, 2, 0, "horizon", "Forecast horizon."], [492, 2, 2, 0, "method", "Method to use when producing the forecast."], [492, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [492, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [492, 2, 2, 0, "resids", "Residuals to use in the recursion"], [492, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [492, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [492, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [492, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.simulate": [[496, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [496, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [496, 2, 2, 0, "nobs", "Number of data points to simulate"], [496, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [496, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ARCH.starting_values": [[498, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ARCH.update": [[500, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [500, 2, 2, 0, "index", "The numerical index of the variance to compute"], [500, 2, 2, 0, "parameters", "The variance model parameters"], [500, 2, 2, 0, "resids", "The residual array."], [500, 2, 2, 0, "sigma2", "The array containing the variances."], [500, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.variance_bounds": [[502, 2, 2, 0, "power", "Power used in the model."], [502, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCHInMean": [[504, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [505, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [506, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [507, 5, 1, 0, "distribution", "Set or gets the error distribution"], [508, 3, 1, 0, "fit", "Estimate model parameters"], [509, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [510, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [511, 5, 1, 0, "form", "The form of the conditional variance in the mean"], [512, 5, 1, 0, "name", "The name of the model."], [513, 5, 1, 0, "num_params", "Returns the number of parameters"], [514, 3, 1, 0, "parameter_names", "List of parameters names"], [515, 3, 1, 0, "resids", "Compute model residuals"], [516, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [517, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [518, 5, 1, 0, "volatility", "Set or gets the volatility process"], [519, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [520, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARCHInMean.__init__": [[440, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [440, 2, 2, 0, "distribution", "Error distribution to use in the model"], [440, 2, 2, 0, "form", "The form of the conditional variance that appears in the mean equation."], [440, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters."], [440, 2, 2, 0, "lags", "Description of lag structure of the HAR."], [440, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation."], [440, 2, 2, 0, "volatility", "Volatility process to use in the model."], [440, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [440, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARCHInMean.compute_param_cov": [[505, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [505, 2, 2, 0, "params", "Model parameters"], [505, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARCHInMean.fit": [[508, 2, 2, 0, "backcast", "Value to use as backcast."], [508, 2, 2, 0, "cov_type", "Estimation method of parameter covariance."], [508, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing."], [508, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [508, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [508, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize."], [508, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [508, 2, 2, 0, "starting_values", "Array of starting values to use."], [508, 2, 2, 0, "tol", "Tolerance for termination."], [508, 2, 2, 0, "update_freq", "Frequency of iteration updates."]], "arch.univariate.ARCHInMean.fix": [[509, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [509, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [509, 2, 2, 0, "params", "User specified parameters to use when generating the result."]], "arch.univariate.ARCHInMean.forecast": [[510, 2, 2, 0, "align", "Either 'origin' or 'target'."], [510, 2, 2, 0, "horizon", "Number of steps to forecast"], [510, 2, 2, 0, "method", "Method to use when producing the forecast."], [510, 2, 2, 0, "params", "Parameters required to forecast."], [510, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [510, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [510, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [510, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [510, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [510, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.ARCHInMean.resids": [[515, 2, 2, 0, "params", "Model parameters"], [515, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [515, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARCHInMean.simulate": [[516, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [516, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model."], [516, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [516, 2, 2, 0, "nobs", "Length of series to simulate"], [516, 2, 2, 0, "params", "Parameters to use when simulating the model."], [516, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ARX": [[521, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [522, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [523, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [524, 5, 1, 0, "distribution", "Set or gets the error distribution"], [525, 3, 1, 0, "fit", "Estimate model parameters"], [526, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [527, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [528, 5, 1, 0, "name", "The name of the model."], [529, 5, 1, 0, "num_params", "Returns the number of parameters"], [530, 3, 1, 0, "parameter_names", "List of parameters names"], [531, 3, 1, 0, "resids", "Compute model residuals"], [532, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [533, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [534, 5, 1, 0, "volatility", "Set or gets the volatility process"], [535, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [536, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARX.__init__": [[441, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [441, 2, 2, 0, "distribution", "Error distribution to use in the model"], [441, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters."], [441, 2, 2, 0, "lags", "Description of lag structure of the HAR."], [441, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation."], [441, 2, 2, 0, "volatility", "Volatility process to use in the model"], [441, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [441, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARX.compute_param_cov": [[522, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [522, 2, 2, 0, "params", "Model parameters"], [522, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARX.fit": [[525, 2, 2, 0, "backcast", "Value to use as backcast."], [525, 2, 2, 0, "cov_type", "Estimation method of parameter covariance."], [525, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing."], [525, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [525, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [525, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize."], [525, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [525, 2, 2, 0, "starting_values", "Array of starting values to use."], [525, 2, 2, 0, "tol", "Tolerance for termination."], [525, 2, 2, 0, "update_freq", "Frequency of iteration updates."]], "arch.univariate.ARX.fix": [[526, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [526, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [526, 2, 2, 0, "params", "User specified parameters to use when generating the result."]], "arch.univariate.ARX.forecast": [[527, 2, 2, 0, "align", "Either 'origin' or 'target'."], [527, 2, 2, 0, "horizon", "Number of steps to forecast"], [527, 2, 2, 0, "method", "Method to use when producing the forecast."], [527, 2, 2, 0, "params", "Parameters required to forecast."], [527, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [527, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [527, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [527, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [527, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [527, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.ARX.resids": [[531, 2, 2, 0, "params", "Model parameters"], [531, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [531, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARX.simulate": [[532, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [532, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model."], [532, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [532, 2, 2, 0, "nobs", "Length of series to simulate"], [532, 2, 2, 0, "params", "Parameters to use when simulating the model."], [532, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ConstantMean": [[537, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [538, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [539, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [540, 5, 1, 0, "distribution", "Set or gets the error distribution"], [541, 3, 1, 0, "fit", "Estimate model parameters"], [542, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [543, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [544, 5, 1, 0, "name", "The name of the model."], [545, 5, 1, 0, "num_params", "Returns the number of parameters"], [546, 3, 1, 0, "parameter_names", "List of parameters names"], [547, 3, 1, 0, "resids", "Compute model residuals"], [548, 3, 1, 0, "simulate", "Simulated data from a constant mean model"], [549, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [550, 5, 1, 0, "volatility", "Set or gets the volatility process"], [551, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [552, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ConstantMean.__init__": [[442, 2, 2, 0, "distribution", "Error distribution to use in the model"], [442, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters."], [442, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation."], [442, 2, 2, 0, "volatility", "Volatility process to use in the model"], [442, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ConstantMean.compute_param_cov": [[538, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [538, 2, 2, 0, "params", "Model parameters"], [538, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ConstantMean.fit": [[541, 2, 2, 0, "backcast", "Value to use as backcast."], [541, 2, 2, 0, "cov_type", "Estimation method of parameter covariance."], [541, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing."], [541, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [541, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [541, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize."], [541, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [541, 2, 2, 0, "starting_values", "Array of starting values to use."], [541, 2, 2, 0, "tol", "Tolerance for termination."], [541, 2, 2, 0, "update_freq", "Frequency of iteration updates."]], "arch.univariate.ConstantMean.fix": [[542, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [542, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [542, 2, 2, 0, "params", "User specified parameters to use when generating the result."]], "arch.univariate.ConstantMean.forecast": [[543, 2, 2, 0, "align", "Either 'origin' or 'target'."], [543, 2, 2, 0, "horizon", "Number of steps to forecast"], [543, 2, 2, 0, "method", "Method to use when producing the forecast."], [543, 2, 2, 0, "params", "Parameters required to forecast."], [543, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [543, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [543, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [543, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [543, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [543, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.ConstantMean.resids": [[547, 2, 2, 0, "params", "Model parameters"], [547, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [547, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ConstantMean.simulate": [[548, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [548, 2, 2, 0, "initial_value", "This value is not used."], [548, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [548, 2, 2, 0, "nobs", "Length of series to simulate"], [548, 2, 2, 0, "params", "Parameters to use when simulating the model."], [548, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.ConstantVariance": [[553, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [554, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [555, 3, 1, 0, "bounds", "Returns bounds for parameters"], [556, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [557, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [558, 3, 1, 0, "forecast", "Forecast volatility from the model"], [559, 5, 1, 0, "name", "The name of the volatility process"], [560, 5, 1, 0, "num_params", "The number of parameters in the model"], [561, 3, 1, 0, "parameter_names", "Names of model parameters"], [562, 3, 1, 0, "simulate", "Simulate data from the model"], [563, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [564, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [565, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [566, 3, 1, 0, "update", "Compute the variance for a single observation"], [567, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [568, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [569, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ConstantVariance.backcast": [[553, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.backcast_transform": [[554, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ConstantVariance.bounds": [[555, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.compute_variance": [[556, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [556, 2, 2, 0, "parameters", "Model parameters"], [556, 2, 2, 0, "resids", "Vector of mean zero residuals"], [556, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [556, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.forecast": [[558, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [558, 2, 2, 0, "horizon", "Forecast horizon."], [558, 2, 2, 0, "method", "Method to use when producing the forecast."], [558, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [558, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [558, 2, 2, 0, "resids", "Residuals to use in the recursion"], [558, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [558, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [558, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [558, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.simulate": [[562, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [562, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [562, 2, 2, 0, "nobs", "Number of data points to simulate"], [562, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [562, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ConstantVariance.starting_values": [[564, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ConstantVariance.update": [[566, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [566, 2, 2, 0, "index", "The numerical index of the variance to compute"], [566, 2, 2, 0, "parameters", "The variance model parameters"], [566, 2, 2, 0, "resids", "The residual array."], [566, 2, 2, 0, "sigma2", "The array containing the variances."], [566, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.variance_bounds": [[568, 2, 2, 0, "power", "Power used in the model."], [568, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EGARCH": [[570, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [571, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [572, 3, 1, 0, "bounds", "Returns bounds for parameters"], [573, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [574, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [575, 3, 1, 0, "forecast", "Forecast volatility from the model"], [576, 5, 1, 0, "name", "The name of the volatility process"], [577, 5, 1, 0, "num_params", "The number of parameters in the model"], [578, 3, 1, 0, "parameter_names", "Names of model parameters"], [579, 3, 1, 0, "simulate", "Simulate data from the model"], [580, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [581, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [582, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [583, 3, 1, 0, "update", "Compute the variance for a single observation"], [584, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [585, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [586, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EGARCH.__init__": [[444, 2, 2, 0, "o", "Order of the asymmetric innovation"], [444, 2, 2, 0, "p", "Order of the symmetric innovation"], [444, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.EGARCH.backcast": [[570, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.backcast_transform": [[571, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EGARCH.bounds": [[572, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.compute_variance": [[573, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [573, 2, 2, 0, "parameters", "Model parameters"], [573, 2, 2, 0, "resids", "Vector of mean zero residuals"], [573, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [573, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.forecast": [[575, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [575, 2, 2, 0, "horizon", "Forecast horizon."], [575, 2, 2, 0, "method", "Method to use when producing the forecast."], [575, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [575, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [575, 2, 2, 0, "resids", "Residuals to use in the recursion"], [575, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [575, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [575, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [575, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.simulate": [[579, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [579, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [579, 2, 2, 0, "nobs", "Number of data points to simulate"], [579, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [579, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EGARCH.starting_values": [[581, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EGARCH.update": [[583, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [583, 2, 2, 0, "index", "The numerical index of the variance to compute"], [583, 2, 2, 0, "parameters", "The variance model parameters"], [583, 2, 2, 0, "resids", "The residual array."], [583, 2, 2, 0, "sigma2", "The array containing the variances."], [583, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.variance_bounds": [[585, 2, 2, 0, "power", "Power used in the model."], [585, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EWMAVariance": [[587, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [588, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [589, 3, 1, 0, "bounds", "Returns bounds for parameters"], [590, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [591, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [592, 3, 1, 0, "forecast", "Forecast volatility from the model"], [593, 5, 1, 0, "name", "The name of the volatility process"], [594, 5, 1, 0, "num_params", "The number of parameters in the model"], [595, 3, 1, 0, "parameter_names", "Names of model parameters"], [596, 3, 1, 0, "simulate", "Simulate data from the model"], [597, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [598, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [599, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [600, 3, 1, 0, "update", "Compute the variance for a single observation"], [601, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [602, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [603, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EWMAVariance.__init__": [[445, 2, 2, 0, "lam", "Smoothing parameter."]], "arch.univariate.EWMAVariance.backcast": [[587, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.backcast_transform": [[588, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EWMAVariance.bounds": [[589, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.compute_variance": [[590, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [590, 2, 2, 0, "parameters", "Model parameters"], [590, 2, 2, 0, "resids", "Vector of mean zero residuals"], [590, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [590, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.forecast": [[592, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [592, 2, 2, 0, "horizon", "Forecast horizon."], [592, 2, 2, 0, "method", "Method to use when producing the forecast."], [592, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [592, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [592, 2, 2, 0, "resids", "Residuals to use in the recursion"], [592, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [592, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [592, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [592, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.simulate": [[596, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [596, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [596, 2, 2, 0, "nobs", "Number of data points to simulate"], [596, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [596, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EWMAVariance.starting_values": [[598, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EWMAVariance.update": [[600, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [600, 2, 2, 0, "index", "The numerical index of the variance to compute"], [600, 2, 2, 0, "parameters", "The variance model parameters"], [600, 2, 2, 0, "resids", "The residual array."], [600, 2, 2, 0, "sigma2", "The array containing the variances."], [600, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.variance_bounds": [[602, 2, 2, 0, "power", "Power used in the model."], [602, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FIGARCH": [[604, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [605, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [606, 3, 1, 0, "bounds", "Returns bounds for parameters"], [607, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [608, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [609, 3, 1, 0, "forecast", "Forecast volatility from the model"], [610, 5, 1, 0, "name", "The name of the volatility process"], [611, 5, 1, 0, "num_params", "The number of parameters in the model"], [612, 3, 1, 0, "parameter_names", "Names of model parameters"], [613, 3, 1, 0, "simulate", "Simulate data from the model"], [614, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [615, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [616, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [617, 5, 1, 0, "truncation", "Truncation lag for the ARCH-infinity approximation"], [618, 3, 1, 0, "update", "Compute the variance for a single observation"], [619, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [620, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [621, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FIGARCH.__init__": [[446, 2, 2, 0, "p", "Order of the symmetric innovation"], [446, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power."], [446, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"], [446, 2, 2, 0, "truncation", "Truncation point to use in ARCH(\\infty) representation. Default is 1000."]], "arch.univariate.FIGARCH.backcast": [[604, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.backcast_transform": [[605, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FIGARCH.bounds": [[606, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.compute_variance": [[607, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [607, 2, 2, 0, "parameters", "Model parameters"], [607, 2, 2, 0, "resids", "Vector of mean zero residuals"], [607, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [607, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.forecast": [[609, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [609, 2, 2, 0, "horizon", "Forecast horizon."], [609, 2, 2, 0, "method", "Method to use when producing the forecast."], [609, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [609, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [609, 2, 2, 0, "resids", "Residuals to use in the recursion"], [609, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [609, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [609, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [609, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.simulate": [[613, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [613, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [613, 2, 2, 0, "nobs", "Number of data points to simulate"], [613, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [613, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FIGARCH.starting_values": [[615, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FIGARCH.update": [[618, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [618, 2, 2, 0, "index", "The numerical index of the variance to compute"], [618, 2, 2, 0, "parameters", "The variance model parameters"], [618, 2, 2, 0, "resids", "The residual array."], [618, 2, 2, 0, "sigma2", "The array containing the variances."], [618, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.variance_bounds": [[620, 2, 2, 0, "power", "Power used in the model."], [620, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FixedVariance": [[622, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [623, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [624, 3, 1, 0, "bounds", "Returns bounds for parameters"], [625, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [626, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [627, 3, 1, 0, "forecast", "Forecast volatility from the model"], [628, 5, 1, 0, "name", "The name of the volatility process"], [629, 5, 1, 0, "num_params", "The number of parameters in the model"], [630, 3, 1, 0, "parameter_names", "Names of model parameters"], [631, 3, 1, 0, "simulate", "Simulate data from the model"], [632, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [633, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [634, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [635, 3, 1, 0, "update", "Compute the variance for a single observation"], [636, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [637, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [638, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FixedVariance.__init__": [[447, 2, 2, 0, "unit_scale", "Flag whether to enforce a unit scale."], [447, 2, 2, 0, "variance", "Array containing the variances to use."]], "arch.univariate.FixedVariance.backcast": [[622, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.backcast_transform": [[623, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FixedVariance.bounds": [[624, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.compute_variance": [[625, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [625, 2, 2, 0, "parameters", "Model parameters"], [625, 2, 2, 0, "resids", "Vector of mean zero residuals"], [625, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [625, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.forecast": [[627, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [627, 2, 2, 0, "horizon", "Forecast horizon."], [627, 2, 2, 0, "method", "Method to use when producing the forecast."], [627, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [627, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [627, 2, 2, 0, "resids", "Residuals to use in the recursion"], [627, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [627, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [627, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [627, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.simulate": [[631, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [631, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [631, 2, 2, 0, "nobs", "Number of data points to simulate"], [631, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [631, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FixedVariance.starting_values": [[633, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FixedVariance.update": [[635, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [635, 2, 2, 0, "index", "The numerical index of the variance to compute"], [635, 2, 2, 0, "parameters", "The variance model parameters"], [635, 2, 2, 0, "resids", "The residual array."], [635, 2, 2, 0, "sigma2", "The array containing the variances."], [635, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.variance_bounds": [[637, 2, 2, 0, "power", "Power used in the model."], [637, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GARCH": [[639, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [640, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [641, 3, 1, 0, "bounds", "Returns bounds for parameters"], [642, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [643, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [644, 3, 1, 0, "forecast", "Forecast volatility from the model"], [645, 5, 1, 0, "name", "The name of the volatility process"], [646, 5, 1, 0, "num_params", "The number of parameters in the model"], [647, 3, 1, 0, "parameter_names", "Names of model parameters"], [648, 3, 1, 0, "simulate", "Simulate data from the model"], [649, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [650, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [651, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [652, 3, 1, 0, "update", "Compute the variance for a single observation"], [653, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [654, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [655, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.GARCH.__init__": [[448, 2, 2, 0, "o", "Order of the asymmetric innovation"], [448, 2, 2, 0, "p", "Order of the symmetric innovation"], [448, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power."], [448, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.GARCH.backcast": [[639, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.backcast_transform": [[640, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.GARCH.bounds": [[641, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.compute_variance": [[642, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [642, 2, 2, 0, "parameters", "Model parameters"], [642, 2, 2, 0, "resids", "Vector of mean zero residuals"], [642, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [642, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.forecast": [[644, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [644, 2, 2, 0, "horizon", "Forecast horizon."], [644, 2, 2, 0, "method", "Method to use when producing the forecast."], [644, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [644, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [644, 2, 2, 0, "resids", "Residuals to use in the recursion"], [644, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [644, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [644, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [644, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.simulate": [[648, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [648, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [648, 2, 2, 0, "nobs", "Number of data points to simulate"], [648, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [648, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.GARCH.starting_values": [[650, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.GARCH.update": [[652, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [652, 2, 2, 0, "index", "The numerical index of the variance to compute"], [652, 2, 2, 0, "parameters", "The variance model parameters"], [652, 2, 2, 0, "resids", "The residual array."], [652, 2, 2, 0, "sigma2", "The array containing the variances."], [652, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.variance_bounds": [[654, 2, 2, 0, "power", "Power used in the model."], [654, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GeneralizedError": [[656, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [657, 3, 1, 0, "cdf", "Cumulative distribution function"], [658, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [659, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [660, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a Generalized Error Distribution, conditional on the variance."], [661, 3, 1, 0, "moment", "Moment of order n"], [662, 5, 1, 0, "name", "The name of the distribution"], [663, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [664, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [665, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [666, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [667, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [668, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.GeneralizedError.__init__": [[449, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated."], [449, 2, 2, 0, "seed", "Random number generator instance or int to use."]], "arch.univariate.GeneralizedError.bounds": [[656, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.GeneralizedError.cdf": [[657, 2, 2, 0, "parameters", "Distribution parameters."], [657, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.GeneralizedError.loglikelihood": [[660, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [660, 2, 2, 0, "parameters", "Shape parameter of the GED distribution"], [660, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [660, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.GeneralizedError.moment": [[661, 2, 2, 0, "n", "Order of moment"], [661, 2, 2, 0, "parameters", "Distribution parameters."]], "arch.univariate.GeneralizedError.partial_moment": [[664, 2, 2, 0, "n", "Order of partial moment"], [664, 2, 2, 0, "parameters", "Distribution parameters."], [664, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.GeneralizedError.ppf": [[665, 2, 2, 0, "parameters", "Distribution parameters."], [665, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.GeneralizedError.simulate": [[667, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.GeneralizedError.starting_values": [[668, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.HARCH": [[669, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [670, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [671, 3, 1, 0, "bounds", "Returns bounds for parameters"], [672, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [673, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [674, 3, 1, 0, "forecast", "Forecast volatility from the model"], [675, 5, 1, 0, "name", "The name of the volatility process"], [676, 5, 1, 0, "num_params", "The number of parameters in the model"], [677, 3, 1, 0, "parameter_names", "Names of model parameters"], [678, 3, 1, 0, "simulate", "Simulate data from the model"], [679, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [680, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [681, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [682, 3, 1, 0, "update", "Compute the variance for a single observation"], [683, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [684, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [685, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.HARCH.__init__": [[450, 2, 2, 0, "lags", "List of lags to include in the model, or if scalar, includes all lags up the value"]], "arch.univariate.HARCH.backcast": [[669, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.backcast_transform": [[670, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.HARCH.bounds": [[671, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.compute_variance": [[672, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [672, 2, 2, 0, "parameters", "Model parameters"], [672, 2, 2, 0, "resids", "Vector of mean zero residuals"], [672, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [672, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.forecast": [[674, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [674, 2, 2, 0, "horizon", "Forecast horizon."], [674, 2, 2, 0, "method", "Method to use when producing the forecast."], [674, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [674, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [674, 2, 2, 0, "resids", "Residuals to use in the recursion"], [674, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [674, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [674, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [674, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.simulate": [[678, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [678, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [678, 2, 2, 0, "nobs", "Number of data points to simulate"], [678, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [678, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.HARCH.starting_values": [[680, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.HARCH.update": [[682, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [682, 2, 2, 0, "index", "The numerical index of the variance to compute"], [682, 2, 2, 0, "parameters", "The variance model parameters"], [682, 2, 2, 0, "resids", "The residual array."], [682, 2, 2, 0, "sigma2", "The array containing the variances."], [682, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.variance_bounds": [[684, 2, 2, 0, "power", "Power used in the model."], [684, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.HARX": [[686, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [687, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [688, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [689, 5, 1, 0, "distribution", "Set or gets the error distribution"], [690, 3, 1, 0, "fit", "Estimate model parameters"], [691, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [692, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [693, 5, 1, 0, "name", "The name of the model."], [694, 5, 1, 0, "num_params", "Returns the number of parameters"], [695, 3, 1, 0, "parameter_names", "List of parameters names"], [696, 3, 1, 0, "resids", "Compute model residuals"], [697, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [698, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [699, 5, 1, 0, "volatility", "Set or gets the volatility process"], [700, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [701, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.HARX.__init__": [[451, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [451, 2, 2, 0, "distribution", "Error distribution to use in the model"], [451, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters."], [451, 2, 2, 0, "lags", "Description of lag structure of the HAR."], [451, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation."], [451, 2, 2, 0, "use_rotated", "Flag indicating to use the alternative rotated form of the HAR where HAR lags do not overlap"], [451, 2, 2, 0, "volatility", "Volatility process to use in the model"], [451, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [451, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.HARX.compute_param_cov": [[687, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [687, 2, 2, 0, "params", "Model parameters"], [687, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.HARX.fit": [[690, 2, 2, 0, "backcast", "Value to use as backcast."], [690, 2, 2, 0, "cov_type", "Estimation method of parameter covariance."], [690, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing."], [690, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [690, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [690, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize."], [690, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [690, 2, 2, 0, "starting_values", "Array of starting values to use."], [690, 2, 2, 0, "tol", "Tolerance for termination."], [690, 2, 2, 0, "update_freq", "Frequency of iteration updates."]], "arch.univariate.HARX.fix": [[691, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [691, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [691, 2, 2, 0, "params", "User specified parameters to use when generating the result."]], "arch.univariate.HARX.forecast": [[692, 2, 2, 0, "align", "Either 'origin' or 'target'."], [692, 2, 2, 0, "horizon", "Number of steps to forecast"], [692, 2, 2, 0, "method", "Method to use when producing the forecast."], [692, 2, 2, 0, "params", "Parameters required to forecast."], [692, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [692, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [692, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [692, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [692, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [692, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.HARX.resids": [[696, 2, 2, 0, "params", "Model parameters"], [696, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [696, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.HARX.simulate": [[697, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [697, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model."], [697, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [697, 2, 2, 0, "nobs", "Length of series to simulate"], [697, 2, 2, 0, "params", "Parameters to use when simulating the model."], [697, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.LS": [[702, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [703, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [704, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [705, 5, 1, 0, "distribution", "Set or gets the error distribution"], [706, 3, 1, 0, "fit", "Estimate model parameters"], [707, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [708, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [709, 5, 1, 0, "name", "The name of the model."], [710, 5, 1, 0, "num_params", "Returns the number of parameters"], [711, 3, 1, 0, "parameter_names", "List of parameters names"], [712, 3, 1, 0, "resids", "Compute model residuals"], [713, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [714, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [715, 5, 1, 0, "volatility", "Set or gets the volatility process"], [716, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [717, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.LS.__init__": [[452, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [452, 2, 2, 0, "distribution", "Error distribution to use in the model"], [452, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters."], [452, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation."], [452, 2, 2, 0, "volatility", "Volatility process to use in the model"], [452, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [452, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.LS.compute_param_cov": [[703, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [703, 2, 2, 0, "params", "Model parameters"], [703, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.LS.fit": [[706, 2, 2, 0, "backcast", "Value to use as backcast."], [706, 2, 2, 0, "cov_type", "Estimation method of parameter covariance."], [706, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing."], [706, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [706, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [706, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize."], [706, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [706, 2, 2, 0, "starting_values", "Array of starting values to use."], [706, 2, 2, 0, "tol", "Tolerance for termination."], [706, 2, 2, 0, "update_freq", "Frequency of iteration updates."]], "arch.univariate.LS.fix": [[707, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [707, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [707, 2, 2, 0, "params", "User specified parameters to use when generating the result."]], "arch.univariate.LS.forecast": [[708, 2, 2, 0, "align", "Either 'origin' or 'target'."], [708, 2, 2, 0, "horizon", "Number of steps to forecast"], [708, 2, 2, 0, "method", "Method to use when producing the forecast."], [708, 2, 2, 0, "params", "Parameters required to forecast."], [708, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [708, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [708, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [708, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [708, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [708, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.LS.resids": [[712, 2, 2, 0, "params", "Model parameters"], [712, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [712, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.LS.simulate": [[713, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [713, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model."], [713, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [713, 2, 2, 0, "nobs", "Length of series to simulate"], [713, 2, 2, 0, "params", "Parameters to use when simulating the model."], [713, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.MIDASHyperbolic": [[718, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [719, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [720, 3, 1, 0, "bounds", "Returns bounds for parameters"], [721, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [722, 3, 1, 0, "constraints", "Constraints"], [723, 3, 1, 0, "forecast", "Forecast volatility from the model"], [724, 5, 1, 0, "name", "The name of the volatility process"], [725, 5, 1, 0, "num_params", "The number of parameters in the model"], [726, 3, 1, 0, "parameter_names", "Names of model parameters"], [727, 3, 1, 0, "simulate", "Simulate data from the model"], [728, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [729, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [730, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [731, 3, 1, 0, "update", "Compute the variance for a single observation"], [732, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [733, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [734, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.MIDASHyperbolic.__init__": [[453, 2, 2, 0, "asym", "Flag indicating whether to include an asymmetric term"], [453, 2, 2, 0, "m", "Length of maximum lag to include in the model"]], "arch.univariate.MIDASHyperbolic.backcast": [[718, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.backcast_transform": [[719, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.MIDASHyperbolic.bounds": [[720, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.compute_variance": [[721, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [721, 2, 2, 0, "parameters", "Model parameters"], [721, 2, 2, 0, "resids", "Vector of mean zero residuals"], [721, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [721, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.forecast": [[723, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [723, 2, 2, 0, "horizon", "Forecast horizon."], [723, 2, 2, 0, "method", "Method to use when producing the forecast."], [723, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [723, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [723, 2, 2, 0, "resids", "Residuals to use in the recursion"], [723, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [723, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [723, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [723, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.simulate": [[727, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [727, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [727, 2, 2, 0, "nobs", "Number of data points to simulate"], [727, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [727, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.MIDASHyperbolic.starting_values": [[729, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.MIDASHyperbolic.update": [[731, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [731, 2, 2, 0, "index", "The numerical index of the variance to compute"], [731, 2, 2, 0, "parameters", "The variance model parameters"], [731, 2, 2, 0, "resids", "The residual array."], [731, 2, 2, 0, "sigma2", "The array containing the variances."], [731, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.variance_bounds": [[733, 2, 2, 0, "power", "Power used in the model."], [733, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.Normal": [[735, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [736, 3, 1, 0, "cdf", "Cumulative distribution function"], [737, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [738, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [739, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are normally distributed, conditional on the variance"], [740, 3, 1, 0, "moment", "Moment of order n"], [741, 5, 1, 0, "name", "The name of the distribution"], [742, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [743, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [744, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [745, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [746, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [747, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.Normal.__init__": [[454, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated."], [454, 2, 2, 0, "seed", "Random number generator instance or int to use."]], "arch.univariate.Normal.bounds": [[735, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.Normal.cdf": [[736, 2, 2, 0, "parameters", "Distribution parameters."], [736, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.Normal.loglikelihood": [[739, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [739, 2, 2, 0, "parameters", "The normal likelihood has no shape parameters."], [739, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [739, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.Normal.moment": [[740, 2, 2, 0, "n", "Order of moment"], [740, 2, 2, 0, "parameters", "Distribution parameters."]], "arch.univariate.Normal.partial_moment": [[743, 2, 2, 0, "n", "Order of partial moment"], [743, 2, 2, 0, "parameters", "Distribution parameters."], [743, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.Normal.ppf": [[744, 2, 2, 0, "parameters", "Distribution parameters."], [744, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.Normal.simulate": [[746, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.Normal.starting_values": [[747, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.RiskMetrics2006": [[748, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [749, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [750, 3, 1, 0, "bounds", "Returns bounds for parameters"], [751, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [752, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [753, 3, 1, 0, "forecast", "Forecast volatility from the model"], [754, 5, 1, 0, "name", "The name of the volatility process"], [755, 5, 1, 0, "num_params", "The number of parameters in the model"], [756, 3, 1, 0, "parameter_names", "Names of model parameters"], [757, 3, 1, 0, "simulate", "Simulate data from the model"], [758, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [759, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [760, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [761, 3, 1, 0, "update", "Compute the variance for a single observation"], [762, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [763, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [764, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.RiskMetrics2006.__init__": [[455, 2, 2, 0, "kmax", "Number of components."], [455, 2, 2, 0, "rho", "Relative scale of adjacent cycles."], [455, 2, 2, 0, "tau0", "Length of long cycle."], [455, 2, 2, 0, "tau1", "Length of short cycle."]], "arch.univariate.RiskMetrics2006.backcast": [[748, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.backcast_transform": [[749, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.RiskMetrics2006.bounds": [[750, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.compute_variance": [[751, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion."], [751, 2, 2, 0, "parameters", "Model parameters"], [751, 2, 2, 0, "resids", "Vector of mean zero residuals"], [751, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [751, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.forecast": [[753, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [753, 2, 2, 0, "horizon", "Forecast horizon."], [753, 2, 2, 0, "method", "Method to use when producing the forecast."], [753, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [753, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [753, 2, 2, 0, "resids", "Residuals to use in the recursion"], [753, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'."], [753, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [753, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast."], [753, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.simulate": [[757, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [757, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [757, 2, 2, 0, "nobs", "Number of data points to simulate"], [757, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [757, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.RiskMetrics2006.starting_values": [[759, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.RiskMetrics2006.update": [[761, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [761, 2, 2, 0, "index", "The numerical index of the variance to compute"], [761, 2, 2, 0, "parameters", "The variance model parameters"], [761, 2, 2, 0, "resids", "The residual array."], [761, 2, 2, 0, "sigma2", "The array containing the variances."], [761, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.variance_bounds": [[763, 2, 2, 0, "power", "Power used in the model."], [763, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.SkewStudent": [[765, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [766, 3, 1, 0, "cdf", "Cumulative distribution function"], [767, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [768, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [769, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Skew Student's t distribution, conditional on the variance."], [770, 3, 1, 0, "moment", "Moment of order n"], [771, 5, 1, 0, "name", "The name of the distribution"], [772, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [773, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [774, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [775, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [776, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [777, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.SkewStudent.__init__": [[456, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated."], [456, 2, 2, 0, "seed", "Random number generator instance or int to use."]], "arch.univariate.SkewStudent.bounds": [[765, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.SkewStudent.cdf": [[766, 2, 2, 0, "parameters", "Distribution parameters."], [766, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.SkewStudent.loglikelihood": [[769, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [769, 2, 2, 0, "parameters", "Shape parameter of the skew-t distribution"], [769, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [769, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.SkewStudent.moment": [[770, 2, 2, 0, "n", "Order of moment"], [770, 2, 2, 0, "parameters", "Distribution parameters."]], "arch.univariate.SkewStudent.partial_moment": [[773, 2, 2, 0, "n", "Order of partial moment"], [773, 2, 2, 0, "parameters", "Distribution parameters."], [773, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.SkewStudent.ppf": [[774, 2, 2, 0, "parameters", "Distribution parameters."], [774, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.SkewStudent.simulate": [[776, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.SkewStudent.starting_values": [[777, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.StudentsT": [[778, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [779, 3, 1, 0, "cdf", "Cumulative distribution function"], [780, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [781, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [782, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Student's t distribution, conditional on the variance."], [783, 3, 1, 0, "moment", "Moment of order n"], [784, 5, 1, 0, "name", "The name of the distribution"], [785, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [786, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [787, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [788, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [789, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [790, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.StudentsT.__init__": [[457, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated."], [457, 2, 2, 0, "seed", "Random number generator instance or int to use."]], "arch.univariate.StudentsT.bounds": [[778, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.StudentsT.cdf": [[779, 2, 2, 0, "parameters", "Distribution parameters."], [779, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.StudentsT.loglikelihood": [[782, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [782, 2, 2, 0, "parameters", "Shape parameter of the t distribution"], [782, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [782, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.StudentsT.moment": [[783, 2, 2, 0, "n", "Order of moment"], [783, 2, 2, 0, "parameters", "Distribution parameters."]], "arch.univariate.StudentsT.partial_moment": [[786, 2, 2, 0, "n", "Order of partial moment"], [786, 2, 2, 0, "parameters", "Distribution parameters."], [786, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.StudentsT.ppf": [[787, 2, 2, 0, "parameters", "Distribution parameters."], [787, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.StudentsT.simulate": [[789, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.StudentsT.starting_values": [[790, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.ZeroMean": [[791, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [792, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [793, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [794, 5, 1, 0, "distribution", "Set or gets the error distribution"], [795, 3, 1, 0, "fit", "Estimate model parameters"], [796, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [797, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [798, 5, 1, 0, "name", "The name of the model."], [799, 5, 1, 0, "num_params", "Returns the number of parameters"], [800, 3, 1, 0, "parameter_names", "List of parameters names"], [801, 3, 1, 0, "resids", "Compute model residuals"], [802, 3, 1, 0, "simulate", "Simulated data from a zero mean model"], [803, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [804, 5, 1, 0, "volatility", "Set or gets the volatility process"], [805, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [806, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ZeroMean.__init__": [[458, 2, 2, 0, "distribution", "Error distribution to use in the model"], [458, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters."], [458, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation."], [458, 2, 2, 0, "volatility", "Volatility process to use in the model"], [458, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ZeroMean.compute_param_cov": [[792, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [792, 2, 2, 0, "params", "Model parameters"], [792, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ZeroMean.fit": [[795, 2, 2, 0, "backcast", "Value to use as backcast."], [795, 2, 2, 0, "cov_type", "Estimation method of parameter covariance."], [795, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing."], [795, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [795, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [795, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize."], [795, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [795, 2, 2, 0, "starting_values", "Array of starting values to use."], [795, 2, 2, 0, "tol", "Tolerance for termination."], [795, 2, 2, 0, "update_freq", "Frequency of iteration updates."]], "arch.univariate.ZeroMean.fix": [[796, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [796, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [796, 2, 2, 0, "params", "User specified parameters to use when generating the result."]], "arch.univariate.ZeroMean.forecast": [[797, 2, 2, 0, "align", "Either 'origin' or 'target'."], [797, 2, 2, 0, "horizon", "Number of steps to forecast"], [797, 2, 2, 0, "method", "Method to use when producing the forecast."], [797, 2, 2, 0, "params", "Parameters required to forecast."], [797, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [797, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [797, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [797, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [797, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [797, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.ZeroMean.resids": [[801, 2, 2, 0, "params", "Model parameters"], [801, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [801, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ZeroMean.simulate": [[802, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [802, 2, 2, 0, "initial_value", "This value is not used."], [802, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [802, 2, 2, 0, "nobs", "Length of series to simulate"], [802, 2, 2, 0, "params", "Parameters to use when simulating the model."], [802, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.arch_model": [[907, 2, 2, 0, "dist", "Name of the error distribution."], [907, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters."], [907, 2, 2, 0, "lags", "Either a scalar integer value indicating lag length or a list of integers specifying lag locations."], [907, 2, 2, 0, "mean", "Name of the mean model."], [907, 2, 2, 0, "o", "Lag order of the asymmetric innovation"], [907, 2, 2, 0, "p", "Lag order of the symmetric innovation"], [907, 2, 2, 0, "power", "Power to use with GARCH and related models"], [907, 2, 2, 0, "q", "Lag order of lagged volatility or equivalent"], [907, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters."], [907, 2, 2, 0, "vol", "Name of the volatility model."], [907, 2, 2, 0, "x", "Exogenous regressors."], [907, 2, 2, 0, "y", "The dependent variable"]], "arch.univariate.base": [[459, 1, 1, 0, "ARCHModel", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [460, 1, 1, 0, "ARCHModelFixedResult", "Results for fixed parameters for an ARCHModel model"], [461, 1, 1, 0, "ARCHModelForecast", "Container for forecasts from an ARCH Model"], [462, 1, 1, 0, "ARCHModelForecastSimulation", "Container for a simulation or bootstrap-based forecasts from an ARCH Model"], [463, 1, 1, 0, "ARCHModelResult", "Results from estimation of an ARCHModel model"]], "arch.univariate.base.ARCHModel": [[807, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [808, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [809, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [810, 5, 1, 0, "distribution", "Set or gets the error distribution"], [811, 3, 1, 0, "fit", "Estimate model parameters"], [812, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [813, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [814, 5, 1, 0, "name", "The name of the model."], [815, 5, 1, 0, "num_params", "Number of parameters in the model"], [816, 3, 1, 0, "parameter_names", "List of parameters names"], [817, 3, 1, 0, "resids", "Compute model residuals"], [818, 3, 1, 0, "simulate", ""], [819, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [820, 5, 1, 0, "volatility", "Set or gets the volatility process"], [821, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.base.ARCHModel.__init__": [[459, 2, 2, "arch.univariate.base.ARCHModel", "distribution", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [459, 2, 2, "arch.univariate.base.ARCHModel", "hold_back", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [459, 2, 2, "arch.univariate.base.ARCHModel", "rescale", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [459, 2, 2, "arch.univariate.base.ARCHModel", "volatility", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [459, 2, 2, "arch.univariate.base.ARCHModel", "y", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."]], "arch.univariate.base.ARCHModel.compute_param_cov": [[808, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [808, 2, 2, 0, "params", "Model parameters"], [808, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.base.ARCHModel.fit": [[811, 2, 2, 0, "backcast", "Value to use as backcast."], [811, 2, 2, 0, "cov_type", "Estimation method of parameter covariance."], [811, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing."], [811, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [811, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [811, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize."], [811, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [811, 2, 2, 0, "starting_values", "Array of starting values to use."], [811, 2, 2, 0, "tol", "Tolerance for termination."], [811, 2, 2, 0, "update_freq", "Frequency of iteration updates."]], "arch.univariate.base.ARCHModel.fix": [[812, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [812, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [812, 2, 2, 0, "params", "User specified parameters to use when generating the result."]], "arch.univariate.base.ARCHModel.forecast": [[813, 2, 2, 0, "align", "Either 'origin' or 'target'."], [813, 2, 2, 0, "horizon", "Number of steps to forecast"], [813, 2, 2, 0, "method", "Method to use when producing the forecast."], [813, 2, 2, 0, "params", "Parameters required to forecast."], [813, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [813, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [813, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [813, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [813, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [813, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.base.ARCHModel.resids": [[817, 2, 2, 0, "params", "Model parameters"], [817, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [817, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.base.ARCHModel.simulate": [[818, 2, 2, "arch.univariate.base.ARCHModel.simulate", "burn", ""], [818, 2, 2, "arch.univariate.base.ARCHModel.simulate", "initial_value", ""], [818, 2, 2, "arch.univariate.base.ARCHModel.simulate", "initial_value_vol", ""], [818, 2, 2, "arch.univariate.base.ARCHModel.simulate", "nobs", ""], [818, 2, 2, "arch.univariate.base.ARCHModel.simulate", "params", ""], [818, 2, 2, "arch.univariate.base.ARCHModel.simulate", "x", ""]], "arch.univariate.base.ARCHModelFixedResult": [[822, 5, 1, 0, "aic", "Akaike Information Criteria"], [823, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [824, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [825, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [826, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [827, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [828, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [829, 5, 1, 0, "model", "Model instance used to produce the fit"], [830, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [831, 5, 1, 0, "num_params", "Number of parameters in model"], [832, 5, 1, 0, "params", "Model Parameters"], [833, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [834, 5, 1, 0, "resid", "Model residuals"], [835, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [836, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."]], "arch.univariate.base.ARCHModelFixedResult.__init__": [[460, 2, 2, 0, "dep_var", "Dependent variable"], [460, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [460, 2, 2, 0, "loglikelihood", "Loglikelihood at specified parameters"], [460, 2, 2, 0, "model", "The model object used to estimate the parameters"], [460, 2, 2, 0, "names", "Model parameter names"], [460, 2, 2, 0, "params", "Estimated parameters"], [460, 2, 2, 0, "resid", "Residuals from model."], [460, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelFixedResult.arch_lm_test": [[823, 2, 2, 0, "lags", "Number of lags to include in the model."], [823, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations."]], "arch.univariate.base.ARCHModelFixedResult.forecast": [[826, 2, 2, 0, "align", "Either 'origin' or 'target'."], [826, 2, 2, 0, "horizon", "Number of steps to forecast"], [826, 2, 2, 0, "method", "Method to use when producing the forecast."], [826, 2, 2, 0, "params", "Alternative parameters to use."], [826, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [826, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [826, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [826, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [826, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [826, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.base.ARCHModelFixedResult.hedgehog_plot": [[827, 2, 2, 0, "horizon", "Number of steps to forecast"], [827, 2, 2, 0, "method", "Method to use when producing the forecast."], [827, 2, 2, 0, "params", "Alternative parameters to use."], [827, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [827, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [827, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [827, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelFixedResult.plot": [[833, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility."], [833, 2, 2, 0, "scale", "Value to use when scaling returns to annualize."]], "arch.univariate.base.ARCHModelForecast": [[837, 5, 1, 0, "mean", "Forecast values for the conditional mean of the process"], [838, 5, 1, 0, "residual_variance", "Forecast values for the conditional variance of the residuals"], [839, 5, 1, 0, "simulations", "Detailed simulation results if using a simulation-based method"], [840, 5, 1, 0, "variance", "Forecast values for the conditional variance of the process"]], "arch.univariate.base.ARCHModelForecast.__init__": [[461, 2, 2, 0, "align", ""], [461, 2, 2, 0, "index", ""], [461, 2, 2, 0, "mean", ""], [461, 2, 2, "arch.univariate.base.ARCHModelForecast", "reindex", "Container for forecasts from an ARCH Model"], [461, 2, 2, 0, "residual_variance", ""], [461, 2, 2, 0, "simulated_paths", ""], [461, 2, 2, 0, "simulated_residual_variances", ""], [461, 2, 2, 0, "simulated_residuals", ""], [461, 2, 2, 0, "simulated_variances", ""], [461, 2, 2, "arch.univariate.base.ARCHModelForecast", "start_index", "Container for forecasts from an ARCH Model"], [461, 2, 2, 0, "variance", ""]], "arch.univariate.base.ARCHModelForecastSimulation": [[841, 5, 1, 0, "index", "The index aligned to dimension 0 of the simulation paths"], [842, 5, 1, 0, "residual_variances", "Simulated variance of the residuals"], [843, 5, 1, 0, "residuals", "Simulated residuals used to produce the values"], [844, 5, 1, 0, "values", "The values of the process"], [845, 5, 1, 0, "variances", "Simulated variances of the values"]], "arch.univariate.base.ARCHModelForecastSimulation.__init__": [[462, 2, 2, 0, "index", ""], [462, 2, 2, 0, "residual_variances", ""], [462, 2, 2, 0, "residuals", ""], [462, 2, 2, 0, "values", ""], [462, 2, 2, 0, "variances", ""]], "arch.univariate.base.ARCHModelResult": [[846, 5, 1, 0, "aic", "Akaike Information Criteria"], [847, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [848, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [849, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [850, 3, 1, 0, "conf_int", "Parameter confidence intervals"], [851, 5, 1, 0, "convergence_flag", "scipy.optimize.minimize result flag"], [852, 5, 1, 0, "fit_start", "Start of sample used to estimate parameters"], [853, 5, 1, 0, "fit_stop", "End of sample used to estimate parameters"], [854, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [855, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [856, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [857, 5, 1, 0, "model", "Model instance used to produce the fit"], [858, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [859, 5, 1, 0, "num_params", "Number of parameters in model"], [860, 5, 1, 0, "optimization_result", "Information about the convergence of the loglikelihood optimization"], [861, 5, 1, 0, "param_cov", "Parameter covariance"], [862, 5, 1, 0, "params", "Model Parameters"], [863, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [864, 5, 1, 0, "pvalues", "Array of p-values for the t-statistics"], [865, 5, 1, 0, "resid", "Model residuals"], [866, 5, 1, 0, "rsquared", "R-squared"], [867, 5, 1, 0, "rsquared_adj", "Degree of freedom adjusted R-squared"], [868, 5, 1, 0, "scale", "The scale applied to the original data before estimating the model."], [869, 5, 1, 0, "std_err", "Array of parameter standard errors"], [870, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [871, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."], [872, 5, 1, 0, "tvalues", "Array of t-statistics testing the null that the coefficient are 0"]], "arch.univariate.base.ARCHModelResult.__init__": [[463, 2, 2, 0, "cov_type", "String describing the covariance estimator used"], [463, 2, 2, 0, "dep_var", "Dependent variable"], [463, 2, 2, 0, "fit_start", "Integer index of the first observation used to fit the model"], [463, 2, 2, 0, "fit_stop", "Integer index of the last observation used to fit the model using slice notation fit_start:fit_stop"], [463, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [463, 2, 2, 0, "loglikelihood", "Loglikelihood at estimated parameters"], [463, 2, 2, 0, "model", "The model object used to estimate the parameters"], [463, 2, 2, 0, "names", "Model parameter names"], [463, 2, 2, 0, "optim_output", "Result of log-likelihood optimization"], [463, 2, 2, 0, "param_cov", "Estimated variance-covariance matrix of params."], [463, 2, 2, 0, "params", "Estimated parameters"], [463, 2, 2, 0, "r2", "Model R-squared"], [463, 2, 2, 0, "resid", "Residuals from model."], [463, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelResult.arch_lm_test": [[847, 2, 2, 0, "lags", "Number of lags to include in the model."], [847, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations."]], "arch.univariate.base.ARCHModelResult.conf_int": [[850, 2, 2, 0, "alpha", "Size (prob.) to use when constructing the confidence interval."]], "arch.univariate.base.ARCHModelResult.forecast": [[854, 2, 2, 0, "align", "Either 'origin' or 'target'."], [854, 2, 2, 0, "horizon", "Number of steps to forecast"], [854, 2, 2, 0, "method", "Method to use when producing the forecast."], [854, 2, 2, 0, "params", "Alternative parameters to use."], [854, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [854, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [854, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [854, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [854, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [854, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model."]], "arch.univariate.base.ARCHModelResult.hedgehog_plot": [[855, 2, 2, 0, "horizon", "Number of steps to forecast"], [855, 2, 2, 0, "method", "Method to use when producing the forecast."], [855, 2, 2, 0, "params", "Alternative parameters to use."], [855, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [855, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [855, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for."], [855, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelResult.plot": [[863, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility."], [863, 2, 2, 0, "scale", "Value to use when scaling returns to annualize."]], "arch.univariate.distribution": [[464, 1, 1, 0, "Distribution", "Template for subclassing only"]], "arch.univariate.distribution.Distribution": [[873, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [874, 3, 1, 0, "cdf", "Cumulative distribution function"], [875, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [876, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [877, 3, 1, 0, "loglikelihood", "Loglikelihood evaluation."], [878, 3, 1, 0, "moment", "Moment of order n"], [879, 5, 1, 0, "name", "The name of the distribution"], [880, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [881, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [882, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [883, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [884, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [885, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.distribution.Distribution.__init__": [[464, 2, 2, "arch.univariate.distribution.Distribution", "random_state", "Template for subclassing only"], [464, 2, 2, "arch.univariate.distribution.Distribution", "seed", "Template for subclassing only"]], "arch.univariate.distribution.Distribution.bounds": [[873, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.distribution.Distribution.cdf": [[874, 2, 2, 0, "parameters", "Distribution parameters."], [874, 2, 2, 0, "resids", 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"VolatilityProcess", "Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."]], "arch.univariate.volatility.VarianceForecast": [[887, 5, 1, 0, "forecast_paths", "The variance forecast paths"], [888, 5, 1, 0, "forecasts", "The variance forecasts"], [889, 5, 1, 0, "shocks", "The shocks used to construct the variance forecast paths"]], "arch.univariate.volatility.VarianceForecast.__init__": [[466, 2, 2, 0, "forecast_paths", "Array containing the forecast paths if using simulation or bootstrap"], [466, 2, 2, 0, "forecasts", "Array containing the forecasts"], [466, 2, 2, 0, "shocks", "Array containing the shocks used to generate the forecast paths if using simulation or bootstrap"]], "arch.univariate.volatility.VolatilityProcess": [[890, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [891, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [892, 3, 1, 0, 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[95, 249, 251], "about": [14, 29, 44, 59, 74, 277, 278, 279, 510, 527, 543, 692, 708, 797, 813, 860, 913, 914], "abov": [3, 89, 271, 275, 276, 277, 278, 290, 433, 915], "absolut": [271, 914], "abstract": [139, 143, 147, 459, 465, 467, 813, 816, 817, 818, 873, 874, 875, 877, 878, 880, 881, 882, 884, 885, 892, 893, 894, 898, 899, 901], "acceler": [14, 29, 44, 59, 74], "accept": [3, 89, 94, 96, 510, 527, 543, 692, 708, 797, 813, 826, 854, 911], "access": [5, 6, 7, 8, 9, 260, 271, 434, 914], "accessibli": [5, 6, 7, 8, 9], "accommod": [510, 527, 543, 692, 708, 797, 813, 826, 854], "accord": 453, "account": [89, 270, 278, 295, 308, 320, 331, 345, 359, 367, 375, 381, 394, 415, 911], "accur": [4, 911], "accuraci": 4, "acf": 434, "across": [3, 7, 14, 29, 44, 59, 74, 271, 434, 437], "act": 915, "activ": 265, "actual": [89, 271, 437, 911, 913], "actual_x_oo": 911, "ad": [14, 29, 44, 59, 74, 92, 93, 94, 95, 96, 97, 510, 527, 543, 692, 708, 797, 813, 826, 854, 914, 917], "add": [93, 96, 271, 914], "addit": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 248, 278, 434, 479, 496, 562, 579, 596, 613, 631, 648, 678, 727, 757, 899, 913, 914], "addition": [434, 437], "adequ": 914, "adf": [95, 276, 277, 278, 280, 286, 289, 290, 311, 411, 430, 432, 433, 434], "adj": [4, 87, 88, 434, 437, 440, 907, 911, 912, 913, 914, 915], "adjac": 455, "adjust": [101, 103, 106, 107, 108, 109, 110, 111, 112, 113, 285, 367, 377, 381, 396, 415, 867], "administr": 433, "admiss": 3, "admit": 3, "adopt": 97, "advanc": 287, "advantag": [437, 915], "affect": [95, 96, 97, 282, 287, 290, 433, 510, 527, 543, 692, 708, 797, 813, 826, 827, 854, 855], "after": [5, 6, 7, 8, 9, 95, 96, 437, 510, 527, 543, 692, 708, 797, 813, 911, 913], "against": [91, 98, 277, 280, 509, 526, 542, 691, 707, 796, 812], "agre": [433, 434], "ahead": [437, 510, 527, 543, 692, 708, 797, 813, 826, 854, 911, 913], "aic": [275, 276, 280, 284, 289, 434, 907, 911, 912, 914, 915], "aim": 908, "akaik": [275, 276, 280, 284, 822, 846], "al": [277, 664, 743, 773, 786, 881], "algorithm": [10, 275, 280, 917], "alia": 273, "align": [437, 461, 510, 527, 543, 692, 708, 797, 813, 825, 826, 841, 849, 854, 913], "all": [2, 3, 4, 7, 10, 14, 29, 44, 59, 74, 87, 88, 95, 96, 99, 251, 271, 279, 280, 284, 381, 385, 398, 430, 432, 433, 434, 436, 437, 438, 440, 441, 443, 450, 451, 459, 465, 475, 492, 510, 527, 543, 558, 575, 592, 609, 627, 644, 674, 692, 708, 723, 753, 797, 813, 826, 827, 854, 855, 895, 908, 909, 910, 911, 913, 914, 915, 917], "allow": [3, 4, 7, 14, 15, 25, 29, 30, 40, 44, 45, 55, 59, 60, 70, 74, 75, 85, 94, 95, 96, 270, 271, 272, 275, 279, 434, 447, 467, 509, 526, 542, 691, 707, 796, 812, 908, 911, 914, 915, 917], "almost": 915, "along": [913, 914], "alpha": [4, 122, 132, 146, 156, 166, 176, 186, 196, 206, 216, 226, 236, 246, 290, 433, 437, 453, 722, 850, 907, 911, 912, 913, 914, 915], "alpha_": [438, 439, 444, 448, 450], "alreadi": 913, "also": [3, 89, 90, 96, 270, 271, 277, 280, 434, 911, 913, 914], "alter": 434, "altern": [3, 14, 29, 44, 59, 74, 88, 94, 100, 104, 105, 251, 252, 270, 275, 276, 277, 278, 286, 288, 291, 304, 317, 328, 341, 356, 400, 416, 430, 432, 433, 434, 451, 510, 515, 527, 531, 543, 547, 692, 696, 708, 712, 797, 801, 813, 817, 826, 827, 854, 855, 907, 911, 914, 916], "although": [4, 89, 271, 433, 434, 437, 446, 448, 911, 914], "alwai": [87, 259, 271, 434, 437, 911, 915], "am": [437, 510, 527, 543, 692, 708, 797, 813, 827, 833, 855, 863, 907, 913, 914], "american": 1, "amount": [271, 437], "an": [1, 3, 4, 5, 6, 7, 8, 9, 12, 14, 27, 29, 42, 44, 57, 59, 72, 74, 88, 89, 90, 95, 96, 101, 103, 106, 107, 108, 109, 110, 111, 112, 113, 118, 128, 142, 152, 162, 172, 182, 192, 202, 212, 222, 232, 242, 250, 251, 252, 269, 270, 271, 276, 277, 279, 280, 284, 289, 290, 367, 381, 415, 432, 433, 434, 436, 437, 441, 449, 450, 453, 454, 456, 457, 460, 461, 462, 463, 472, 490, 509, 510, 516, 526, 527, 532, 542, 543, 548, 556, 573, 590, 607, 625, 642, 672, 691, 692, 697, 707, 708, 713, 721, 751, 796, 797, 802, 812, 813, 826, 827, 833, 854, 855, 863, 893, 907, 912, 913, 914, 915], "analys": 432, "analysi": [3, 248, 275, 278], "analyt": [4, 14, 29, 44, 59, 74, 88, 273, 475, 492, 510, 527, 543, 558, 575, 592, 609, 627, 644, 674, 692, 708, 723, 753, 797, 813, 826, 827, 854, 855, 895, 910, 915], "andrew": [10, 95, 110, 277, 279, 280, 281, 432], "ani": [3, 5, 6, 7, 8, 9, 11, 12, 14, 15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 36, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 51, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 66, 67, 68, 69, 70, 71, 72, 74, 75, 77, 78, 81, 82, 83, 84, 85, 102, 120, 130, 134, 135, 136, 137, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 244, 258, 259, 264, 265, 268, 271, 273, 278, 284, 290, 367, 375, 381, 394, 415, 430, 434, 437, 440, 441, 447, 451, 459, 460, 461, 462, 463, 466, 468, 469, 470, 472, 473, 475, 479, 481, 483, 485, 487, 488, 489, 490, 491, 492, 496, 498, 500, 502, 505, 506, 508, 510, 515, 516, 517, 522, 523, 525, 527, 531, 532, 533, 538, 539, 541, 543, 547, 548, 549, 553, 554, 555, 556, 557, 558, 562, 564, 566, 568, 570, 571, 572, 573, 574, 575, 579, 581, 583, 585, 587, 588, 589, 590, 591, 592, 596, 598, 600, 602, 604, 605, 606, 607, 608, 609, 613, 615, 618, 620, 622, 623, 624, 625, 626, 627, 631, 633, 635, 637, 639, 640, 641, 642, 643, 644, 648, 650, 652, 654, 656, 657, 658, 660, 665, 667, 668, 669, 670, 671, 672, 673, 674, 678, 680, 682, 684, 687, 688, 690, 692, 696, 697, 698, 703, 704, 706, 708, 712, 713, 714, 718, 719, 720, 721, 722, 723, 727, 729, 731, 733, 735, 736, 737, 739, 744, 746, 747, 748, 749, 750, 751, 752, 753, 757, 759, 761, 763, 765, 766, 767, 769, 774, 776, 777, 778, 779, 780, 782, 787, 789, 790, 792, 793, 795, 797, 801, 802, 803, 808, 809, 811, 813, 817, 819, 825, 826, 834, 835, 842, 843, 844, 845, 849, 854, 865, 870, 873, 874, 875, 877, 882, 884, 885, 887, 888, 889, 890, 891, 892, 893, 894, 895, 899, 901, 903, 905, 907, 911, 913, 914, 915], "ann_infl": 914, "annual": [3, 4, 87, 833, 863, 914, 915], "anoth": 915, "anyarrai": 273, "anypanda": 273, "anyth": 290, "aparch": [95, 907], "api": [3, 271, 275, 276, 277, 278, 434], "appear": [3, 95, 275, 276, 277, 278, 282, 287, 432, 433, 434, 440, 911, 914], "append": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 271, 911, 913], "appli": [3, 87, 95, 281, 469, 488, 508, 525, 541, 554, 571, 588, 605, 623, 640, 670, 690, 706, 719, 749, 795, 811, 868, 891], "applic": [1, 88, 89, 271, 279, 459, 907, 908, 915], "approach": [433, 911, 915], "appropri": [3, 4, 7, 14, 29, 44, 59, 74, 86, 250, 251, 252, 469, 488, 554, 571, 588, 605, 623, 640, 670, 719, 749, 891], "approxim": [14, 29, 44, 59, 74, 275, 278, 280, 284, 468, 469, 470, 481, 485, 487, 488, 489, 498, 502, 553, 554, 555, 564, 568, 570, 571, 572, 581, 585, 587, 588, 589, 598, 602, 604, 605, 606, 615, 617, 620, 622, 623, 624, 633, 637, 639, 640, 641, 650, 654, 669, 670, 671, 680, 684, 718, 719, 720, 729, 733, 748, 749, 750, 759, 763, 890, 891, 892, 901, 905], "ar": [3, 4, 7, 10, 11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 89, 90, 95, 96, 97, 101, 102, 103, 106, 107, 108, 109, 110, 111, 112, 113, 116, 126, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 248, 250, 251, 252, 254, 255, 259, 270, 271, 272, 275, 276, 277, 278, 279, 280, 282, 284, 287, 289, 290, 334, 337, 367, 381, 415, 430, 432, 433, 434, 437, 438, 440, 441, 444, 446, 448, 450, 451, 455, 459, 465, 467, 473, 475, 485, 491, 492, 502, 508, 509, 510, 516, 525, 526, 527, 532, 541, 542, 543, 557, 558, 568, 574, 575, 585, 591, 592, 602, 608, 609, 620, 626, 627, 637, 643, 644, 654, 660, 673, 674, 684, 690, 691, 692, 697, 706, 707, 708, 713, 722, 723, 733, 739, 752, 753, 763, 769, 782, 795, 796, 797, 802, 811, 812, 813, 825, 826, 827, 833, 849, 854, 855, 863, 872, 877, 894, 895, 905, 907, 908, 909, 911, 912, 913, 914, 915, 917], "arang": [12, 27, 42, 57, 72, 89, 911, 913, 915], "arbitrag": 433, "arbitrari": [3, 448], "arch": [3, 4, 87, 88, 89, 92, 93, 94, 95, 96, 97, 248, 270, 271, 273, 430, 433, 434, 436, 437, 910, 911, 912, 913, 915, 916], "arch_lm_test": 95, "arch_model": [95, 96, 437, 510, 527, 543, 692, 708, 797, 813, 827, 833, 855, 863, 907, 910, 911, 913, 914, 915], "arch_no_binari": 96, "archinmean": [96, 917], "archmodel": [460, 463, 829, 857, 907, 908], "archmodelfixedresult": [509, 526, 542, 691, 707, 796, 812, 909], "archmodelforecast": [437, 510, 527, 543, 692, 708, 797, 813, 826, 854, 913], "archmodelforecastsimul": [839, 913], "archmodelresult": [508, 525, 541, 690, 706, 795, 811, 909, 915], "area": 1, "arg": [4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 25, 26, 28, 29, 30, 40, 41, 43, 44, 45, 55, 56, 58, 59, 60, 70, 71, 73, 74, 75, 85], "argument": [3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 25, 26, 27, 28, 29, 30, 40, 41, 42, 43, 44, 45, 48, 55, 56, 57, 58, 59, 60, 70, 71, 72, 73, 74, 75, 85, 87, 88, 89, 95, 96, 250, 251, 252, 449, 454, 456, 457, 667, 746, 776, 789, 884, 914], "aris": 86, "around": [5, 8, 9, 10, 90, 270, 433], "arrai": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 15, 25, 26, 30, 40, 41, 45, 55, 56, 60, 70, 71, 75, 85, 87, 88, 89, 96, 101, 103, 106, 107, 108, 109, 110, 111, 112, 113, 250, 251, 252, 265, 269, 271, 282, 287, 290, 430, 440, 441, 447, 451, 452, 466, 472, 473, 475, 479, 481, 483, 485, 490, 491, 492, 496, 498, 500, 502, 506, 508, 510, 516, 523, 525, 527, 532, 539, 541, 543, 548, 556, 557, 558, 562, 564, 566, 568, 573, 574, 575, 579, 581, 583, 585, 590, 591, 592, 596, 598, 600, 602, 607, 608, 609, 613, 615, 618, 620, 625, 626, 627, 631, 633, 635, 637, 642, 643, 644, 648, 650, 652, 654, 658, 668, 672, 673, 674, 678, 680, 682, 684, 688, 690, 692, 697, 704, 706, 708, 713, 721, 723, 727, 729, 731, 733, 737, 751, 752, 753, 757, 759, 761, 763, 767, 777, 780, 790, 793, 795, 797, 802, 809, 811, 813, 825, 826, 849, 850, 854, 864, 869, 872, 875, 877, 893, 894, 895, 899, 901, 903, 905, 913], "array_forecast": 911, "array_multi_forecast": 911, "arraylik": 273, "arraylike1d": 273, "arraylike2d": 273, "articl": [1, 249], "arx": [907, 911, 914], "as_text": 434, "asarrai": 915, "ask": 271, "aspect": 271, "assembl": [907, 914], "assert": 96, "asset": [833, 863, 914], "associ": [1, 486, 503, 569, 586, 603, 621, 638, 655, 685, 734, 764, 906], "assum": [89, 95, 289, 433, 434, 437, 508, 525, 541, 660, 690, 706, 739, 769, 782, 795, 811, 911, 913, 914, 915], "assumpt": [433, 914, 915], "asym": 453, "asymmetr": [438, 444, 448, 453, 907, 914], "asymmetri": 438, "asymptot": [89, 275, 278, 290], "attach": [437, 659, 666, 738, 745, 768, 775, 781, 788, 876, 883, 913], "attempt": [270, 280, 475, 492, 558, 575, 592, 609, 627, 644, 674, 723, 753, 895, 913], "attribut": [5, 6, 7, 8, 9, 437, 913, 915], "augment": [93, 275, 289, 290, 404, 430, 432, 433], "auto_bandwidth": [95, 432], "autocorrel": [3, 10, 277, 278, 375, 381, 394, 434], "autocovari": [10, 290], "autolag": [275, 280], "automat": [10, 95, 96, 97, 275, 276, 277, 278, 280, 281, 289, 294, 307, 430, 432, 434, 440, 441, 442, 451, 452, 458, 907], "autoregress": [276, 284, 441, 451, 456, 914], "avail": [5, 6, 7, 8, 9, 271, 275, 277, 278, 434, 437, 447, 465, 475, 492, 510, 527, 543, 558, 575, 592, 609, 627, 644, 674, 692, 708, 723, 753, 797, 813, 826, 854, 895, 911, 913, 914, 915], "avar": 4, "avarch": 448, "averag": [3, 9, 15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 88, 271, 437, 445, 451, 455, 914, 915], "avg_model_loss": 271, "avgarch": 448, "ax": [408, 424, 433, 911, 912, 913, 914, 915], "axi": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 271, 510, 527, 543, 692, 708, 797, 813, 826, 854, 911, 914, 915], "b": [1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 58, 59, 60, 70, 71, 72, 73, 74, 75, 85, 86, 87, 88, 89, 122, 132, 146, 156, 166, 176, 186, 196, 206, 216, 226, 236, 246, 271, 277, 278, 287, 437, 456, 473, 491, 506, 523, 539, 557, 574, 591, 608, 626, 643, 658, 673, 688, 704, 722, 737, 752, 767, 769, 780, 793, 809, 875, 894, 915], "b_cb": 10, "b_sb": 10, "ba": 281, "baa": [430, 434], "back": [94, 97, 451], 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270, 275, 278, 433, 437, 440, 907, 912, 913, 914, 915], "closer": [251, 915], "cm": [442, 548], "cm_param": 548, "cmd": 96, "co": [110, 111, 112, 113], "code": [3, 94, 95, 271, 280, 433, 434, 437, 912, 913, 914], "coef": [434, 907, 911, 912, 914, 915], "coeffici": [278, 290, 411, 433, 516, 532, 697, 713, 872, 914], "cointegr": [95, 248, 275, 278], "cointegrating_vector": 433, "cointegrationaltern": 433, "cointegrationanalysisresult": [367, 415], "cointegrationdistribut": 433, "col": [271, 911], "collect": 914, "colleg": 280, "color": [913, 915], "color_palett": 915, "column": [3, 10, 14, 29, 44, 59, 74, 87, 88, 102, 254, 255, 256, 259, 265, 269, 271, 434, 437, 451, 472, 475, 483, 485, 490, 492, 500, 502, 510, 516, 527, 532, 543, 548, 556, 558, 566, 568, 573, 575, 583, 585, 590, 592, 600, 602, 607, 609, 618, 620, 625, 627, 635, 637, 642, 644, 652, 654, 672, 674, 682, 684, 692, 697, 708, 713, 721, 723, 731, 733, 751, 753, 761, 763, 797, 802, 813, 826, 854, 893, 895, 903, 905, 911, 913, 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713, 721, 733, 739, 751, 763, 769, 782, 802, 823, 825, 833, 835, 837, 838, 840, 847, 849, 863, 870, 877, 893, 905, 908, 913, 914], "conditional_volatil": [912, 913, 914, 915], "conduct": 89, "conf": [907, 911, 912, 914, 915], "conf_int": [3, 4, 89, 92], "confid": [1, 2, 3, 14, 29, 44, 59, 74, 89, 95, 249, 250, 253, 850], "configur": 907, "confirm": 434, "conflict": [11, 26, 41, 56, 71], "consecut": 10, "conserv": 10, "consid": [89, 270, 284, 289, 911], "consider": 911, "consist": [91, 98, 251, 252, 259, 261, 262, 271, 277, 278, 381, 433, 911, 912, 914, 915], "const": [3, 433, 434, 911, 912, 914], "constant": [10, 95, 119, 122, 129, 132, 143, 146, 153, 156, 163, 166, 173, 176, 183, 186, 193, 196, 203, 206, 213, 216, 223, 226, 233, 236, 243, 246, 275, 276, 277, 278, 280, 282, 284, 287, 289, 290, 430, 433, 434, 437, 440, 441, 442, 443, 451, 452, 510, 516, 527, 532, 543, 548, 692, 697, 708, 713, 769, 797, 802, 813, 827, 855, 907, 912, 915], "constantcrit": 433, "constantmean": [907, 914, 915], "constrain": [658, 737, 767, 780, 875], "constraint": [509, 526, 542, 691, 707, 796, 812], "construct": [2, 3, 4, 5, 6, 8, 9, 89, 96, 271, 437, 446, 468, 473, 485, 487, 491, 502, 504, 506, 508, 509, 510, 521, 523, 525, 526, 527, 537, 539, 541, 542, 543, 553, 557, 568, 570, 574, 585, 587, 591, 602, 604, 608, 620, 622, 626, 637, 639, 643, 654, 658, 668, 669, 673, 684, 686, 688, 690, 691, 692, 702, 704, 706, 707, 708, 718, 733, 737, 747, 748, 752, 763, 767, 777, 780, 790, 791, 793, 795, 796, 797, 807, 809, 811, 812, 813, 826, 836, 850, 854, 871, 875, 885, 889, 890, 894, 905, 907, 911, 914, 915], "constructor": [96, 914], "contain": [0, 2, 3, 4, 5, 6, 7, 8, 9, 12, 14, 15, 17, 22, 23, 25, 27, 29, 30, 32, 37, 38, 40, 42, 44, 45, 47, 48, 52, 53, 55, 57, 59, 60, 62, 67, 68, 70, 72, 74, 75, 77, 82, 83, 85, 87, 89, 90, 95, 96, 117, 127, 141, 151, 161, 171, 181, 191, 201, 211, 221, 231, 241, 248, 250, 256, 261, 269, 271, 272, 278, 284, 292, 300, 305, 313, 318, 324, 329, 336, 342, 351, 357, 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827, 854, 855, 895, 911, 913, 915], "how": [3, 87, 433, 437, 465, 915], "howev": [3, 914, 915], "hqic": 284, "html": [275, 278, 836, 871], "http": [248, 275, 278, 456], "hyperbol": [95, 453], "hypothesi": [271, 275, 276, 277, 278, 279, 280, 286, 289, 291, 296, 304, 309, 317, 321, 328, 332, 341, 346, 356, 360, 400, 403, 407, 416, 420, 423, 430, 433, 434, 916], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 20, 23, 25, 26, 27, 29, 30, 35, 38, 40, 41, 42, 44, 45, 50, 53, 55, 56, 57, 59, 60, 65, 68, 70, 71, 72, 74, 75, 80, 83, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 101, 102, 103, 106, 107, 108, 109, 110, 111, 112, 113, 116, 118, 122, 126, 128, 132, 140, 142, 146, 150, 152, 156, 160, 162, 166, 170, 172, 176, 180, 182, 186, 190, 192, 196, 200, 202, 206, 210, 212, 216, 220, 222, 226, 230, 232, 236, 240, 242, 246, 248, 250, 251, 252, 256, 259, 265, 269, 270, 271, 272, 275, 276, 277, 278, 279, 280, 282, 284, 287, 289, 290, 293, 306, 319, 330, 344, 358, 367, 375, 381, 390, 394, 411, 415, 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855, 913, 914, 915], "identifi": [270, 271], "idx": [913, 915], "ie": 911, "ier_94": 456, "ignor": [14, 29, 44, 59, 74, 434, 833, 863, 907, 911], "iid": [4, 915], "iidbootstrap": [3, 4, 7, 11, 12, 14, 15, 25, 41, 42, 43, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 86, 87, 89, 96], "illustr": [912, 915], "iloc": [3, 271, 434, 911, 913, 915], "immateri": 96, "immut": 96, "implement": [3, 4, 10, 89, 250, 252, 275, 289, 434, 465, 912, 913, 915], "impli": 668, "import": [3, 4, 5, 6, 7, 8, 9, 11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 87, 88, 89, 95, 97, 271, 275, 276, 277, 278, 279, 430, 433, 434, 437, 438, 439, 440, 441, 442, 444, 445, 446, 448, 450, 451, 452, 453, 455, 458, 510, 516, 527, 532, 543, 548, 692, 697, 708, 713, 797, 802, 813, 827, 833, 855, 863, 907, 908, 911, 912, 913, 914, 915], "impos": 912, "improv": [4, 91, 95, 96, 98, 270, 276, 434, 465, 914], "in_samp_forcast_exog": 911, "inappropri": 7, "incl": 385, 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39, 44, 48, 50, 54, 59, 65, 69, 74, 80, 84, 101, 103, 106, 107, 108, 109, 110, 111, 112, 113, 116, 118, 126, 128, 140, 142, 150, 152, 160, 162, 170, 172, 180, 182, 190, 192, 200, 202, 210, 212, 220, 222, 230, 232, 240, 242, 251, 252, 254, 255, 259, 265, 269, 271, 275, 279, 284, 343, 347, 349, 398, 433, 434, 437, 440, 441, 442, 451, 452, 453, 458, 484, 501, 505, 508, 510, 516, 522, 525, 527, 532, 538, 541, 543, 567, 584, 601, 619, 636, 653, 660, 683, 687, 690, 692, 697, 703, 706, 708, 713, 732, 739, 762, 769, 782, 792, 795, 797, 808, 811, 813, 823, 826, 827, 833, 847, 854, 855, 863, 877, 904, 907, 911, 914], "indistinguish": 271, "individu": [660, 739, 769, 782, 877, 907], "inequ": [473, 491, 557, 574, 591, 608, 626, 643, 673, 752, 894], "inf": [14, 29, 44, 59, 74, 664, 743, 773, 786, 881], "infeas": 95, "infer": [95, 279, 287, 914], "infin": 617, "inflat": [275, 276, 277, 278, 914], "inform": [278, 284, 433, 465, 508, 525, 541, 690, 706, 795, 811, 822, 824, 825, 846, 848, 849, 860, 911, 913, 914], "infti": [282, 287, 446, 664, 743, 769, 773, 786, 881, 915], "inherit": [99, 436, 465, 908, 917], "initi": [3, 5, 6, 7, 8, 9, 11, 20, 26, 35, 41, 50, 56, 65, 71, 80, 89, 94, 95, 96, 257, 259, 263, 267, 276, 277, 290, 434, 472, 475, 479, 483, 490, 492, 496, 500, 516, 532, 548, 556, 558, 562, 566, 573, 575, 579, 583, 590, 592, 596, 600, 607, 609, 613, 618, 625, 627, 631, 635, 642, 644, 648, 652, 672, 674, 678, 682, 697, 713, 721, 723, 727, 731, 751, 753, 757, 761, 802, 893, 895, 899, 903, 907, 911, 913, 914], "initial_valu": [479, 496, 516, 532, 548, 562, 579, 596, 613, 631, 648, 678, 697, 713, 727, 757, 802, 818, 899], "initial_value_vol": [516, 532, 548, 697, 713, 802, 818], "initialize_upd": 465, "inlin": [95, 915], "inner": [4, 14, 29, 44, 59, 74, 101, 103, 106, 107, 108, 109, 110, 111, 112, 113, 290], "innov": [438, 439, 444, 446, 448, 907], "input": [3, 4, 5, 6, 7, 8, 9, 15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 87, 89, 95, 97, 271, 430, 437, 460, 463, 475, 479, 492, 496, 510, 527, 543, 558, 562, 575, 579, 592, 596, 609, 613, 627, 631, 644, 648, 674, 678, 692, 708, 723, 727, 753, 757, 797, 813, 825, 826, 827, 849, 854, 855, 877, 895, 899, 907, 913, 914], "insid": [10, 911], "inspect": 451, "instal": 97, "instanc": [5, 7, 8, 9, 13, 16, 19, 28, 31, 34, 43, 46, 49, 58, 61, 64, 73, 76, 79, 87, 89, 95, 96, 117, 127, 141, 151, 161, 171, 181, 191, 201, 211, 221, 231, 241, 285, 286, 367, 379, 398, 408, 415, 424, 449, 454, 456, 457, 475, 492, 510, 527, 543, 558, 575, 592, 609, 627, 644, 674, 692, 708, 723, 753, 797, 813, 826, 829, 836, 854, 857, 871, 895, 914], "instead": [4, 5, 6, 8, 9, 94, 434, 449, 454, 456, 457, 666, 745, 775, 788, 883, 911, 915], "int": [3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 21, 22, 23, 25, 26, 27, 28, 29, 30, 32, 36, 37, 38, 40, 41, 42, 43, 44, 45, 47, 51, 52, 53, 55, 56, 57, 58, 59, 60, 62, 66, 67, 68, 70, 71, 72, 73, 74, 75, 77, 81, 82, 83, 85, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 250, 251, 252, 258, 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914, 915, 916], "int32": [273, 440, 441, 451, 907], "int32arrai": 273, "int64": 273, "int64arrai": 273, "int_": [664, 743, 773, 786, 881], "intarrai": 273, "integ": [6, 94, 96, 101, 103, 106, 107, 108, 109, 110, 111, 112, 113, 118, 128, 142, 152, 162, 172, 182, 192, 202, 212, 222, 232, 242, 250, 251, 252, 258, 264, 268, 271, 290, 367, 381, 415, 434, 437, 463, 479, 496, 510, 527, 543, 562, 579, 596, 613, 631, 648, 678, 692, 708, 727, 757, 797, 813, 826, 827, 854, 855, 899, 907], "integr": [89, 95, 284, 664, 665, 743, 744, 773, 774, 786, 787, 881, 882], "intent": [4, 434], "intercept": 915, "interest": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 914], "interfac": 2, "intermedi": [433, 914], "intern": [2, 87, 265, 456, 915], "internet": 3, "interpret": 440, "interv": [1, 2, 3, 14, 29, 44, 59, 74, 89, 95, 665, 744, 774, 787, 850, 882], "introduc": 95, "introduct": [1, 432, 910, 914], "invalidlengthwarn": 434, "invers": [665, 744, 774, 787, 882], "investig": [277, 280, 433], "invoc": 914, "ipython": [3, 94, 271, 433, 434, 912, 913, 914], "irrespect": 94, "is_panda": [460, 463], "isin": 271, "issu": [95, 96, 440, 441, 442, 451, 452, 458, 907, 912], "iter": [11, 12, 24, 26, 27, 39, 41, 42, 54, 56, 57, 69, 71, 72, 84, 92, 465, 508, 525, 541, 690, 706, 795, 811, 907, 912, 913, 914], "ith": 850, "its": [10, 20, 35, 50, 65, 80, 263, 270, 271, 381, 433, 434, 914], "j": [1, 249, 250, 252, 271, 275, 276, 278, 282, 284, 444, 446, 448, 450, 451, 453, 510, 516, 527, 532, 543, 692, 697, 708, 713, 797, 813, 826, 854, 911], "jackknif": [4, 95], "jarqu": 434, "jb": 434, "jersei": 275, "jim": 95, "jit": 92, "john": [1, 279], "join": 911, "jointli": [95, 438, 445, 467], "journal": [1, 249, 251, 275, 277, 278, 280, 282, 284, 290], "jth": [516, 532, 697, 713], "judg": [275, 278], "just": [271, 911], "k": [10, 250, 251, 252, 271, 277, 278, 280, 284, 367, 381, 415, 438, 440, 441, 444, 448, 451, 452, 516, 532, 697, 713, 915], "k_n": 10, "kappa": [282, 290, 440, 914], "kappa_i": 284, "kde": 914, "kei": [12, 27, 42, 57, 72, 402, 419, 430, 510, 527, 543, 692, 708, 797, 813, 826, 854, 911], "ken": 434, "kernel": [95, 281, 290, 367, 381, 390, 415, 433], "kernel_est": [283, 285, 288], "kernel_nam": 103, "kevin": [248, 453], "keyword": [3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 25, 26, 27, 28, 29, 30, 40, 41, 42, 43, 44, 45, 48, 55, 56, 57, 58, 59, 60, 70, 71, 72, 73, 74, 75, 85, 87, 88, 89, 95, 96], "khrapov": 94, "kind": [434, 914], "kmax": 455, "known": [251, 271, 290, 367, 381, 415, 475, 492, 558, 575, 592, 609, 627, 644, 674, 723, 753, 895, 914], "kpss": [93, 95, 432], "kurtosi": [434, 777, 790], "kw": 87, "kw_data": [5, 6, 7, 8, 9], "kwarg": [4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 25, 26, 28, 29, 30, 40, 41, 43, 44, 45, 55, 56, 58, 59, 60, 70, 71, 73, 74, 75, 85], "kwdata": [12, 27, 42, 57, 72], "kwiatkowski": 277, "l": [4, 446, 907, 911, 914], "l1": 434, "l2": 434, "l3": 434, "l4": 434, "l5": 434, "l_": [270, 271, 440, 441, 450, 451], "l_i": [270, 271], "label": [102, 273, 913, 915], "lack": 909, "lag": [95, 96, 97, 123, 133, 147, 157, 167, 177, 187, 197, 207, 217, 227, 237, 247, 275, 276, 277, 278, 279, 280, 284, 285, 286, 289, 294, 295, 307, 308, 320, 331, 334, 345, 354, 359, 381, 385, 386, 405, 430, 433, 438, 440, 441, 442, 444, 446, 448, 450, 451, 452, 453, 458, 510, 516, 527, 532, 543, 617, 692, 697, 708, 713, 797, 813, 823, 827, 847, 855, 907, 911, 912, 914], "lam": 445, "lambda": [282, 287, 445, 448, 456, 769, 914], "lambda_": 446, "lambda_0": [102, 282, 287], "lambda_1": [102, 446], "lambda_i": 446, "lambda_j": [284, 446], "larg": [1, 95, 97, 271, 282, 287, 430, 433, 434, 914], "larger": [271, 437, 446, 448, 475, 492, 558, 575, 592, 609, 627, 644, 674, 723, 753, 895, 911, 913, 915], "last": [20, 35, 50, 65, 80, 279, 282, 287, 437, 463, 508, 509, 525, 526, 541, 542, 690, 691, 706, 707, 795, 796, 811, 812, 911], "last_ob": [94, 95, 437, 508, 509, 525, 526, 541, 542, 690, 691, 706, 707, 795, 796, 811, 812, 911, 913], "later": [91, 97, 98, 914], "latest": [94, 248], "latex": [379, 398, 836, 871], "lceil": 10, "ldot": [101, 103, 106, 107, 108, 109, 110, 111, 112, 113, 437, 911], "lead": [1, 271, 284, 285, 381, 385, 386], "least": [86, 279, 282, 287, 381, 434, 452], "leav": [4, 95], "left": [4, 10, 101, 106, 110, 122, 132, 146, 156, 166, 176, 186, 196, 206, 216, 226, 236, 246, 282, 284, 287, 289, 290, 437, 438, 444, 448, 450, 453, 660, 739, 769, 782, 911, 914], "leg": 913, "legaci": 96, "legend": [911, 913, 915], "len": [475, 492, 558, 575, 592, 609, 627, 644, 674, 723, 753, 895, 915], "length": [5, 7, 8, 9, 10, 90, 95, 96, 97, 250, 251, 252, 275, 276, 278, 280, 284, 289, 294, 307, 405, 433, 434, 437, 440, 441, 442, 450, 451, 452, 453, 455, 458, 510, 516, 527, 532, 543, 548, 692, 697, 708, 713, 797, 802, 813, 826, 854, 907, 911, 913], "leq": [106, 271], "leq1": [101, 106, 107, 108, 109, 111, 112, 113], "less": [95, 276, 914], "let": 290, "letter": 276, "level": [2, 92, 95, 434, 910], "lie": 438, "like": [4, 10, 88, 89, 270, 433, 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"arch.covariance.kernel.Gallant.centered", "arch.covariance.kernel.Gallant.cov", "arch.covariance.kernel.Gallant.force_int", "arch.covariance.kernel.Gallant.kernel_const", "arch.covariance.kernel.Gallant.kernel_weights", "arch.covariance.kernel.Gallant.name", "arch.covariance.kernel.Gallant.opt_bandwidth", "arch.covariance.kernel.Gallant.rate", "arch.covariance.kernel.NeweyWest.bandwidth", "arch.covariance.kernel.NeweyWest.bandwidth_scale", "arch.covariance.kernel.NeweyWest.centered", "arch.covariance.kernel.NeweyWest.cov", "arch.covariance.kernel.NeweyWest.force_int", "arch.covariance.kernel.NeweyWest.kernel_const", "arch.covariance.kernel.NeweyWest.kernel_weights", "arch.covariance.kernel.NeweyWest.name", "arch.covariance.kernel.NeweyWest.opt_bandwidth", "arch.covariance.kernel.NeweyWest.rate", "arch.covariance.kernel.Parzen.bandwidth", "arch.covariance.kernel.Parzen.bandwidth_scale", "arch.covariance.kernel.Parzen.centered", "arch.covariance.kernel.Parzen.cov", "arch.covariance.kernel.Parzen.force_int", "arch.covariance.kernel.Parzen.kernel_const", "arch.covariance.kernel.Parzen.kernel_weights", "arch.covariance.kernel.Parzen.name", "arch.covariance.kernel.Parzen.opt_bandwidth", "arch.covariance.kernel.Parzen.rate", "arch.covariance.kernel.ParzenCauchy.bandwidth", "arch.covariance.kernel.ParzenCauchy.bandwidth_scale", "arch.covariance.kernel.ParzenCauchy.centered", "arch.covariance.kernel.ParzenCauchy.cov", "arch.covariance.kernel.ParzenCauchy.force_int", "arch.covariance.kernel.ParzenCauchy.kernel_const", "arch.covariance.kernel.ParzenCauchy.kernel_weights", "arch.covariance.kernel.ParzenCauchy.name", "arch.covariance.kernel.ParzenCauchy.opt_bandwidth", "arch.covariance.kernel.ParzenCauchy.rate", "arch.covariance.kernel.ParzenGeometric.bandwidth", "arch.covariance.kernel.ParzenGeometric.bandwidth_scale", "arch.covariance.kernel.ParzenGeometric.centered", "arch.covariance.kernel.ParzenGeometric.cov", "arch.covariance.kernel.ParzenGeometric.force_int", "arch.covariance.kernel.ParzenGeometric.kernel_const", "arch.covariance.kernel.ParzenGeometric.kernel_weights", "arch.covariance.kernel.ParzenGeometric.name", "arch.covariance.kernel.ParzenGeometric.opt_bandwidth", "arch.covariance.kernel.ParzenGeometric.rate", "arch.covariance.kernel.ParzenRiesz.bandwidth", "arch.covariance.kernel.ParzenRiesz.bandwidth_scale", "arch.covariance.kernel.ParzenRiesz.centered", "arch.covariance.kernel.ParzenRiesz.cov", "arch.covariance.kernel.ParzenRiesz.force_int", "arch.covariance.kernel.ParzenRiesz.kernel_const", "arch.covariance.kernel.ParzenRiesz.kernel_weights", "arch.covariance.kernel.ParzenRiesz.name", "arch.covariance.kernel.ParzenRiesz.opt_bandwidth", "arch.covariance.kernel.ParzenRiesz.rate", "arch.covariance.kernel.QuadraticSpectral.bandwidth", "arch.covariance.kernel.QuadraticSpectral.bandwidth_scale", "arch.covariance.kernel.QuadraticSpectral.centered", "arch.covariance.kernel.QuadraticSpectral.cov", "arch.covariance.kernel.QuadraticSpectral.force_int", "arch.covariance.kernel.QuadraticSpectral.kernel_const", "arch.covariance.kernel.QuadraticSpectral.kernel_weights", "arch.covariance.kernel.QuadraticSpectral.name", "arch.covariance.kernel.QuadraticSpectral.opt_bandwidth", "arch.covariance.kernel.QuadraticSpectral.rate", "arch.covariance.kernel.TukeyHamming.bandwidth", "arch.covariance.kernel.TukeyHamming.bandwidth_scale", "arch.covariance.kernel.TukeyHamming.centered", "arch.covariance.kernel.TukeyHamming.cov", "arch.covariance.kernel.TukeyHamming.force_int", "arch.covariance.kernel.TukeyHamming.kernel_const", "arch.covariance.kernel.TukeyHamming.kernel_weights", "arch.covariance.kernel.TukeyHamming.name", "arch.covariance.kernel.TukeyHamming.opt_bandwidth", "arch.covariance.kernel.TukeyHamming.rate", "arch.covariance.kernel.TukeyHanning.bandwidth", "arch.covariance.kernel.TukeyHanning.bandwidth_scale", "arch.covariance.kernel.TukeyHanning.centered", "arch.covariance.kernel.TukeyHanning.cov", "arch.covariance.kernel.TukeyHanning.force_int", "arch.covariance.kernel.TukeyHanning.kernel_const", "arch.covariance.kernel.TukeyHanning.kernel_weights", "arch.covariance.kernel.TukeyHanning.name", "arch.covariance.kernel.TukeyHanning.opt_bandwidth", "arch.covariance.kernel.TukeyHanning.rate", "arch.covariance.kernel.TukeyParzen.bandwidth", "arch.covariance.kernel.TukeyParzen.bandwidth_scale", "arch.covariance.kernel.TukeyParzen.centered", "arch.covariance.kernel.TukeyParzen.cov", "arch.covariance.kernel.TukeyParzen.force_int", "arch.covariance.kernel.TukeyParzen.kernel_const", "arch.covariance.kernel.TukeyParzen.kernel_weights", "arch.covariance.kernel.TukeyParzen.name", "arch.covariance.kernel.TukeyParzen.opt_bandwidth", "arch.covariance.kernel.TukeyParzen.rate", "Introduction", "References", "arch.bootstrap.MCS", "arch.bootstrap.SPA", "arch.bootstrap.StepM", "arch.bootstrap.MCS.compute", "arch.bootstrap.MCS.excluded", "arch.bootstrap.MCS.included", "arch.bootstrap.MCS.pvalues", "arch.bootstrap.MCS.reset", "arch.bootstrap.MCS.seed", "arch.bootstrap.SPA.better_models", "arch.bootstrap.SPA.compute", "arch.bootstrap.SPA.critical_values", "arch.bootstrap.SPA.pvalues", "arch.bootstrap.SPA.reset", "arch.bootstrap.SPA.seed", "arch.bootstrap.SPA.subset", "arch.bootstrap.StepM.compute", "arch.bootstrap.StepM.reset", "arch.bootstrap.StepM.seed", "arch.bootstrap.StepM.superior_models", "Module Reference", "Multiple Comparisons", "Multiple Comparison Procedures", "Common Type Definitions", "Cointegration Tests", "arch.unitroot.ADF", "arch.unitroot.DFGLS", "arch.unitroot.KPSS", "arch.unitroot.PhillipsPerron", "arch.unitroot.VarianceRatio", "arch.unitroot.ZivotAndrews", "arch.unitroot.auto_bandwidth", "arch.unitroot.cointegration.CanonicalCointegratingReg", "arch.unitroot.cointegration.CointegrationAnalysisResults", "arch.unitroot.cointegration.DynamicOLS", "arch.unitroot.cointegration.DynamicOLSResults", "arch.unitroot.cointegration.EngleGrangerTestResults", "arch.unitroot.cointegration.FullyModifiedOLS", "arch.unitroot.cointegration.PhillipsOuliarisTestResults", "arch.unitroot.cointegration.engle_granger", "arch.unitroot.cointegration.phillips_ouliaris", "arch.unitroot.ADF.alternative_hypothesis", "arch.unitroot.ADF.critical_values", "arch.unitroot.ADF.lags", "arch.unitroot.ADF.max_lags", "arch.unitroot.ADF.nobs", "arch.unitroot.ADF.null_hypothesis", "arch.unitroot.ADF.pvalue", "arch.unitroot.ADF.regression", "arch.unitroot.ADF.stat", "arch.unitroot.ADF.summary", "arch.unitroot.ADF.trend", "arch.unitroot.ADF.valid_trends", "arch.unitroot.ADF.y", "arch.unitroot.DFGLS.alternative_hypothesis", "arch.unitroot.DFGLS.critical_values", "arch.unitroot.DFGLS.lags", "arch.unitroot.DFGLS.max_lags", "arch.unitroot.DFGLS.nobs", "arch.unitroot.DFGLS.null_hypothesis", "arch.unitroot.DFGLS.pvalue", "arch.unitroot.DFGLS.regression", "arch.unitroot.DFGLS.stat", "arch.unitroot.DFGLS.summary", "arch.unitroot.DFGLS.trend", "arch.unitroot.DFGLS.valid_trends", "arch.unitroot.DFGLS.y", "arch.unitroot.KPSS.alternative_hypothesis", "arch.unitroot.KPSS.critical_values", "arch.unitroot.KPSS.lags", "arch.unitroot.KPSS.nobs", "arch.unitroot.KPSS.null_hypothesis", "arch.unitroot.KPSS.pvalue", "arch.unitroot.KPSS.stat", "arch.unitroot.KPSS.summary", "arch.unitroot.KPSS.trend", "arch.unitroot.KPSS.valid_trends", "arch.unitroot.KPSS.y", "arch.unitroot.PhillipsPerron.alternative_hypothesis", "arch.unitroot.PhillipsPerron.critical_values", "arch.unitroot.PhillipsPerron.lags", "arch.unitroot.PhillipsPerron.nobs", "arch.unitroot.PhillipsPerron.null_hypothesis", "arch.unitroot.PhillipsPerron.pvalue", "arch.unitroot.PhillipsPerron.regression", "arch.unitroot.PhillipsPerron.stat", "arch.unitroot.PhillipsPerron.summary", "arch.unitroot.PhillipsPerron.test_type", "arch.unitroot.PhillipsPerron.trend", "arch.unitroot.PhillipsPerron.valid_trends", "arch.unitroot.PhillipsPerron.y", "arch.unitroot.VarianceRatio.alternative_hypothesis", "arch.unitroot.VarianceRatio.critical_values", "arch.unitroot.VarianceRatio.debiased", "arch.unitroot.VarianceRatio.lags", "arch.unitroot.VarianceRatio.nobs", "arch.unitroot.VarianceRatio.null_hypothesis", "arch.unitroot.VarianceRatio.overlap", "arch.unitroot.VarianceRatio.pvalue", "arch.unitroot.VarianceRatio.robust", "arch.unitroot.VarianceRatio.stat", "arch.unitroot.VarianceRatio.summary", "arch.unitroot.VarianceRatio.trend", "arch.unitroot.VarianceRatio.valid_trends", "arch.unitroot.VarianceRatio.vr", "arch.unitroot.VarianceRatio.y", "arch.unitroot.ZivotAndrews.alternative_hypothesis", "arch.unitroot.ZivotAndrews.critical_values", "arch.unitroot.ZivotAndrews.lags", "arch.unitroot.ZivotAndrews.nobs", "arch.unitroot.ZivotAndrews.null_hypothesis", "arch.unitroot.ZivotAndrews.pvalue", "arch.unitroot.ZivotAndrews.stat", "arch.unitroot.ZivotAndrews.summary", "arch.unitroot.ZivotAndrews.trend", "arch.unitroot.ZivotAndrews.valid_trends", "arch.unitroot.ZivotAndrews.y", "arch.unitroot.cointegration.CanonicalCointegratingReg.fit", "arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth", "arch.unitroot.cointegration.CointegrationAnalysisResults.cov", "arch.unitroot.cointegration.CointegrationAnalysisResults.kernel", "arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance", "arch.unitroot.cointegration.CointegrationAnalysisResults.params", "arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues", "arch.unitroot.cointegration.CointegrationAnalysisResults.resid", "arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance", "arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared", "arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj", "arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors", "arch.unitroot.cointegration.CointegrationAnalysisResults.summary", "arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues", "arch.unitroot.cointegration.DynamicOLS.fit", "arch.unitroot.cointegration.DynamicOLSResults.bandwidth", "arch.unitroot.cointegration.DynamicOLSResults.cov", "arch.unitroot.cointegration.DynamicOLSResults.cov_type", "arch.unitroot.cointegration.DynamicOLSResults.full_cov", "arch.unitroot.cointegration.DynamicOLSResults.full_params", "arch.unitroot.cointegration.DynamicOLSResults.kernel", "arch.unitroot.cointegration.DynamicOLSResults.lags", "arch.unitroot.cointegration.DynamicOLSResults.leads", "arch.unitroot.cointegration.DynamicOLSResults.long_run_variance", "arch.unitroot.cointegration.DynamicOLSResults.params", "arch.unitroot.cointegration.DynamicOLSResults.pvalues", "arch.unitroot.cointegration.DynamicOLSResults.resid", "arch.unitroot.cointegration.DynamicOLSResults.residual_variance", "arch.unitroot.cointegration.DynamicOLSResults.rsquared", "arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj", "arch.unitroot.cointegration.DynamicOLSResults.std_errors", "arch.unitroot.cointegration.DynamicOLSResults.summary", "arch.unitroot.cointegration.DynamicOLSResults.tvalues", "arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis", "arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector", "arch.unitroot.cointegration.EngleGrangerTestResults.critical_values", "arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order", "arch.unitroot.cointegration.EngleGrangerTestResults.lags", "arch.unitroot.cointegration.EngleGrangerTestResults.max_lags", "arch.unitroot.cointegration.EngleGrangerTestResults.name", "arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis", "arch.unitroot.cointegration.EngleGrangerTestResults.plot", "arch.unitroot.cointegration.EngleGrangerTestResults.pvalue", "arch.unitroot.cointegration.EngleGrangerTestResults.resid", "arch.unitroot.cointegration.EngleGrangerTestResults.rho", "arch.unitroot.cointegration.EngleGrangerTestResults.stat", "arch.unitroot.cointegration.EngleGrangerTestResults.summary", "arch.unitroot.cointegration.EngleGrangerTestResults.trend", "arch.unitroot.cointegration.FullyModifiedOLS.fit", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.name", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary", "arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend", "Introduction", "The Unit Root Tests", "Unit Root Testing", "Cointegration Testing", "Unit Root Testing", "Theoretical Background", "Distributions", "Forecasting", "arch.univariate.APARCH", "arch.univariate.ARCH", "arch.univariate.ARCHInMean", "arch.univariate.ARX", "arch.univariate.ConstantMean", "arch.univariate.ConstantVariance", "arch.univariate.EGARCH", "arch.univariate.EWMAVariance", "arch.univariate.FIGARCH", "arch.univariate.FixedVariance", "arch.univariate.GARCH", "arch.univariate.GeneralizedError", "arch.univariate.HARCH", "arch.univariate.HARX", "arch.univariate.LS", "arch.univariate.MIDASHyperbolic", "arch.univariate.Normal", "arch.univariate.RiskMetrics2006", "arch.univariate.SkewStudent", "arch.univariate.StudentsT", "arch.univariate.ZeroMean", "arch.univariate.base.ARCHModel", "arch.univariate.base.ARCHModelFixedResult", "arch.univariate.base.ARCHModelForecast", "arch.univariate.base.ARCHModelForecastSimulation", "arch.univariate.base.ARCHModelResult", "arch.univariate.distribution.Distribution", "arch.univariate.recursions.VolatilityUpdater", "arch.univariate.volatility.VarianceForecast", "arch.univariate.volatility.VolatilityProcess", "arch.univariate.APARCH.backcast", "arch.univariate.APARCH.backcast_transform", "arch.univariate.APARCH.bounds", "arch.univariate.APARCH.common_asym", "arch.univariate.APARCH.compute_variance", "arch.univariate.APARCH.constraints", "arch.univariate.APARCH.delta", "arch.univariate.APARCH.forecast", "arch.univariate.APARCH.name", "arch.univariate.APARCH.num_params", "arch.univariate.APARCH.parameter_names", "arch.univariate.APARCH.simulate", "arch.univariate.APARCH.start", "arch.univariate.APARCH.starting_values", "arch.univariate.APARCH.stop", "arch.univariate.APARCH.update", "arch.univariate.APARCH.updateable", "arch.univariate.APARCH.variance_bounds", "arch.univariate.APARCH.volatility_updater", "arch.univariate.ARCH.backcast", "arch.univariate.ARCH.backcast_transform", "arch.univariate.ARCH.bounds", "arch.univariate.ARCH.compute_variance", "arch.univariate.ARCH.constraints", "arch.univariate.ARCH.forecast", "arch.univariate.ARCH.name", "arch.univariate.ARCH.num_params", "arch.univariate.ARCH.parameter_names", "arch.univariate.ARCH.simulate", "arch.univariate.ARCH.start", "arch.univariate.ARCH.starting_values", "arch.univariate.ARCH.stop", "arch.univariate.ARCH.update", "arch.univariate.ARCH.updateable", "arch.univariate.ARCH.variance_bounds", "arch.univariate.ARCH.volatility_updater", "arch.univariate.ARCHInMean.bounds", "arch.univariate.ARCHInMean.compute_param_cov", "arch.univariate.ARCHInMean.constraints", "arch.univariate.ARCHInMean.distribution", "arch.univariate.ARCHInMean.fit", "arch.univariate.ARCHInMean.fix", "arch.univariate.ARCHInMean.forecast", "arch.univariate.ARCHInMean.form", "arch.univariate.ARCHInMean.name", "arch.univariate.ARCHInMean.num_params", "arch.univariate.ARCHInMean.parameter_names", "arch.univariate.ARCHInMean.resids", "arch.univariate.ARCHInMean.simulate", "arch.univariate.ARCHInMean.starting_values", "arch.univariate.ARCHInMean.volatility", "arch.univariate.ARCHInMean.x", "arch.univariate.ARCHInMean.y", "arch.univariate.ARX.bounds", "arch.univariate.ARX.compute_param_cov", "arch.univariate.ARX.constraints", "arch.univariate.ARX.distribution", "arch.univariate.ARX.fit", "arch.univariate.ARX.fix", "arch.univariate.ARX.forecast", "arch.univariate.ARX.name", "arch.univariate.ARX.num_params", "arch.univariate.ARX.parameter_names", "arch.univariate.ARX.resids", "arch.univariate.ARX.simulate", "arch.univariate.ARX.starting_values", "arch.univariate.ARX.volatility", "arch.univariate.ARX.x", "arch.univariate.ARX.y", "arch.univariate.ConstantMean.bounds", "arch.univariate.ConstantMean.compute_param_cov", "arch.univariate.ConstantMean.constraints", "arch.univariate.ConstantMean.distribution", "arch.univariate.ConstantMean.fit", "arch.univariate.ConstantMean.fix", "arch.univariate.ConstantMean.forecast", "arch.univariate.ConstantMean.name", "arch.univariate.ConstantMean.num_params", "arch.univariate.ConstantMean.parameter_names", "arch.univariate.ConstantMean.resids", "arch.univariate.ConstantMean.simulate", "arch.univariate.ConstantMean.starting_values", "arch.univariate.ConstantMean.volatility", "arch.univariate.ConstantMean.x", "arch.univariate.ConstantMean.y", "arch.univariate.ConstantVariance.backcast", "arch.univariate.ConstantVariance.backcast_transform", "arch.univariate.ConstantVariance.bounds", "arch.univariate.ConstantVariance.compute_variance", "arch.univariate.ConstantVariance.constraints", "arch.univariate.ConstantVariance.forecast", "arch.univariate.ConstantVariance.name", "arch.univariate.ConstantVariance.num_params", "arch.univariate.ConstantVariance.parameter_names", "arch.univariate.ConstantVariance.simulate", "arch.univariate.ConstantVariance.start", "arch.univariate.ConstantVariance.starting_values", "arch.univariate.ConstantVariance.stop", "arch.univariate.ConstantVariance.update", "arch.univariate.ConstantVariance.updateable", "arch.univariate.ConstantVariance.variance_bounds", "arch.univariate.ConstantVariance.volatility_updater", "arch.univariate.EGARCH.backcast", "arch.univariate.EGARCH.backcast_transform", "arch.univariate.EGARCH.bounds", "arch.univariate.EGARCH.compute_variance", "arch.univariate.EGARCH.constraints", "arch.univariate.EGARCH.forecast", "arch.univariate.EGARCH.name", "arch.univariate.EGARCH.num_params", "arch.univariate.EGARCH.parameter_names", "arch.univariate.EGARCH.simulate", "arch.univariate.EGARCH.start", "arch.univariate.EGARCH.starting_values", "arch.univariate.EGARCH.stop", "arch.univariate.EGARCH.update", "arch.univariate.EGARCH.updateable", "arch.univariate.EGARCH.variance_bounds", "arch.univariate.EGARCH.volatility_updater", "arch.univariate.EWMAVariance.backcast", "arch.univariate.EWMAVariance.backcast_transform", "arch.univariate.EWMAVariance.bounds", "arch.univariate.EWMAVariance.compute_variance", "arch.univariate.EWMAVariance.constraints", "arch.univariate.EWMAVariance.forecast", "arch.univariate.EWMAVariance.name", "arch.univariate.EWMAVariance.num_params", "arch.univariate.EWMAVariance.parameter_names", "arch.univariate.EWMAVariance.simulate", "arch.univariate.EWMAVariance.start", "arch.univariate.EWMAVariance.starting_values", "arch.univariate.EWMAVariance.stop", "arch.univariate.EWMAVariance.update", "arch.univariate.EWMAVariance.updateable", "arch.univariate.EWMAVariance.variance_bounds", "arch.univariate.EWMAVariance.volatility_updater", "arch.univariate.FIGARCH.backcast", "arch.univariate.FIGARCH.backcast_transform", "arch.univariate.FIGARCH.bounds", "arch.univariate.FIGARCH.compute_variance", "arch.univariate.FIGARCH.constraints", "arch.univariate.FIGARCH.forecast", "arch.univariate.FIGARCH.name", "arch.univariate.FIGARCH.num_params", "arch.univariate.FIGARCH.parameter_names", "arch.univariate.FIGARCH.simulate", "arch.univariate.FIGARCH.start", "arch.univariate.FIGARCH.starting_values", "arch.univariate.FIGARCH.stop", "arch.univariate.FIGARCH.truncation", "arch.univariate.FIGARCH.update", "arch.univariate.FIGARCH.updateable", "arch.univariate.FIGARCH.variance_bounds", "arch.univariate.FIGARCH.volatility_updater", "arch.univariate.FixedVariance.backcast", "arch.univariate.FixedVariance.backcast_transform", "arch.univariate.FixedVariance.bounds", "arch.univariate.FixedVariance.compute_variance", "arch.univariate.FixedVariance.constraints", "arch.univariate.FixedVariance.forecast", "arch.univariate.FixedVariance.name", "arch.univariate.FixedVariance.num_params", 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index fe912b82c9..d2bdd60825 100644
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+++ b/devel/types.html
@@ -15,7 +15,7 @@
     
   
       
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+            arch v7.1.1 (+18)
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 039a9ca99b..90ef1ff2e3 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.html
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.CanonicalCointegratingReg - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html b/devel/unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html
index b37407d29f..a5b2098246 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html
+++ b/devel/unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index db663e3713..30aafe397b 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLS.html
+++ b/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLS.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLSResults.html b/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLSResults.html
index a5263245ba..f0277abfe6 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLSResults.html
+++ b/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLSResults.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 626e20d549..1541a8ed11 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.FullyModifiedOLS.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index aee6c7d49d..c10890a2b7 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.engle_granger.html b/devel/unitroot/generated/arch.unitroot.cointegration.engle_granger.html
index 9b7061eae9..910d692702 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 672a7c3a9d..58cc55767d 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.phillips_ouliaris.html
+++ b/devel/unitroot/generated/arch.unitroot.cointegration.phillips_ouliaris.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.phillips_ouliaris - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 6027352959..532b9a68ba 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ADF.alternative_hypothesis.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 48a3edc101..b34a74f7ad 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    arch v7.1.1 (+18)
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--- a/devel/unitroot/generated/generated/arch.unitroot.KPSS.summary.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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+    arch v7.1.1 (+18)
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--- a/devel/unitroot/generated/generated/arch.unitroot.KPSS.trend.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.KPSS.trend.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.y - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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index c1399fd840..d0598900f5 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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index e423093a41..d33f0aa7a2 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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index 6f0491c3e6..ead609db56 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.null_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.null_hypothesis.html
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+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 3da3f7ec1c..d55687c7cb 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.pvalue.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.pvalue - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.PhillipsPerron.pvalue - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index d6c2619fac..28d7320d0e 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.regression.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.regression.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.regression - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.PhillipsPerron.regression - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html
index 652151ed7e..23892e9d66 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.stat - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.PhillipsPerron.stat - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.summary - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.PhillipsPerron.summary - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.test_type - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.PhillipsPerron.test_type - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.trend - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.PhillipsPerron.trend - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.valid_trends.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.valid_trends.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.valid_trends - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.PhillipsPerron.valid_trends - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 5ea9a909ed..9bb71fd1fc 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.y - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.alternative_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.alternative_hypothesis.html
index c21a1be8b6..baf6898d09 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.alternative_hypothesis - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.critical_values.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.critical_values.html
index 57f607541d..3de6217abb 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.critical_values.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.bandwidth - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.bandwidth - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html
index b02aa14e6c..a8d4bd6141 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.cov - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.cov - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html
index 9162e81a8e..3cda5fbaed 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.cov_type - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.cov_type - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html
index 835c6b51b6..e5f9f90cf1 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.full_cov - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.full_cov - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html
index f84d353ecf..15fbd94838 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.full_params - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.full_params - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html
index d427a29a7d..804ff184f8 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.kernel - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.kernel - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html
index ee48c55982..82ba1b0db7 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.lags - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.lags - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html
index d6f841542b..5ed93eb82a 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.leads - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.leads - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html
index 42b8edd947..cfaa157349 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.long_run_variance - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.long_run_variance - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html
index bfc08c87f0..fc1a0bf635 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.params - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.params - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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index 3b360ff299..27fdfd2554 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.critical_values.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.critical_values - arch v7.1.1 (+18)</title>
       
     
 
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+            arch v7.1.1 (+18)
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+    arch v7.1.1 (+18)
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index 04b01b7fe7..c6e1c1725f 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order.html
@@ -15,7 +15,7 @@
     
   
       
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 1e77c75436..6e577c436a 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.lags.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.lags - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.max_lags.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.max_lags.html
index 1d3a4c55c3..e064d4d16f 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.max_lags.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.name.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.name.html
index 5e9eaf22f5..ede7ba07ef 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.name.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.name - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis.html
index 1a81755f65..be58d3177b 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html
index e9edf6a040..d0f5e63fe7 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.plot - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.pvalue.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.pvalue.html
index 84c2387a43..7d0c4fbba0 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.pvalue - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html
index 4de622c9f3..768f965a4f 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html
index 5574065cff..fa74ad3e22 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.rho - arch v7.1.1 (+16)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.rho - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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+    arch v7.1.1 (+18)
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index 23b253d4fd..00571c0780 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.stat.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.stat - arch v7.1.1 (+18)</title>
       
     
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.summary - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.trend.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.trend.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+            arch v7.1.1 (+18)
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+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values.html
index 0f0e5a6b70..cbe7afe24d 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order.html
index 08668f6004..809a4d82fc 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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index 077585dafc..07432d21e3 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel.html
@@ -15,7 +15,7 @@
     
   
       
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.html
index 7f11e53f09..b628a17501 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat.html
index d83626e375..c6a695759d 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend.html
index 88bb120fc2..39cc5328cd 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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index 3cc866ddc2..02dd3d403e 100644
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-        <title>Introduction - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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diff --git a/devel/unitroot/tests.html b/devel/unitroot/tests.html
index c418a53e9c..a5fba73bff 100644
--- a/devel/unitroot/tests.html
+++ b/devel/unitroot/tests.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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diff --git a/devel/unitroot/unitroot.html b/devel/unitroot/unitroot.html
index 9a9679fbbe..a970d541b2 100644
--- a/devel/unitroot/unitroot.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>Unit Root Testing - arch v7.1.1 (+16)</title>
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@@ -87,7 +87,7 @@
 
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@@ -184,10 +184,10 @@
 
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diff --git a/devel/unitroot/unitroot_cointegration_examples.html b/devel/unitroot/unitroot_cointegration_examples.html
index 494b37f387..60b32a6e33 100644
--- a/devel/unitroot/unitroot_cointegration_examples.html
+++ b/devel/unitroot/unitroot_cointegration_examples.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Cointegration Testing - arch v7.1.1 (+16)</title>
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@@ -93,7 +93,7 @@
 
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@@ -103,7 +103,7 @@
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diff --git a/devel/unitroot/unitroot_cointegration_examples.ipynb b/devel/unitroot/unitroot_cointegration_examples.ipynb
index 632c1a497e..1264dfd2b5 100644
--- a/devel/unitroot/unitroot_cointegration_examples.ipynb
+++ b/devel/unitroot/unitroot_cointegration_examples.ipynb
@@ -19,10 +19,10 @@
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@@ -58,10 +58,10 @@
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@@ -106,10 +106,10 @@
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@@ -250,10 +250,10 @@
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@@ -288,7 +288,7 @@
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@@ -426,7 +426,7 @@
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@@ -560,7 +560,7 @@
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@@ -585,10 +585,10 @@
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@@ -635,10 +635,10 @@
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@@ -718,10 +718,10 @@
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@@ -806,10 +806,10 @@
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@@ -887,10 +887,10 @@
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@@ -975,10 +975,10 @@
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    "outputs": [
diff --git a/devel/unitroot/unitroot_examples.html b/devel/unitroot/unitroot_examples.html
index d373217c13..cc6e19641a 100644
--- a/devel/unitroot/unitroot_examples.html
+++ b/devel/unitroot/unitroot_examples.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Unit Root Testing - arch v7.1.1 (+16)</title>
+        <title>Unit Root Testing - arch v7.1.1 (+18)</title>
       
     
 
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
@@ -833,7 +833,7 @@ <h3 id="Regression-output">Regression output<a class="headerlink" href="#Regress
 Model:                            OLS   Adj. R-squared:                  0.090
 Method:                 Least Squares   F-statistic:                     17.83
 Date:                Mon, 04 Nov 2024   Prob (F-statistic):           1.30e-22
-Time:                        11:37:14   Log-Likelihood:                 630.15
+Time:                        12:33:10   Log-Likelihood:                 630.15
 No. Observations:                1194   AIC:                            -1244.
 Df Residuals:                    1186   BIC:                            -1204.
 Df Model:                           7
diff --git a/devel/unitroot/unitroot_examples.ipynb b/devel/unitroot/unitroot_examples.ipynb
index f50f9283e2..b94938fe9d 100644
--- a/devel/unitroot/unitroot_examples.ipynb
+++ b/devel/unitroot/unitroot_examples.ipynb
@@ -19,10 +19,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:41.185055Z",
-     "iopub.status.busy": "2024-11-04T11:35:41.184775Z",
-     "iopub.status.idle": "2024-11-04T11:35:42.849926Z",
-     "shell.execute_reply": "2024-11-04T11:35:42.849053Z"
+     "iopub.execute_input": "2024-11-04T12:31:34.553899Z",
+     "iopub.status.busy": "2024-11-04T12:31:34.553633Z",
+     "iopub.status.idle": "2024-11-04T12:31:36.237866Z",
+     "shell.execute_reply": "2024-11-04T12:31:36.236957Z"
     }
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    "outputs": [],
@@ -60,10 +60,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:42.854430Z",
-     "iopub.status.busy": "2024-11-04T11:35:42.853219Z",
-     "iopub.status.idle": "2024-11-04T11:35:43.876094Z",
-     "shell.execute_reply": "2024-11-04T11:35:43.875264Z"
+     "iopub.execute_input": "2024-11-04T12:31:36.241742Z",
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+     "iopub.status.idle": "2024-11-04T12:31:37.285634Z",
+     "shell.execute_reply": "2024-11-04T12:31:37.284733Z"
     }
    },
    "outputs": [
@@ -103,10 +103,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:43.881001Z",
-     "iopub.status.busy": "2024-11-04T11:35:43.879595Z",
-     "iopub.status.idle": "2024-11-04T11:35:44.496903Z",
-     "shell.execute_reply": "2024-11-04T11:35:44.496106Z"
+     "iopub.execute_input": "2024-11-04T12:31:37.289941Z",
+     "iopub.status.busy": "2024-11-04T12:31:37.289127Z",
+     "iopub.status.idle": "2024-11-04T12:31:37.997021Z",
+     "shell.execute_reply": "2024-11-04T12:31:37.995960Z"
     }
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    "outputs": [
@@ -149,10 +149,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:44.501405Z",
-     "iopub.status.busy": "2024-11-04T11:35:44.500321Z",
-     "iopub.status.idle": "2024-11-04T11:35:45.844998Z",
-     "shell.execute_reply": "2024-11-04T11:35:45.844263Z"
+     "iopub.execute_input": "2024-11-04T12:31:38.002858Z",
+     "iopub.status.busy": "2024-11-04T12:31:38.001463Z",
+     "iopub.status.idle": "2024-11-04T12:31:39.348472Z",
+     "shell.execute_reply": "2024-11-04T12:31:39.347489Z"
     }
    },
    "outputs": [
@@ -202,10 +202,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:45.849024Z",
-     "iopub.status.busy": "2024-11-04T11:35:45.848109Z",
-     "iopub.status.idle": "2024-11-04T11:35:45.866183Z",
-     "shell.execute_reply": "2024-11-04T11:35:45.865424Z"
+     "iopub.execute_input": "2024-11-04T12:31:39.352254Z",
+     "iopub.status.busy": "2024-11-04T12:31:39.351728Z",
+     "iopub.status.idle": "2024-11-04T12:31:39.367989Z",
+     "shell.execute_reply": "2024-11-04T12:31:39.367322Z"
     }
    },
    "outputs": [
@@ -258,10 +258,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:45.870001Z",
-     "iopub.status.busy": "2024-11-04T11:35:45.869070Z",
-     "iopub.status.idle": "2024-11-04T11:35:45.877922Z",
-     "shell.execute_reply": "2024-11-04T11:35:45.877325Z"
+     "iopub.execute_input": "2024-11-04T12:31:39.371208Z",
+     "iopub.status.busy": "2024-11-04T12:31:39.370941Z",
+     "iopub.status.idle": "2024-11-04T12:31:39.431169Z",
+     "shell.execute_reply": "2024-11-04T12:31:39.430483Z"
     }
    },
    "outputs": [
@@ -307,10 +307,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:45.881320Z",
-     "iopub.status.busy": "2024-11-04T11:35:45.880473Z",
-     "iopub.status.idle": "2024-11-04T11:35:45.914094Z",
-     "shell.execute_reply": "2024-11-04T11:35:45.913416Z"
+     "iopub.execute_input": "2024-11-04T12:31:39.435219Z",
+     "iopub.status.busy": "2024-11-04T12:31:39.434236Z",
+     "iopub.status.idle": "2024-11-04T12:31:39.450119Z",
+     "shell.execute_reply": "2024-11-04T12:31:39.449452Z"
     }
    },
    "outputs": [
@@ -324,7 +324,7 @@
       "Model:                            OLS   Adj. R-squared:                  0.090\n",
       "Method:                 Least Squares   F-statistic:                     17.83\n",
       "Date:                Mon, 04 Nov 2024   Prob (F-statistic):           1.30e-22\n",
-      "Time:                        11:35:45   Log-Likelihood:                 630.15\n",
+      "Time:                        12:31:39   Log-Likelihood:                 630.15\n",
       "No. Observations:                1194   AIC:                            -1244.\n",
       "Df Residuals:                    1186   BIC:                            -1204.\n",
       "Df Model:                           7                                         \n",
@@ -364,10 +364,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:45.917746Z",
-     "iopub.status.busy": "2024-11-04T11:35:45.916865Z",
-     "iopub.status.idle": "2024-11-04T11:35:46.272118Z",
-     "shell.execute_reply": "2024-11-04T11:35:46.271271Z"
+     "iopub.execute_input": "2024-11-04T12:31:39.453204Z",
+     "iopub.status.busy": "2024-11-04T12:31:39.452945Z",
+     "iopub.status.idle": "2024-11-04T12:31:39.915507Z",
+     "shell.execute_reply": "2024-11-04T12:31:39.914573Z"
     }
    },
    "outputs": [
@@ -404,10 +404,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:46.276503Z",
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-     "iopub.status.idle": "2024-11-04T11:35:46.896176Z",
-     "shell.execute_reply": "2024-11-04T11:35:46.895152Z"
+     "iopub.execute_input": "2024-11-04T12:31:39.920691Z",
+     "iopub.status.busy": "2024-11-04T12:31:39.919436Z",
+     "iopub.status.idle": "2024-11-04T12:31:40.465144Z",
+     "shell.execute_reply": "2024-11-04T12:31:40.464304Z"
     }
    },
    "outputs": [
@@ -448,10 +448,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:46.901567Z",
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-     "shell.execute_reply": "2024-11-04T11:35:47.582562Z"
+     "iopub.execute_input": "2024-11-04T12:31:40.469715Z",
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    "outputs": [
@@ -492,10 +492,10 @@
    "execution_count": 11,
    "metadata": {
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-     "iopub.execute_input": "2024-11-04T11:35:47.587677Z",
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-     "shell.execute_reply": "2024-11-04T11:35:48.281428Z"
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+     "shell.execute_reply": "2024-11-04T12:31:41.897251Z"
     }
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    "outputs": [
@@ -543,10 +543,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:48.285959Z",
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-     "shell.execute_reply": "2024-11-04T11:35:48.306387Z"
+     "iopub.execute_input": "2024-11-04T12:31:41.900771Z",
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+     "shell.execute_reply": "2024-11-04T12:31:41.925258Z"
     }
    },
    "outputs": [
@@ -587,10 +587,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:48.310728Z",
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-     "shell.execute_reply": "2024-11-04T11:35:48.326283Z"
+     "iopub.execute_input": "2024-11-04T12:31:41.929325Z",
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+     "shell.execute_reply": "2024-11-04T12:31:41.947924Z"
     }
    },
    "outputs": [
@@ -629,10 +629,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:48.330539Z",
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-     "shell.execute_reply": "2024-11-04T11:35:48.346482Z"
+     "iopub.execute_input": "2024-11-04T12:31:41.951890Z",
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+     "iopub.status.idle": "2024-11-04T12:31:41.972331Z",
+     "shell.execute_reply": "2024-11-04T12:31:41.971442Z"
     }
    },
    "outputs": [
@@ -671,10 +671,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:48.350481Z",
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-     "iopub.status.idle": "2024-11-04T11:35:48.370359Z",
-     "shell.execute_reply": "2024-11-04T11:35:48.369418Z"
+     "iopub.execute_input": "2024-11-04T12:31:41.975733Z",
+     "iopub.status.busy": "2024-11-04T12:31:41.975200Z",
+     "iopub.status.idle": "2024-11-04T12:31:41.993633Z",
+     "shell.execute_reply": "2024-11-04T12:31:41.992916Z"
     }
    },
    "outputs": [
@@ -722,10 +722,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:48.373856Z",
-     "iopub.status.busy": "2024-11-04T11:35:48.373414Z",
-     "iopub.status.idle": "2024-11-04T11:35:48.380292Z",
-     "shell.execute_reply": "2024-11-04T11:35:48.379446Z"
+     "iopub.execute_input": "2024-11-04T12:31:42.002664Z",
+     "iopub.status.busy": "2024-11-04T12:31:41.996053Z",
+     "iopub.status.idle": "2024-11-04T12:31:42.008947Z",
+     "shell.execute_reply": "2024-11-04T12:31:42.008131Z"
     }
    },
    "outputs": [
@@ -766,10 +766,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:48.383955Z",
-     "iopub.status.busy": "2024-11-04T11:35:48.383648Z",
-     "iopub.status.idle": "2024-11-04T11:35:48.392097Z",
-     "shell.execute_reply": "2024-11-04T11:35:48.391245Z"
+     "iopub.execute_input": "2024-11-04T12:31:42.011932Z",
+     "iopub.status.busy": "2024-11-04T12:31:42.011606Z",
+     "iopub.status.idle": "2024-11-04T12:31:42.019085Z",
+     "shell.execute_reply": "2024-11-04T12:31:42.018304Z"
     }
    },
    "outputs": [
@@ -810,10 +810,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:48.396671Z",
-     "iopub.status.busy": "2024-11-04T11:35:48.395575Z",
-     "iopub.status.idle": "2024-11-04T11:35:48.580826Z",
-     "shell.execute_reply": "2024-11-04T11:35:48.579908Z"
+     "iopub.execute_input": "2024-11-04T12:31:42.021808Z",
+     "iopub.status.busy": "2024-11-04T12:31:42.021394Z",
+     "iopub.status.idle": "2024-11-04T12:31:42.355153Z",
+     "shell.execute_reply": "2024-11-04T12:31:42.354273Z"
     }
    },
    "outputs": [
@@ -861,10 +861,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:48.585683Z",
-     "iopub.status.busy": "2024-11-04T11:35:48.584531Z",
-     "iopub.status.idle": "2024-11-04T11:35:48.610601Z",
-     "shell.execute_reply": "2024-11-04T11:35:48.609706Z"
+     "iopub.execute_input": "2024-11-04T12:31:42.360009Z",
+     "iopub.status.busy": "2024-11-04T12:31:42.358519Z",
+     "iopub.status.idle": "2024-11-04T12:31:42.385253Z",
+     "shell.execute_reply": "2024-11-04T12:31:42.384351Z"
     }
    },
    "outputs": [
@@ -908,10 +908,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:48.615338Z",
-     "iopub.status.busy": "2024-11-04T11:35:48.614199Z",
-     "iopub.status.idle": "2024-11-04T11:35:48.634222Z",
-     "shell.execute_reply": "2024-11-04T11:35:48.633030Z"
+     "iopub.execute_input": "2024-11-04T12:31:42.390495Z",
+     "iopub.status.busy": "2024-11-04T12:31:42.388934Z",
+     "iopub.status.idle": "2024-11-04T12:31:42.398772Z",
+     "shell.execute_reply": "2024-11-04T12:31:42.397890Z"
     }
    },
    "outputs": [
@@ -949,10 +949,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:48.639624Z",
-     "iopub.status.busy": "2024-11-04T11:35:48.638406Z",
-     "iopub.status.idle": "2024-11-04T11:35:48.649926Z",
-     "shell.execute_reply": "2024-11-04T11:35:48.649002Z"
+     "iopub.execute_input": "2024-11-04T12:31:42.403705Z",
+     "iopub.status.busy": "2024-11-04T12:31:42.402137Z",
+     "iopub.status.idle": "2024-11-04T12:31:42.413877Z",
+     "shell.execute_reply": "2024-11-04T12:31:42.413053Z"
     }
    },
    "outputs": [
diff --git a/devel/univariate/background.html b/devel/univariate/background.html
index a35be24a1f..16775af0c3 100644
--- a/devel/univariate/background.html
+++ b/devel/univariate/background.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Theoretical Background - arch v7.1.1 (+16)</title>
+        <title>Theoretical Background - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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--- a/devel/univariate/generated/arch.univariate.FixedVariance.html
+++ b/devel/univariate/generated/arch.univariate.FixedVariance.html
@@ -15,7 +15,7 @@
     
   
       
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+            arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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--- a/devel/univariate/generated/arch.univariate.GeneralizedError.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    arch v7.1.1 (+16)
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--- a/devel/univariate/generated/arch.univariate.HARX.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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--- a/devel/univariate/generated/arch.univariate.LS.html
+++ b/devel/univariate/generated/arch.univariate.LS.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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+    arch v7.1.1 (+18)
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+++ b/devel/univariate/generated/arch.univariate.Normal.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+++ b/devel/univariate/generated/generated/arch.univariate.APARCH.backcast.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index d5492df003..e792fde63a 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 5c61a14780..915913064f 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index cfe080918f..0b95610d9d 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.APARCH.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.num_params - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index e893c56ecc..e41c722bfe 100644
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.bounds.html b/devel/univariate/generated/generated/arch.univariate.ARCH.bounds.html
index 141aa16a60..628ade5419 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.bounds - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ARCH.bounds - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 4ec5ac57a1..88c08ef72e 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.compute_variance - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.constraints.html b/devel/univariate/generated/generated/arch.univariate.ARCH.constraints.html
index e8b8028032..4e1dcd686d 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.constraints - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ARCH.constraints - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.forecast.html b/devel/univariate/generated/generated/arch.univariate.ARCH.forecast.html
index 6704bfbeb4..7430575416 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.forecast - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ARCH.forecast - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.name.html b/devel/univariate/generated/generated/arch.univariate.ARCH.name.html
index 22607766d8..d5201b67d6 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.name - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ARCH.name - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.num_params.html b/devel/univariate/generated/generated/arch.univariate.ARCH.num_params.html
index c566a17d7e..07537f0f08 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.num_params - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.ARCH.parameter_names.html
index 9d9e6c68d5..59083c9606 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.parameter_names - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.simulate.html b/devel/univariate/generated/generated/arch.univariate.ARCH.simulate.html
index d7570ad484..68cb216544 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.simulate - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.start.html b/devel/univariate/generated/generated/arch.univariate.ARCH.start.html
index 238e44b61b..c7a8fc537f 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.start.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.start.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.start - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ARCH.start - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.starting_values.html b/devel/univariate/generated/generated/arch.univariate.ARCH.starting_values.html
index d7eab72613..810b399d8f 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.starting_values - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ARCH.starting_values - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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+            arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+            arch v7.1.1 (+18)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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index 0cdcd0dc20..efbc7a9a21 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+++ b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.form.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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index 05019e245a..166ad4657c 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index c2df00d05b..48ff386258 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.y.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 06fd3ee1ed..5664307c03 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARX.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARX.bounds.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.ARX.bounds - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 553ac251bb..ef404b621b 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARX.compute_param_cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.compute_param_cov - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.constraints.html b/devel/univariate/generated/generated/arch.univariate.ARX.constraints.html
index b92d41316f..f06391581f 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARX.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARX.constraints.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.distribution.html b/devel/univariate/generated/generated/arch.univariate.ARX.distribution.html
index 2676161c98..c4e14e5a34 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.fit.html b/devel/univariate/generated/generated/arch.univariate.ARX.fit.html
index a2c187c99b..88f5406531 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARX.fit.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.fix.html b/devel/univariate/generated/generated/arch.univariate.ARX.fix.html
index 3379cdd6f7..48e322bcb3 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.forecast.html b/devel/univariate/generated/generated/arch.univariate.ARX.forecast.html
index 72804aba5a..3504a81b31 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.name.html b/devel/univariate/generated/generated/arch.univariate.ARX.name.html
index 69e13ded30..af65f192e3 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.num_params.html b/devel/univariate/generated/generated/arch.univariate.ARX.num_params.html
index 1317807968..dfb417b359 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARX.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARX.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.num_params - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    arch v7.1.1 (+16)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.distribution.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.distribution - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantMean.distribution - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index b8325a5493..6d3c349c21 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.fit.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.fit - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantMean.fit - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 968d432296..8f5f5630d4 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.fix.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.fix - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantMean.fix - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 81edfbdf5c..1abbd01daa 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.forecast - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantMean.forecast - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 97c3c40a35..7274e00500 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.name - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantMean.name - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.num_params.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.num_params.html
index a8e0eb46a8..3c931b41a0 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.num_params - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantMean.num_params - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html
index 83cd18fb31..8c860e6caf 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.parameter_names - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.resids.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.resids.html
index 1f8ecb7a9e..d7e726bb79 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.resids.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.resids.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.resids - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantMean.resids - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.simulate.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.simulate.html
index 7a451c75a9..dbbc45f171 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.simulate - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantMean.simulate - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html
index 8bc8a85384..350e7d0033 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.starting_values - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantMean.starting_values - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html
index 360949256f..a6d062a7a3 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.volatility - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantMean.volatility - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.x.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.x.html
index db6165e6fa..049053e16c 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.x.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.x - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantMean.x - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.y.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.y.html
index 1aa76181f4..ed7ea74704 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.y.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.y - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantMean.y - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html
index 42b951c56f..9b7176b3f8 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.backcast - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantVariance.backcast - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html
index 5888b2c7ef..92d6e0fe6e 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.backcast_transform - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantVariance.backcast_transform - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html
index 35e77e7d41..d8098fc3e6 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.bounds - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantVariance.bounds - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html
index d1cfa407c1..b74395867d 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.compute_variance - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantVariance.compute_variance - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html
index 523f401ce7..1e296eaee1 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.constraints - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantVariance.constraints - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html
index 27a0994308..b19109434a 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.forecast - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantVariance.forecast - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.name.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.name.html
index 0ae10895c2..5c413b4cf8 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.name - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantVariance.name - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.num_params.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.num_params.html
index 76bc9c73a3..ec3568676e 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.num_params - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.ConstantVariance.num_params - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.simulate.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
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+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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+    arch v7.1.1 (+18)
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.updateable - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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index b35f280d5a..a582bb90cf 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.variance_bounds - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index e31a6dc45e..68198d6fca 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index d9e52da8d6..76d70a28d0 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.backcast - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 50485c18f5..3edd51ab6c 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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index 320e503f23..fd23ddab4d 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+    arch v7.1.1 (+18)
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+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index b20596901e..dcf4b09afd 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 1847de3bab..11002f1522 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.name - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.num_params - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.parameter_names - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 0a25fb6392..30f631ea2b 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.start.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.start.html
index 80f521d495..f570e80151 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.starting_values.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.starting_values.html
index db97899e6c..ad320dc212 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.starting_values - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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index 0799b17fca..9ea3cc6b67 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.stop.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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--- a/devel/univariate/generated/generated/arch.univariate.EGARCH.updateable.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.backcast.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.backcast.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.backcast_transform.html
index a173d34b18..12dbc17d01 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.bounds.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.bounds.html
index 4c19a06a83..23a49fc98f 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.bounds - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html
index 32afab8bc5..448636073f 100644
--- a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.compute_variance - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html
index 96f9fd885e..5df9055857 100644
--- a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.constraints - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+    arch v7.1.1 (+18)
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index c5730d1921..8ac676e0f2 100644
--- a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.variance_bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.variance_bounds - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.EWMAVariance.variance_bounds - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 52d8bb7954..f5742dd4a6 100644
--- a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.volatility_updater.html
+++ b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.volatility_updater - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.EWMAVariance.volatility_updater - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index d373e9e041..c05c6cd5b0 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.backcast - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.FIGARCH.backcast - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html
index 587762e9c0..0c2546c139 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.backcast_transform - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html
index 79fa0a0bb1..4921e3d9ec 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.bounds - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.FIGARCH.bounds - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html
index f5ca306b96..7968aee9d8 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.compute_variance - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.FIGARCH.compute_variance - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html
index fa39015bca..e16d192c65 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.constraints - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.forecast.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.forecast.html
index 592fd7d596..4869eca5ce 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.forecast - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.FIGARCH.forecast - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.name.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.name.html
index c80283bf67..fd8a650a7d 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.name - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.num_params.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.num_params.html
index c1f4e8e9ad..5c3c4c93bb 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.num_params - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.FIGARCH.num_params - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+    arch v7.1.1 (+18)
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index 910a72ed1d..0ab24cba15 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast.html
index fcb37d9510..5fc5923062 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.backcast - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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index 602534ab9f..f34017cac4 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.backcast_transform - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.FixedVariance.backcast_transform - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.bounds.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 177a8b02fd..86c9e8e708 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.constraints - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.forecast.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.forecast.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.forecast - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index b247741693..ce3d07f73f 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.num_params.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.num_params.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.num_params - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.parameter_names.html
index 699197cbde..afef30136e 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.parameter_names - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.simulate.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.simulate.html
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--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.simulate - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index cb5ed0b3e0..4a7e35f4db 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.start - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+    arch v7.1.1 (+18)
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index d50e023851..d537245de1 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.starting_values - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.FixedVariance.starting_values - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+    arch v7.1.1 (+18)
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index b73c8e2eeb..fa23da22ef 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.stop.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.stop.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index a97755d151..929ba6a68f 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.update.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.update - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.FixedVariance.update - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.updateable.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.updateable.html
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--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.updateable.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.updateable - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 24cdef91b9..eefe628c68 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.variance_bounds - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.FixedVariance.variance_bounds - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 9d3671519e..78257620f7 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.volatility_updater.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.volatility_updater - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.backcast.html b/devel/univariate/generated/generated/arch.univariate.GARCH.backcast.html
index e2d32684e9..ff2c70033d 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.backcast - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.GARCH.backcast_transform.html
index 564e984d78..738d7dbb6e 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.backcast_transform - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.bounds.html b/devel/univariate/generated/generated/arch.univariate.GARCH.bounds.html
index b3836a0888..f2e958ff30 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.bounds - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.GARCH.compute_variance.html
index b7bc4acda7..8a38b15fa1 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.compute_variance - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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index 521c4e4460..7696e6b27f 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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index c7922b8cfc..28ade57e16 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.bounds.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 1868e74acc..180897494d 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.cdf - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.constraints - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.generator - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.parameter_names.html
index 03fc49e3f0..0e9baf9ccd 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.parameter_names - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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index c937f13295..b47aa804da 100644
--- a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.partial_moment.html
+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.partial_moment.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.GeneralizedError.partial_moment - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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--- a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.ppf.html
+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.ppf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.ppf - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.GeneralizedError.ppf - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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--- a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.random_state.html
+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.random_state - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index c94998e19c..25cae3044a 100644
--- a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.simulate - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.starting_values.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.starting_values.html
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--- a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARCH.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.HARCH.backcast_transform.html
index f1e3a6de21..73d9655647 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.backcast_transform - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 17ed189de3..be39e116cc 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARCH.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.HARCH.compute_variance.html
index d7b5ffbe76..a8cb7c1b7d 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.compute_variance - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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index fd09c98dab..f053172f68 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.HARCH.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.constraints - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
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+++ b/devel/univariate/generated/generated/arch.univariate.HARCH.forecast.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 06e492d8ae..8eea058c96 100644
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARCH.update.html b/devel/univariate/generated/generated/arch.univariate.HARCH.update.html
index 75cd7c1639..f526340f27 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 91d78fdcc7..bf11a3b3f6 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARCH.variance_bounds.html b/devel/univariate/generated/generated/arch.univariate.HARCH.variance_bounds.html
index 9aa08a5cae..de7a13ccef 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.num_params.html b/devel/univariate/generated/generated/arch.univariate.HARX.num_params.html
index b8a2515960..94bce6ee1d 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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index b875e00b16..14d562563f 100644
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.distribution.html b/devel/univariate/generated/generated/arch.univariate.LS.distribution.html
index 45713e1372..7abeb83024 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.distribution.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.distribution.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.distribution - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.LS.distribution - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.fit.html b/devel/univariate/generated/generated/arch.univariate.LS.fit.html
index 0da1dd8650..66ba0aac8b 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.fit.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.fit - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.LS.fit - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.fix.html b/devel/univariate/generated/generated/arch.univariate.LS.fix.html
index 279e5f56f0..bd4c927868 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.fix.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.fix - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.LS.fix - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.forecast.html b/devel/univariate/generated/generated/arch.univariate.LS.forecast.html
index 3456157f66..8040ac3b12 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.forecast - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.LS.forecast - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.name.html b/devel/univariate/generated/generated/arch.univariate.LS.name.html
index 5b10770b80..561ddc9894 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.name - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.LS.name - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.num_params.html b/devel/univariate/generated/generated/arch.univariate.LS.num_params.html
index 39be8c999d..d91652e1f6 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.num_params - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.LS.num_params - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.LS.parameter_names.html
index 0ba7b8bac0..1c56d2f6fa 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.parameter_names - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.LS.resids.html b/devel/univariate/generated/generated/arch.univariate.LS.resids.html
index 7ad850fc04..9da7fee5b2 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.resids.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.resids.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.resids - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.LS.resids - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.LS.simulate.html b/devel/univariate/generated/generated/arch.univariate.LS.simulate.html
index e05da9b389..6ae52a334e 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.simulate - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.LS.simulate - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.starting_values.html b/devel/univariate/generated/generated/arch.univariate.LS.starting_values.html
index c8babb6712..dd1155ae40 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.starting_values - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.LS.starting_values - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.LS.volatility.html b/devel/univariate/generated/generated/arch.univariate.LS.volatility.html
index 85bff3897c..d2efbd2fef 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.volatility.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.volatility - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.LS.volatility - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.x.html b/devel/univariate/generated/generated/arch.univariate.LS.x.html
index 43cf43dcc2..b9fee6944d 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.x.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.x - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.LS.x - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.LS.y.html b/devel/univariate/generated/generated/arch.univariate.LS.y.html
index 353d3eea0d..92e42ba501 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.y.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.y - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.LS.y - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html
index 765dc99157..253dd3c160 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.backcast - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.MIDASHyperbolic.backcast - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html
index 08ef03d6cc..35aa92a2ef 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.backcast_transform - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.MIDASHyperbolic.backcast_transform - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html
index 211f250c92..026bf7a96e 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.bounds - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.MIDASHyperbolic.bounds - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html
index 2fa7a84f74..fee600b660 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.compute_variance - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.MIDASHyperbolic.compute_variance - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html
index ca3b6a48c4..77fab60a7f 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.constraints - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.MIDASHyperbolic.constraints - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.forecast.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.forecast.html
index e2631179d8..65160ef4e1 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.forecast - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.MIDASHyperbolic.forecast - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.name.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.name.html
index c71ce1ad7d..6185b75639 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.name - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.MIDASHyperbolic.name - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.num_params.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.num_params.html
index 6e00e4519c..783dab567f 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.num_params - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.MIDASHyperbolic.num_params - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 4f3741bba9..80763a44bf 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.MIDASHyperbolic.parameter_names - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 23487e8abc..19ea9224f4 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.simulate - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.MIDASHyperbolic.simulate - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 7928f6a135..5dadda2e7f 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.start.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.start.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.MIDASHyperbolic.start - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.starting_values.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.starting_values.html
index 427494e121..c57845e78a 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.starting_values.html
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.MIDASHyperbolic.starting_values - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.stop.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.stop.html
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--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.stop.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.stop - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.MIDASHyperbolic.stop - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.update.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.update.html
index 83c38704b6..77584a5f23 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.update.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.update.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.updateable.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.updateable.html
index 3b3afbb2f4..d792769c2f 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.updateable.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.updateable - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.MIDASHyperbolic.updateable - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.variance_bounds.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.variance_bounds.html
index 31977a2a87..e3af66b495 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.variance_bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.variance_bounds - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.MIDASHyperbolic.variance_bounds - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.volatility_updater.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.volatility_updater.html
index d5d7207899..6ad565aa1a 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.volatility_updater.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.volatility_updater - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.bounds.html b/devel/univariate/generated/generated/arch.univariate.Normal.bounds.html
index e0c01c245c..846b389327 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.bounds - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.Normal.bounds - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.cdf.html b/devel/univariate/generated/generated/arch.univariate.Normal.cdf.html
index 11fecbea3b..0af8a56bba 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.cdf.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.cdf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.cdf - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.Normal.cdf - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.constraints.html b/devel/univariate/generated/generated/arch.univariate.Normal.constraints.html
index ef477310e3..7f8ff62ece 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.constraints - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.Normal.constraints - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.generator.html b/devel/univariate/generated/generated/arch.univariate.Normal.generator.html
index 0183cd3415..f759f572e1 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.generator.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.generator - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.Normal.generator - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.loglikelihood.html b/devel/univariate/generated/generated/arch.univariate.Normal.loglikelihood.html
index ed84c5e311..e556c761f4 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.loglikelihood.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.loglikelihood - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.Normal.loglikelihood - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.moment.html b/devel/univariate/generated/generated/arch.univariate.Normal.moment.html
index c117ccfa23..c103d5fd3b 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.moment.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.moment - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.Normal.moment - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.name.html b/devel/univariate/generated/generated/arch.univariate.Normal.name.html
index 0cc047a997..469963d4af 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.name - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.Normal.name - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.Normal.parameter_names.html
index 5765a318ac..00afda4a93 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.parameter_names - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.Normal.parameter_names - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.partial_moment.html b/devel/univariate/generated/generated/arch.univariate.Normal.partial_moment.html
index da88e4ad21..faf6fc788f 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.partial_moment.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.partial_moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.partial_moment - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.Normal.partial_moment - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.ppf.html b/devel/univariate/generated/generated/arch.univariate.Normal.ppf.html
index 644d9b8632..ce158109bf 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.ppf.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.ppf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.ppf - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.Normal.ppf - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.random_state.html b/devel/univariate/generated/generated/arch.univariate.Normal.random_state.html
index 6ac858c3eb..bb6159c9d0 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.random_state.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.random_state - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.Normal.random_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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+++ b/devel/univariate/generated/generated/arch.univariate.Normal.simulate.html
@@ -15,7 +15,7 @@
     
   
       
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+            arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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+++ b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.constraints.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.constraints.html
index 8c174a1e9e..317b3bfd22 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.constraints - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.SkewStudent.constraints - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.generator.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.generator.html
index ef1042bffc..83dde896af 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.generator.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.generator - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.SkewStudent.generator - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.loglikelihood.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.loglikelihood.html
index 0b37f08ac5..4f8d35cf62 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.loglikelihood.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.loglikelihood - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.SkewStudent.loglikelihood - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.moment.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.moment.html
index 0c4e0ff2f0..8c000c7c56 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.moment.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.moment - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.SkewStudent.moment - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.name.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.name.html
index d34ae75064..4df1da0b14 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.name - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.SkewStudent.name - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.parameter_names.html
index 6390cb71b0..2b2a7d7d5f 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.parameter_names - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.SkewStudent.parameter_names - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html
index 990d3b7e9e..2c6cfaf3a8 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.partial_moment - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.SkewStudent.partial_moment - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html
index fec8407a94..643c681da4 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.ppf - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.SkewStudent.ppf - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.random_state.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.random_state.html
index 039423f7ff..b75e0336b3 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.random_state.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.random_state - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.SkewStudent.random_state - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.simulate.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.simulate.html
index 08e3c66daa..cf2fec2eea 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.simulate - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.SkewStudent.simulate - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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index bc6b7ba0d7..a437c83cd2 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.starting_values - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.SkewStudent.starting_values - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+    arch v7.1.1 (+18)
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--- a/devel/univariate/generated/generated/arch.univariate.StudentsT.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.bounds.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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--- a/devel/univariate/generated/generated/arch.univariate.StudentsT.cdf.html
+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.cdf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.cdf - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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--- a/devel/univariate/generated/generated/arch.univariate.StudentsT.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.constraints.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.StudentsT.generator.html b/devel/univariate/generated/generated/arch.univariate.StudentsT.generator.html
index 32baeb82bd..c881a67a3f 100644
--- a/devel/univariate/generated/generated/arch.univariate.StudentsT.generator.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.loglikelihood - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 234c70c787..beefd12503 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index da67a8416a..1cd0b5542b 100644
--- a/devel/univariate/generated/generated/arch.univariate.StudentsT.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.parameter_names - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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index 11f94a6546..294b9c101b 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.partial_moment - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.ppf.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.random_state.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
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--- a/devel/univariate/generated/generated/arch.univariate.StudentsT.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.simulate.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index b2cc9d8cff..dec14cfe0e 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.distribution.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.distribution.html
index e0d2a94d05..480d520397 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 1d5c31ce82..6bbe9790fb 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.fix.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.fix.html
index 343e130767..23550c7df7 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.forecast.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.forecast.html
index e145379767..046b13e920 100644
--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.forecast.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.forecast - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 0cd1b5d8e6..8bf631c53d 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 8c30e9eb98..92252091dd 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 359893c06a..bade2aebbf 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.y - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 8996d89777..8161b52f47 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.bounds.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.base.ARCHModelResult.scale - arch v7.1.1 (+18)</title>
       
     
 
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_err.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.base.ARCHModelResult.std_err - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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+    arch v7.1.1 (+18)
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--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_resid.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_resid.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.base.ARCHModelResult.std_resid - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.summary.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.summary - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.base.ARCHModelResult.summary - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 1f83e8e889..a994d69b9c 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.bounds.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index b55bc7ab6f..8e61ce37e5 100644
--- a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.cdf.html
+++ b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.cdf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.cdf - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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index 08d3230a72..4ee670a3d8 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.constraints - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index ca34a2398a..6022390906 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.generator.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.loglikelihood.html b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.loglikelihood.html
index aa34305a05..708fc88495 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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index 4318ea1767..80ad54612f 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    arch v7.1.1 (+16)
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index 5aca442af2..2a34682a9a 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+            arch v7.1.1 (+18)
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+    arch v7.1.1 (+18)
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--- a/devel/univariate/generated/generated/arch.univariate.volatility.VarianceForecast.shocks.html
+++ b/devel/univariate/generated/generated/arch.univariate.volatility.VarianceForecast.shocks.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.volatility.VarianceForecast.shocks - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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+    arch v7.1.1 (+18)
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--- a/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast.html
+++ b/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.backcast - arch v7.1.1 (+16)</title>
+        <title>arch.univariate.volatility.VolatilityProcess.backcast - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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+++ b/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.volatility.VolatilityProcess.backcast_transform - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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+++ b/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.bounds - arch v7.1.1 (+16)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.volatility.VolatilityProcess.compute_variance - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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index 1354f1426b..abe42e81ed 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.num_params.html b/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.num_params.html
index aca2479e7d..8c8c508af9 100644
--- a/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.num_params.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
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-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
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diff --git a/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.parameter_names.html
index c7795e3530..453628428b 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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index 56ab4e711e..608f9ae052 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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index 1d2ec471ba..7880dd67cd 100644
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@@ -92,7 +92,7 @@
 
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-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/results.html b/devel/univariate/results.html
index 3993e1ccd0..3e91ba4b64 100644
--- a/devel/univariate/results.html
+++ b/devel/univariate/results.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Model Results - arch v7.1.1 (+16)</title>
+        <title>Model Results - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/univariate.html b/devel/univariate/univariate.html
index 8a86363634..a814b002ad 100644
--- a/devel/univariate/univariate.html
+++ b/devel/univariate/univariate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Univariate Volatility Models - arch v7.1.1 (+16)</title>
+        <title>Univariate Volatility Models - arch v7.1.1 (+18)</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/univariate_forecasting_with_exogenous_variables.html b/devel/univariate/univariate_forecasting_with_exogenous_variables.html
index a08fca0991..69e262f019 100644
--- a/devel/univariate/univariate_forecasting_with_exogenous_variables.html
+++ b/devel/univariate/univariate_forecasting_with_exogenous_variables.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Forecasting with Exogenous Regressors - arch v7.1.1 (+16)</title>
+        <title>Forecasting with Exogenous Regressors - arch v7.1.1 (+18)</title>
       
     
 
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
@@ -1060,8 +1060,8 @@ <h2 id="Forecasting-the-X-values">Forecasting the X values<a class="headerlink"
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-array([5.06287863, 5.03143932, 5.01571966, 5.00785983, 5.00392991,
-       5.00196496, 5.00098248, 5.00049124, 5.00024562, 5.00012281])
+array([4.60803748, 4.80401874, 4.90200937, 4.95100468, 4.97550234,
+       4.98775117, 4.99387559, 4.99693779, 4.9984689 , 4.99923445])
 </pre></div></div>
 </div>
 <h2 id="Fitting-the-model">Fitting the model<a class="headerlink" href="#Fitting-the-model" title="Link to this heading">¶</a></h2>
@@ -1084,30 +1084,30 @@ <h2 id="Fitting-the-model">Fitting the model<a class="headerlink" href="#Fitting
 <div class="highlight"><pre>
                           AR-X - GARCH Model Results
 ==============================================================================
-Dep. Variable:                   data   R-squared:                       0.990
-Mean Model:                      AR-X   Adj. R-squared:                  0.990
-Vol Model:                      GARCH   Log-Likelihood:               -1373.15
-Distribution:                  Normal   AIC:                           2760.31
-Method:            Maximum Likelihood   BIC:                           2794.65
+Dep. Variable:                   data   R-squared:                       0.992
+Mean Model:                      AR-X   Adj. R-squared:                  0.992
+Vol Model:                      GARCH   Log-Likelihood:               -1356.70
+Distribution:                  Normal   AIC:                           2727.39
+Method:            Maximum Likelihood   BIC:                           2761.74
                                         No. Observations:                  999
 Date:                Mon, Nov 04 2024   Df Residuals:                      995
-Time:                        11:37:02   Df Model:                            4
+Time:                        12:32:57   Df Model:                            4
                                Mean Model
 ========================================================================
                  coef    std err          t      P&gt;|t|  95.0% Conf. Int.
 ------------------------------------------------------------------------
-Const          3.1359      0.167     18.755  1.755e-78 [  2.808,  3.464]
-data[1]        0.7033  3.806e-03    184.759      0.000 [  0.696,  0.711]
-x0             1.9946  2.304e-02     86.577      0.000 [  1.949,  2.040]
-x1            -2.0285  2.682e-02    -75.619      0.000 [ -2.081, -1.976]
-                             Volatility Model
-==========================================================================
-                 coef    std err          t      P&gt;|t|    95.0% Conf. Int.
---------------------------------------------------------------------------
-omega          0.0937  3.338e-02      2.806  5.022e-03 [2.823e-02,  0.159]
-alpha[1]       0.0686  2.237e-02      3.065  2.180e-03 [2.471e-02,  0.112]
-beta[1]        0.8300  4.784e-02     17.349  1.995e-67   [  0.736,  0.924]
-==========================================================================
+Const          3.0509      0.162     18.824  4.807e-79 [  2.733,  3.369]
+data[1]        0.6911  3.759e-03    183.834      0.000 [  0.684,  0.698]
+x0             2.0338  2.120e-02     95.944      0.000 [  1.992,  2.075]
+x1            -2.0181  2.710e-02    -74.481      0.000 [ -2.071, -1.965]
+                               Volatility Model
+=============================================================================
+                 coef    std err          t      P&gt;|t|       95.0% Conf. Int.
+-----------------------------------------------------------------------------
+omega          0.1575      0.107      1.466      0.143   [-5.303e-02,  0.368]
+alpha[1]       0.0496  2.536e-02      1.956  5.052e-02 [-1.121e-04,9.931e-02]
+beta[1]        0.7737      0.134      5.758  8.488e-09      [  0.510,  1.037]
+=============================================================================
 
 Covariance estimator: robust
 </pre></div></div>
@@ -1216,133 +1216,133 @@ <h2 id="Producing-multiple-forecasts">Producing multiple forecasts<a class="head
   <tbody>
     <tr>
       <th>990</th>
-      <td>3.936189</td>
-      <td>3.272171</td>
-      <td>3.436563</td>
-      <td>3.984636</td>
-      <td>4.679974</td>
-      <td>5.399032</td>
-      <td>6.079952</td>
-      <td>6.694685</td>
-      <td>7.233598</td>
-      <td>7.696865</td>
+      <td>27.314303</td>
+      <td>26.128042</td>
+      <td>24.589133</td>
+      <td>22.906024</td>
+      <td>21.224355</td>
+      <td>19.635506</td>
+      <td>18.189875</td>
+      <td>16.909387</td>
+      <td>15.797478</td>
+      <td>14.846444</td>
     </tr>
     <tr>
       <th>991</th>
-      <td>-0.387330</td>
-      <td>-0.260791</td>
-      <td>0.590091</td>
-      <td>1.709403</td>
-      <td>2.869238</td>
-      <td>3.961161</td>
-      <td>4.939250</td>
-      <td>5.789914</td>
-      <td>6.515793</td>
-      <td>7.127122</td>
+      <td>32.160943</td>
+      <td>29.953536</td>
+      <td>27.364096</td>
+      <td>24.802822</td>
+      <td>22.454730</td>
+      <td>20.389023</td>
+      <td>18.616630</td>
+      <td>17.120575</td>
+      <td>15.871989</td>
+      <td>14.838436</td>
     </tr>
     <tr>
       <th>992</th>
-      <td>0.630414</td>
-      <td>0.976673</td>
-      <td>1.674090</td>
-      <td>2.540491</td>
-      <td>3.457299</td>
-      <td>4.351678</td>
-      <td>5.182345</td>
-      <td>5.928985</td>
-      <td>6.584684</td>
-      <td>7.150688</td>
+      <td>26.416045</td>
+      <td>23.520892</td>
+      <td>20.906023</td>
+      <td>18.706703</td>
+      <td>16.922701</td>
+      <td>15.503509</td>
+      <td>14.386263</td>
+      <td>13.511325</td>
+      <td>12.827623</td>
+      <td>12.293527</td>
     </tr>
     <tr>
       <th>993</th>
-      <td>1.248537</td>
-      <td>0.975692</td>
-      <td>1.103402</td>
-      <td>1.571283</td>
-      <td>2.264658</td>
-      <td>3.075172</td>
-      <td>3.919540</td>
-      <td>4.741110</td>
-      <td>5.505400</td>
-      <td>6.194378</td>
+      <td>24.489390</td>
+      <td>21.722839</td>
+      <td>19.300475</td>
+      <td>17.311773</td>
+      <td>15.732780</td>
+      <td>14.501559</td>
+      <td>13.550639</td>
+      <td>12.819497</td>
+      <td>12.258118</td>
+      <td>11.826856</td>
     </tr>
     <tr>
       <th>994</th>
-      <td>-1.508928</td>
-      <td>-1.828094</td>
-      <td>-1.468314</td>
-      <td>-0.671483</td>
-      <td>0.362872</td>
-      <td>1.489441</td>
-      <td>2.611371</td>
-      <td>3.669529</td>
-      <td>4.631900</td>
-      <td>5.484836</td>
+      <td>19.003419</td>
+      <td>16.832340</td>
+      <td>15.186358</td>
+      <td>13.953791</td>
+      <td>13.036812</td>
+      <td>12.356515</td>
+      <td>11.851958</td>
+      <td>11.477217</td>
+      <td>11.198181</td>
+      <td>10.989713</td>
     </tr>
     <tr>
       <th>995</th>
-      <td>11.843792</td>
-      <td>13.485318</td>
-      <td>13.990311</td>
-      <td>13.930033</td>
-      <td>13.607153</td>
-      <td>13.181465</td>
-      <td>12.735982</td>
-      <td>12.312114</td>
-      <td>11.928667</td>
-      <td>11.592232</td>
+      <td>14.693448</td>
+      <td>12.632756</td>
+      <td>11.369583</td>
+      <td>10.615284</td>
+      <td>10.184152</td>
+      <td>9.956125</td>
+      <td>9.853509</td>
+      <td>9.826173</td>
+      <td>9.842014</td>
+      <td>9.880723</td>
     </tr>
     <tr>
       <th>996</th>
-      <td>16.329221</td>
-      <td>17.200559</td>
-      <td>17.212974</td>
-      <td>16.765587</td>
-      <td>16.097739</td>
-      <td>15.351045</td>
-      <td>14.606877</td>
-      <td>13.909436</td>
-      <td>13.280146</td>
-      <td>12.726708</td>
+      <td>8.103566</td>
+      <td>6.370812</td>
+      <td>5.776833</td>
+      <td>5.788174</td>
+      <td>6.103377</td>
+      <td>6.552317</td>
+      <td>7.040255</td>
+      <td>7.516134</td>
+      <td>7.954298</td>
+      <td>8.343779</td>
     </tr>
     <tr>
       <th>997</th>
-      <td>16.943210</td>
-      <td>16.348475</td>
-      <td>15.548706</td>
-      <td>14.723080</td>
-      <td>13.952838</td>
-      <td>13.269873</td>
-      <td>12.681705</td>
-      <td>12.184327</td>
-      <td>11.768801</td>
-      <td>11.424599</td>
+      <td>5.341480</td>
+      <td>4.381054</td>
+      <td>4.087323</td>
+      <td>4.243321</td>
+      <td>4.670469</td>
+      <td>5.237665</td>
+      <td>5.855826</td>
+      <td>6.468483</td>
+      <td>7.042652</td>
+      <td>7.561379</td>
     </tr>
     <tr>
       <th>998</th>
-      <td>14.943955</td>
-      <td>13.808667</td>
-      <td>12.771144</td>
-      <td>11.933437</td>
-      <td>11.299687</td>
-      <td>10.839349</td>
-      <td>10.514522</td>
-      <td>10.290598</td>
-      <td>10.139475</td>
-      <td>10.039686</td>
+      <td>11.144492</td>
+      <td>10.548660</td>
+      <td>9.934469</td>
+      <td>9.482191</td>
+      <td>9.214889</td>
+      <td>9.100449</td>
+      <td>9.094850</td>
+      <td>9.158558</td>
+      <td>9.261146</td>
+      <td>9.381217</td>
     </tr>
     <tr>
       <th>999</th>
-      <td>12.296464</td>
-      <td>11.388004</td>
-      <td>10.806928</td>
-      <td>10.435617</td>
-      <td>10.199979</td>
-      <td>10.052518</td>
-      <td>9.962384</td>
-      <td>9.909363</td>
-      <td>9.880141</td>
-      <td>9.865943</td>
+      <td>13.589678</td>
+      <td>13.560771</td>
+      <td>13.229948</td>
+      <td>12.810267</td>
+      <td>12.396197</td>
+      <td>12.025211</td>
+      <td>11.708164</td>
+      <td>11.444122</td>
+      <td>11.227496</td>
+      <td>11.051367</td>
     </tr>
   </tbody>
 </table>
@@ -1446,29 +1446,29 @@ <h2 id="Special-Cases-with-a-single-x-variable">Special Cases with a single <cod
 <div class="highlight"><pre>
                           AR-X - GARCH Model Results
 ==============================================================================
-Dep. Variable:                   data   R-squared:                       0.934
-Mean Model:                      AR-X   Adj. R-squared:                  0.934
-Vol Model:                      GARCH   Log-Likelihood:               -2323.26
-Distribution:                  Normal   AIC:                           4658.52
-Method:            Maximum Likelihood   BIC:                           4687.96
+Dep. Variable:                   data   R-squared:                       0.947
+Mean Model:                      AR-X   Adj. R-squared:                  0.947
+Vol Model:                      GARCH   Log-Likelihood:               -2324.73
+Distribution:                  Normal   AIC:                           4661.45
+Method:            Maximum Likelihood   BIC:                           4690.89
                                         No. Observations:                  999
 Date:                Mon, Nov 04 2024   Df Residuals:                      996
-Time:                        11:37:02   Df Model:                            3
+Time:                        12:32:58   Df Model:                            3
                                Mean Model
 ========================================================================
                  coef    std err          t      P&gt;|t|  95.0% Conf. Int.
 ------------------------------------------------------------------------
-Const         -6.7726      0.278    -24.402 1.639e-131 [ -7.317, -6.229]
-data[1]        0.7415  1.140e-02     65.055      0.000 [  0.719,  0.764]
-x0             1.8655  6.630e-02     28.136 3.520e-174 [  1.736,  1.995]
-                             Volatility Model
-==========================================================================
-                 coef    std err          t      P&gt;|t|    95.0% Conf. Int.
---------------------------------------------------------------------------
-omega          3.9809      1.151      3.459  5.412e-04   [  1.726,  6.236]
-alpha[1]       0.1425  4.671e-02      3.052  2.276e-03 [5.099e-02,  0.234]
-beta[1]        0.2195      0.185      1.187      0.235   [ -0.143,  0.582]
-==========================================================================
+Const         -6.5768      0.259    -25.429 1.191e-142 [ -7.084, -6.070]
+data[1]        0.7654  9.416e-03     81.285      0.000 [  0.747,  0.784]
+x0             1.7625  5.865e-02     30.052 2.062e-198 [  1.648,  1.877]
+                              Volatility Model
+===========================================================================
+                 coef    std err          t      P&gt;|t|     95.0% Conf. Int.
+---------------------------------------------------------------------------
+omega          5.5033      3.140      1.752  7.970e-02    [ -0.652, 11.658]
+alpha[1]       0.1147  9.369e-02      1.224      0.221 [-6.897e-02,  0.298]
+beta[1]        0.0000      0.581      0.000      1.000    [ -1.139,  1.139]
+===========================================================================
 
 Covariance estimator: robust
 </pre></div></div>
@@ -1518,8 +1518,8 @@ <h3 id="Static-out-of-sample-x">Static out-of-sample <code class="docutils liter
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-array([[0.96573609, 1.12818176, 1.27181596, 1.3228566 , 1.19022239,
-        1.25786933, 1.3111209 , 1.30761741, 1.27377076, 1.3823135 ]])
+array([[0.90776848, 1.01861   , 0.98325674, 1.20735108, 1.08413908,
+        1.22037117, 1.19070924, 1.37300659, 1.32222697, 1.23404414]])
 </pre></div></div>
 </div>
 <h3 id="Simulating-the-out-of-sample-x">Simulating the out-of-sample <code class="docutils literal notranslate"><span class="pre">x</span></code><a class="headerlink" href="#Simulating-the-out-of-sample-x" title="Link to this heading">¶</a></h3>
@@ -1567,8 +1567,8 @@ <h3 id="Simulating-the-out-of-sample-x">Simulating the out-of-sample <code class
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-array([[3.11596413, 4.94574   , 6.46627971, 7.57477582, 8.30789544,
-        8.71384095, 8.89916046, 8.994375  , 9.44519064, 9.63090577]])
+array([[3.21077105, 4.92071295, 6.36104612, 7.59856914, 8.52630442,
+        8.77916236, 8.8538268 , 8.91881848, 9.18739146, 9.36742011]])
 </pre></div></div>
 </div>
 <h2 id="Conditional-Mean-Alignment-vs.-Forecast-Alignment">Conditional Mean Alignment vs. Forecast Alignment<a class="headerlink" href="#Conditional-Mean-Alignment-vs.-Forecast-Alignment" title="Link to this heading">¶</a></h2>
@@ -1593,45 +1593,46 @@ <h2 id="Conditional-Mean-Alignment-vs.-Forecast-Alignment">Conditional Mean Alig
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 0            NaN
-1      19.201886
-2      19.592296
-3      21.956620
-4      18.674146
+1      15.527460
+2      17.145197
+3      17.213133
+4      18.025002
          ...
-995     8.026979
-996    13.378036
-997    15.800237
-998    16.812645
-999    13.615690
+995    16.858582
+996    10.734250
+997     8.477494
+998     9.750082
+999    13.318992
 Length: 1000, dtype: float64
 </pre></div></div>
 </div>
 <h3 id="Conditional-Mean-uses-target-alignment">Conditional Mean uses target alignment<a class="headerlink" href="#Conditional-Mean-uses-target-alignment" title="Link to this heading">¶</a></h3>
-<p>When modeling the conditional mean in an AR-X, HAR-X, or LS model, the <span class="math notranslate nohighlight">\(X\)</span> data is target-aligned. This requires that when modeling the mean of <code class="docutils literal notranslate"><span class="pre">y[t]</span></code>, the correct values of <span class="math notranslate nohighlight">\(X\)</span> must appear in <code class="docutils literal notranslate"><span class="pre">x[t]</span></code>. Mathematically, the <span class="math notranslate nohighlight">\(X\)</span> matrix used when estimating a model should have the structure</p>
+<p>When modeling the conditional mean in an AR-X, HAR-X, or LS model, the <span class="math notranslate nohighlight">\(X\)</span> data is target-aligned. This requires that when modeling the mean of <code class="docutils literal notranslate"><span class="pre">y[t]</span></code>, the correct values of <span class="math notranslate nohighlight">\(X\)</span> must appear in <code class="docutils literal notranslate"><span class="pre">x[t]</span></code>. Mathematically, the <span class="math notranslate nohighlight">\(X\)</span> matrix used when estimating a model should have the structure (using the Python indexing convention of a T-element data set having indices 0, 1, …, T-1):</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}\left[\begin{array}{c}
 X_{0}\\
 X_{1}\\
 \vdots\\
-X_{t-1}
+X_{T-1}
 \end{array}\right]\end{split}\]</div>
 <h3 id="forecast-uses-origin-alignment"><code class="docutils literal notranslate"><span class="pre">forecast</span></code> uses origin alignment<a class="headerlink" href="#forecast-uses-origin-alignment" title="Link to this heading">¶</a></h3>
 <p>Forecasting with <span class="math notranslate nohighlight">\(X\)</span> values aligns them differently. When producing a 1-step-ahead forecast for <span class="math notranslate nohighlight">\(Y_{t+1}\)</span> using information available at time <span class="math notranslate nohighlight">\(t\)</span>, the <span class="math notranslate nohighlight">\(X\)</span> values used for this forecast must appear in for <code class="docutils literal notranslate"><span class="pre">t</span></code>. This is needed since when once wants to produce true out-of-sample forecasts (see below), it must be the case that the final row of <code class="docutils literal notranslate"><span class="pre">x</span></code> passed <code class="docutils literal notranslate"><span class="pre">forecast</span></code> must all occur after the final time stamp of the most recent <span class="math notranslate nohighlight">\(Y\)</span> value. Mathematically, the <span class="math notranslate nohighlight">\(X\)</span>
 matrix used in forecasting should have the following structure (using Python indexing convention so that a <span class="math notranslate nohighlight">\(T\)</span> observation dataset will have indices 0, 1, …, T-1).</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}\left[\begin{array}{cccc}
-E\left[X_{1|0}\right] &amp; E\left[X_{2|0}\right] &amp; \ldots &amp; E\left[X_{h|0}\right]\\
-E\left[X_{2|1}\right] &amp; E\left[X_{3|1}\right] &amp; \ldots &amp; E\left[X_{h+1|1}\right]\\
+E\left[X_{1}|\mathcal{F}_0\right] &amp; E\left[X_{2}|\mathcal{F}_0\right] &amp; \ldots &amp; E\left[X_{h|\mathcal{F}_0}\right]\\
+E\left[X_{2}|\mathcal{F}_1\right] &amp; E\left[X_{3}|\mathcal{F}_0\right] &amp; \ldots &amp; E\left[X_{h+1}|\mathcal{F}_1\right]\\
 \vdots &amp; \vdots &amp; \vdots &amp; \vdots\\
-E\left[X_{T|T-1}\right] &amp; E\left[X_{T+1|T-1}\right] &amp; \ldots &amp; E\left[X_{T+h-1|T-1}\right]
+E\left[X_{T}|\mathcal{F}_{T-1}\right] &amp; E\left[X_{T+1}|\mathcal{F}_{T-1}\right] &amp; \ldots &amp; E\left[X_{T+h-1}|\mathcal{F}_{T-1}\right]
 \end{array}\right]\end{split}\]</div>
+<p>where <span class="math notranslate nohighlight">\(|\mathcal{F}_{s}\)</span> is the time-<span class="math notranslate nohighlight">\(s\)</span> information set.</p>
 <p>If you use the same <code class="docutils literal notranslate"><span class="pre">x</span></code> value in the model when forecasting, you will see different values due to this alignment difference. Naively using the same <code class="docutils literal notranslate"><span class="pre">x</span></code> values ie equivalent to setting</p>
 <div class="math notranslate nohighlight">
-\[E\left[X_{j|j-1}] \right] = X_{j-1}\]</div>
+\[E\left[X_{s}|\mathcal{F}_{s-1} \right] = X_{s-1}\]</div>
 <p>In general this would not be correct when forecasting, and will always produce forecasts that differ from the conditional mean. In order to recover the conditional mean using the forecast function, it is necessary to <code class="docutils literal notranslate"><span class="pre">shift</span></code> the <span class="math notranslate nohighlight">\(X\)</span> values by -1, so that once shifted, the <code class="docutils literal notranslate"><span class="pre">x</span></code> values will have the relationship</p>
 <div class="math notranslate nohighlight">
-\[E\left[X_{j|j-1}] \right] = X_{j} .\]</div>
-<p>Here we shift the <span class="math notranslate nohighlight">\(X\)</span> data by <code class="docutils literal notranslate"><span class="pre">-1</span></code> so <code class="docutils literal notranslate"><span class="pre">x[j]</span></code> is treated as being in the information set for <code class="docutils literal notranslate"><span class="pre">y[j-1]</span></code>. Also note that the final forecast is <code class="docutils literal notranslate"><span class="pre">NaN</span></code>. Conceptually this must be the case because the value of <span class="math notranslate nohighlight">\(X\)</span> at 999 should be ahead of 999 (i.e., at observation 1,000), and we do not have this value.</p>
+\[E\left[X_{s}|\mathcal{F}_{s-1} \right] = X_{s} .\]</div>
+<p>Here we shift the <span class="math notranslate nohighlight">\(X\)</span> data by <code class="docutils literal notranslate"><span class="pre">-1</span></code> so <code class="docutils literal notranslate"><span class="pre">x[s]</span></code> is treated as being in the information set for <code class="docutils literal notranslate"><span class="pre">y[s-1]</span></code>. Also, note that the final forecast is <code class="docutils literal notranslate"><span class="pre">NaN</span></code>. Conceptually this must be the case because the value of <span class="math notranslate nohighlight">\(X\)</span> at 999 should be ahead of 999 (i.e., at observation 1,000), and we do not have this value.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[18]:
 </code></pre></div>
@@ -1671,23 +1672,23 @@ <h3 id="forecast-uses-origin-alignment"><code class="docutils literal notranslat
   <tbody>
     <tr>
       <th>0</th>
-      <td>19.201886</td>
+      <td>15.527460</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>19.592296</td>
+      <td>17.145197</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>21.956620</td>
+      <td>17.213133</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>18.674146</td>
+      <td>18.025002</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>16.099861</td>
+      <td>22.834203</td>
     </tr>
     <tr>
       <th>...</th>
@@ -1695,19 +1696,19 @@ <h3 id="forecast-uses-origin-alignment"><code class="docutils literal notranslat
     </tr>
     <tr>
       <th>995</th>
-      <td>13.378036</td>
+      <td>10.734250</td>
     </tr>
     <tr>
       <th>996</th>
-      <td>15.800237</td>
+      <td>8.477494</td>
     </tr>
     <tr>
       <th>997</th>
-      <td>16.812645</td>
+      <td>9.750082</td>
     </tr>
     <tr>
       <th>998</th>
-      <td>13.615690</td>
+      <td>13.318992</td>
     </tr>
     <tr>
       <th>999</th>
@@ -1766,23 +1767,23 @@ <h3 id="(Nearly)-out-of-sample-forecasts">(Nearly) out-of-sample forecasts<a cla
   <tbody>
     <tr>
       <th>0</th>
-      <td>20.103522</td>
+      <td>9.081665</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>16.232286</td>
+      <td>15.483518</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>18.559099</td>
+      <td>17.427116</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>17.916252</td>
+      <td>16.607654</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>18.682919</td>
+      <td>19.990744</td>
     </tr>
     <tr>
       <th>...</th>
@@ -1790,19 +1791,19 @@ <h3 id="(Nearly)-out-of-sample-forecasts">(Nearly) out-of-sample forecasts<a cla
     </tr>
     <tr>
       <th>995</th>
-      <td>10.738150</td>
+      <td>13.696883</td>
     </tr>
     <tr>
       <th>996</th>
-      <td>16.474858</td>
+      <td>9.501098</td>
     </tr>
     <tr>
       <th>997</th>
-      <td>17.336579</td>
+      <td>6.063016</td>
     </tr>
     <tr>
       <th>998</th>
-      <td>13.687288</td>
+      <td>9.900736</td>
     </tr>
     <tr>
       <th>999</th>
@@ -1869,16 +1870,16 @@ <h3 id="True-out-of-sample-forecasts">True out-of-sample forecasts<a class="head
   <tbody>
     <tr>
       <th>999</th>
-      <td>12.289069</td>
-      <td>11.367745</td>
-      <td>10.771001</td>
-      <td>10.384016</td>
-      <td>10.134274</td>
-      <td>9.97485</td>
-      <td>9.874886</td>
-      <td>9.813904</td>
-      <td>9.778258</td>
-      <td>9.758859</td>
+      <td>13.441362</td>
+      <td>13.239109</td>
+      <td>12.738644</td>
+      <td>12.165736</td>
+      <td>11.618694</td>
+      <td>11.134496</td>
+      <td>10.721741</td>
+      <td>10.376805</td>
+      <td>10.091564</td>
+      <td>9.856942</td>
     </tr>
   </tbody>
 </table>
diff --git a/devel/univariate/univariate_forecasting_with_exogenous_variables.ipynb b/devel/univariate/univariate_forecasting_with_exogenous_variables.ipynb
index 4ac71c544f..11a9332e28 100644
--- a/devel/univariate/univariate_forecasting_with_exogenous_variables.ipynb
+++ b/devel/univariate/univariate_forecasting_with_exogenous_variables.ipynb
@@ -38,10 +38,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:19.097788Z",
-     "iopub.status.busy": "2024-11-04T11:35:19.097478Z",
-     "iopub.status.idle": "2024-11-04T11:35:20.880379Z",
-     "shell.execute_reply": "2024-11-04T11:35:20.879435Z"
+     "iopub.execute_input": "2024-11-04T12:31:12.946673Z",
+     "iopub.status.busy": "2024-11-04T12:31:12.946380Z",
+     "iopub.status.idle": "2024-11-04T12:31:14.584147Z",
+     "shell.execute_reply": "2024-11-04T12:31:14.583402Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -83,10 +83,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:20.885754Z",
-     "iopub.status.busy": "2024-11-04T11:35:20.884445Z",
-     "iopub.status.idle": "2024-11-04T11:35:21.344726Z",
-     "shell.execute_reply": "2024-11-04T11:35:21.343733Z"
+     "iopub.execute_input": "2024-11-04T12:31:14.587488Z",
+     "iopub.status.busy": "2024-11-04T12:31:14.586876Z",
+     "iopub.status.idle": "2024-11-04T12:31:15.041970Z",
+     "shell.execute_reply": "2024-11-04T12:31:15.041205Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -137,10 +137,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:21.349804Z",
-     "iopub.status.busy": "2024-11-04T11:35:21.348672Z",
-     "iopub.status.idle": "2024-11-04T11:35:21.703453Z",
-     "shell.execute_reply": "2024-11-04T11:35:21.702478Z"
+     "iopub.execute_input": "2024-11-04T12:31:15.045363Z",
+     "iopub.status.busy": "2024-11-04T12:31:15.044729Z",
+     "iopub.status.idle": "2024-11-04T12:31:15.360423Z",
+     "shell.execute_reply": "2024-11-04T12:31:15.359526Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -152,7 +152,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -186,10 +186,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:21.765363Z",
-     "iopub.status.busy": "2024-11-04T11:35:21.764962Z",
-     "iopub.status.idle": "2024-11-04T11:35:21.778377Z",
-     "shell.execute_reply": "2024-11-04T11:35:21.777405Z"
+     "iopub.execute_input": "2024-11-04T12:31:15.409635Z",
+     "iopub.status.busy": "2024-11-04T12:31:15.409290Z",
+     "iopub.status.idle": "2024-11-04T12:31:15.421276Z",
+     "shell.execute_reply": "2024-11-04T12:31:15.420478Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -202,8 +202,8 @@
     {
      "data": {
       "text/plain": [
-       "array([5.10481468, 5.05240734, 5.02620367, 5.01310183, 5.00655092,\n",
-       "       5.00327546, 5.00163773, 5.00081886, 5.00040943, 5.00020472])"
+       "array([5.03283221, 5.01641611, 5.00820805, 5.00410403, 5.00205201,\n",
+       "       5.00102601, 5.000513  , 5.0002565 , 5.00012825, 5.00006413])"
       ]
      },
      "execution_count": 4,
@@ -248,10 +248,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:21.782303Z",
-     "iopub.status.busy": "2024-11-04T11:35:21.781712Z",
-     "iopub.status.idle": "2024-11-04T11:35:21.866595Z",
-     "shell.execute_reply": "2024-11-04T11:35:21.865682Z"
+     "iopub.execute_input": "2024-11-04T12:31:15.424815Z",
+     "iopub.status.busy": "2024-11-04T12:31:15.424245Z",
+     "iopub.status.idle": "2024-11-04T12:31:15.495549Z",
+     "shell.execute_reply": "2024-11-04T12:31:15.494709Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -267,29 +267,29 @@
      "text": [
       "                          AR-X - GARCH Model Results                          \n",
       "==============================================================================\n",
-      "Dep. Variable:                   data   R-squared:                       0.990\n",
-      "Mean Model:                      AR-X   Adj. R-squared:                  0.990\n",
-      "Vol Model:                      GARCH   Log-Likelihood:               -1431.95\n",
-      "Distribution:                  Normal   AIC:                           2877.90\n",
-      "Method:            Maximum Likelihood   BIC:                           2912.24\n",
+      "Dep. Variable:                   data   R-squared:                       0.991\n",
+      "Mean Model:                      AR-X   Adj. R-squared:                  0.991\n",
+      "Vol Model:                      GARCH   Log-Likelihood:               -1390.39\n",
+      "Distribution:                  Normal   AIC:                           2794.78\n",
+      "Method:            Maximum Likelihood   BIC:                           2829.13\n",
       "                                        No. Observations:                  999\n",
       "Date:                Mon, Nov 04 2024   Df Residuals:                      995\n",
-      "Time:                        11:35:21   Df Model:                            4\n",
+      "Time:                        12:31:15   Df Model:                            4\n",
       "                               Mean Model                               \n",
       "========================================================================\n",
       "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
       "------------------------------------------------------------------------\n",
-      "Const          2.8481      0.175     16.230  3.113e-59 [  2.504,  3.192]\n",
-      "data[1]        0.7030  4.076e-03    172.485      0.000 [  0.695,  0.711]\n",
-      "x0             1.9892  2.542e-02     78.250      0.000 [  1.939,  2.039]\n",
-      "x1            -1.9746  2.723e-02    -72.509      0.000 [ -2.028, -1.921]\n",
+      "Const          2.8541      0.170     16.824  1.635e-63 [  2.522,  3.187]\n",
+      "data[1]        0.7032  3.639e-03    193.204      0.000 [  0.696,  0.710]\n",
+      "x0             1.9988  2.362e-02     84.615      0.000 [  1.952,  2.045]\n",
+      "x1            -1.9842  2.742e-02    -72.369      0.000 [ -2.038, -1.930]\n",
       "                             Volatility Model                             \n",
       "==========================================================================\n",
       "                 coef    std err          t      P>|t|    95.0% Conf. Int.\n",
       "--------------------------------------------------------------------------\n",
-      "omega          0.0772  3.133e-02      2.465  1.371e-02 [1.582e-02,  0.139]\n",
-      "alpha[1]       0.0901  2.510e-02      3.591  3.293e-04 [4.094e-02,  0.139]\n",
-      "beta[1]        0.8387  4.452e-02     18.838  3.654e-79   [  0.751,  0.926]\n",
+      "omega          0.1154  4.497e-02      2.565  1.031e-02 [2.722e-02,  0.204]\n",
+      "alpha[1]       0.0785  2.640e-02      2.975  2.926e-03 [2.681e-02,  0.130]\n",
+      "beta[1]        0.8014  6.117e-02     13.100  3.289e-39   [  0.681,  0.921]\n",
       "==========================================================================\n",
       "\n",
       "Covariance estimator: robust\n"
@@ -326,10 +326,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:21.871584Z",
-     "iopub.status.busy": "2024-11-04T11:35:21.870395Z",
-     "iopub.status.idle": "2024-11-04T11:35:22.135919Z",
-     "shell.execute_reply": "2024-11-04T11:35:22.134981Z"
+     "iopub.execute_input": "2024-11-04T12:31:15.500093Z",
+     "iopub.status.busy": "2024-11-04T12:31:15.499027Z",
+     "iopub.status.idle": "2024-11-04T12:31:15.745026Z",
+     "shell.execute_reply": "2024-11-04T12:31:15.744140Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -351,7 +351,7 @@
     },
     {
      "data": {
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       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -381,10 +381,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:22.141038Z",
-     "iopub.status.busy": "2024-11-04T11:35:22.139812Z",
-     "iopub.status.idle": "2024-11-04T11:35:22.156654Z",
-     "shell.execute_reply": "2024-11-04T11:35:22.155759Z"
+     "iopub.execute_input": "2024-11-04T12:31:15.749851Z",
+     "iopub.status.busy": "2024-11-04T12:31:15.748723Z",
+     "iopub.status.idle": "2024-11-04T12:31:15.764579Z",
+     "shell.execute_reply": "2024-11-04T12:31:15.763755Z"
     },
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      "outputs_hidden": false
@@ -426,10 +426,10 @@
    "metadata": {
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-     "iopub.status.idle": "2024-11-04T11:35:22.286667Z",
-     "shell.execute_reply": "2024-11-04T11:35:22.285727Z"
+     "iopub.execute_input": "2024-11-04T12:31:15.769201Z",
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+     "iopub.status.idle": "2024-11-04T12:31:15.869048Z",
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      "outputs_hidden": false
@@ -475,133 +475,133 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>990</th>\n",
-       "      <td>7.730108</td>\n",
-       "      <td>7.787503</td>\n",
-       "      <td>7.890355</td>\n",
-       "      <td>8.039606</td>\n",
-       "      <td>8.219401</td>\n",
-       "      <td>8.412242</td>\n",
-       "      <td>8.604159</td>\n",
-       "      <td>8.785702</td>\n",
-       "      <td>8.951361</td>\n",
-       "      <td>9.098587</td>\n",
+       "      <td>23.574232</td>\n",
+       "      <td>24.184688</td>\n",
+       "      <td>23.457877</td>\n",
+       "      <td>22.134477</td>\n",
+       "      <td>20.609805</td>\n",
+       "      <td>19.089958</td>\n",
+       "      <td>17.676570</td>\n",
+       "      <td>16.413562</td>\n",
+       "      <td>15.313296</td>\n",
+       "      <td>14.371394</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>991</th>\n",
-       "      <td>13.762935</td>\n",
-       "      <td>13.493430</td>\n",
-       "      <td>12.858808</td>\n",
-       "      <td>12.190980</td>\n",
-       "      <td>11.611163</td>\n",
-       "      <td>11.148651</td>\n",
-       "      <td>10.796170</td>\n",
-       "      <td>10.534729</td>\n",
-       "      <td>10.344082</td>\n",
-       "      <td>10.206572</td>\n",
+       "      <td>23.391861</td>\n",
+       "      <td>22.690869</td>\n",
+       "      <td>21.439125</td>\n",
+       "      <td>20.001786</td>\n",
+       "      <td>18.569993</td>\n",
+       "      <td>17.238395</td>\n",
+       "      <td>16.048082</td>\n",
+       "      <td>15.010730</td>\n",
+       "      <td>14.122332</td>\n",
+       "      <td>13.371071</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>992</th>\n",
-       "      <td>13.931439</td>\n",
-       "      <td>13.286085</td>\n",
-       "      <td>12.461913</td>\n",
-       "      <td>11.716061</td>\n",
-       "      <td>11.123246</td>\n",
-       "      <td>10.683864</td>\n",
-       "      <td>10.372811</td>\n",
-       "      <td>10.160282</td>\n",
-       "      <td>10.019659</td>\n",
-       "      <td>9.929719</td>\n",
+       "      <td>25.255768</td>\n",
+       "      <td>23.499000</td>\n",
+       "      <td>21.543405</td>\n",
+       "      <td>19.672211</td>\n",
+       "      <td>17.999540</td>\n",
+       "      <td>16.557743</td>\n",
+       "      <td>15.341305</td>\n",
+       "      <td>14.328759</td>\n",
+       "      <td>13.493443</td>\n",
+       "      <td>12.808605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>993</th>\n",
-       "      <td>13.277199</td>\n",
-       "      <td>12.057514</td>\n",
-       "      <td>11.080017</td>\n",
-       "      <td>10.378560</td>\n",
-       "      <td>9.914566</td>\n",
-       "      <td>9.631723</td>\n",
-       "      <td>9.477417</td>\n",
-       "      <td>9.409382</td>\n",
-       "      <td>9.396233</td>\n",
-       "      <td>9.415842</td>\n",
+       "      <td>28.496529</td>\n",
+       "      <td>26.791664</td>\n",
+       "      <td>24.708677</td>\n",
+       "      <td>22.598020</td>\n",
+       "      <td>20.627476</td>\n",
+       "      <td>18.867701</td>\n",
+       "      <td>17.338291</td>\n",
+       "      <td>16.032817</td>\n",
+       "      <td>14.932516</td>\n",
+       "      <td>14.013745</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>994</th>\n",
-       "      <td>7.723261</td>\n",
-       "      <td>6.808237</td>\n",
-       "      <td>6.587439</td>\n",
-       "      <td>6.714732</td>\n",
-       "      <td>7.002304</td>\n",
-       "      <td>7.348833</td>\n",
-       "      <td>7.700787</td>\n",
-       "      <td>8.031245</td>\n",
-       "      <td>8.328115</td>\n",
-       "      <td>8.587494</td>\n",
+       "      <td>22.470856</td>\n",
+       "      <td>19.932870</td>\n",
+       "      <td>17.816867</td>\n",
+       "      <td>16.109586</td>\n",
+       "      <td>14.756549</td>\n",
+       "      <td>13.694667</td>\n",
+       "      <td>12.865441</td>\n",
+       "      <td>12.219273</td>\n",
+       "      <td>11.715944</td>\n",
+       "      <td>11.323618</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>995</th>\n",
-       "      <td>12.860308</td>\n",
-       "      <td>12.686606</td>\n",
-       "      <td>12.273454</td>\n",
-       "      <td>11.824363</td>\n",
-       "      <td>11.418753</td>\n",
-       "      <td>11.080144</td>\n",
-       "      <td>10.808521</td>\n",
-       "      <td>10.595274</td>\n",
-       "      <td>10.429791</td>\n",
-       "      <td>10.302121</td>\n",
+       "      <td>25.222384</td>\n",
+       "      <td>23.684687</td>\n",
+       "      <td>21.803961</td>\n",
+       "      <td>19.940765</td>\n",
+       "      <td>18.247341</td>\n",
+       "      <td>16.774514</td>\n",
+       "      <td>15.525438</td>\n",
+       "      <td>14.482448</td>\n",
+       "      <td>13.620310</td>\n",
+       "      <td>12.912569</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>996</th>\n",
-       "      <td>17.543268</td>\n",
-       "      <td>17.007492</td>\n",
-       "      <td>15.965260</td>\n",
-       "      <td>14.858901</td>\n",
-       "      <td>13.861451</td>\n",
-       "      <td>13.024029</td>\n",
-       "      <td>12.346048</td>\n",
-       "      <td>11.807807</td>\n",
-       "      <td>11.384995</td>\n",
-       "      <td>11.054625</td>\n",
+       "      <td>23.649885</td>\n",
+       "      <td>21.812372</td>\n",
+       "      <td>19.978453</td>\n",
+       "      <td>18.302041</td>\n",
+       "      <td>16.836961</td>\n",
+       "      <td>15.589270</td>\n",
+       "      <td>14.543655</td>\n",
+       "      <td>13.676611</td>\n",
+       "      <td>12.962878</td>\n",
+       "      <td>12.378435</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>997</th>\n",
-       "      <td>13.182799</td>\n",
-       "      <td>12.064435</td>\n",
-       "      <td>11.254060</td>\n",
-       "      <td>10.691007</td>\n",
-       "      <td>10.313301</td>\n",
-       "      <td>10.068552</td>\n",
-       "      <td>9.916169</td>\n",
-       "      <td>9.826251</td>\n",
-       "      <td>9.777496</td>\n",
-       "      <td>9.755103</td>\n",
+       "      <td>22.256693</td>\n",
+       "      <td>20.398623</td>\n",
+       "      <td>18.699419</td>\n",
+       "      <td>17.205901</td>\n",
+       "      <td>15.924374</td>\n",
+       "      <td>14.841916</td>\n",
+       "      <td>13.937519</td>\n",
+       "      <td>13.187852</td>\n",
+       "      <td>12.570141</td>\n",
+       "      <td>12.063518</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
-       "      <td>10.413635</td>\n",
-       "      <td>9.266262</td>\n",
-       "      <td>8.580070</td>\n",
-       "      <td>8.233426</td>\n",
-       "      <td>8.117691</td>\n",
-       "      <td>8.148123</td>\n",
-       "      <td>8.263492</td>\n",
-       "      <td>8.421927</td>\n",
-       "      <td>8.596161</td>\n",
-       "      <td>8.769366</td>\n",
+       "      <td>26.947307</td>\n",
+       "      <td>25.928049</td>\n",
+       "      <td>24.383327</td>\n",
+       "      <td>22.646091</td>\n",
+       "      <td>20.908875</td>\n",
+       "      <td>19.277038</td>\n",
+       "      <td>17.802222</td>\n",
+       "      <td>16.503543</td>\n",
+       "      <td>15.381047</td>\n",
+       "      <td>14.424210</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
-       "      <td>10.329565</td>\n",
-       "      <td>9.744141</td>\n",
-       "      <td>9.455254</td>\n",
-       "      <td>9.333989</td>\n",
-       "      <td>9.305960</td>\n",
-       "      <td>9.327857</td>\n",
-       "      <td>9.374401</td>\n",
-       "      <td>9.430951</td>\n",
-       "      <td>9.489202</td>\n",
-       "      <td>9.544653</td>\n",
+       "      <td>26.289402</td>\n",
+       "      <td>24.535316</td>\n",
+       "      <td>22.694467</td>\n",
+       "      <td>20.907669</td>\n",
+       "      <td>19.253787</td>\n",
+       "      <td>17.770828</td>\n",
+       "      <td>16.470867</td>\n",
+       "      <td>15.350302</td>\n",
+       "      <td>14.396729</td>\n",
+       "      <td>13.593420</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -609,28 +609,28 @@
       ],
       "text/plain": [
        "          h.01       h.02       h.03       h.04       h.05       h.06  \\\n",
-       "990   7.730108   7.787503   7.890355   8.039606   8.219401   8.412242   \n",
-       "991  13.762935  13.493430  12.858808  12.190980  11.611163  11.148651   \n",
-       "992  13.931439  13.286085  12.461913  11.716061  11.123246  10.683864   \n",
-       "993  13.277199  12.057514  11.080017  10.378560   9.914566   9.631723   \n",
-       "994   7.723261   6.808237   6.587439   6.714732   7.002304   7.348833   \n",
-       "995  12.860308  12.686606  12.273454  11.824363  11.418753  11.080144   \n",
-       "996  17.543268  17.007492  15.965260  14.858901  13.861451  13.024029   \n",
-       "997  13.182799  12.064435  11.254060  10.691007  10.313301  10.068552   \n",
-       "998  10.413635   9.266262   8.580070   8.233426   8.117691   8.148123   \n",
-       "999  10.329565   9.744141   9.455254   9.333989   9.305960   9.327857   \n",
+       "990  23.574232  24.184688  23.457877  22.134477  20.609805  19.089958   \n",
+       "991  23.391861  22.690869  21.439125  20.001786  18.569993  17.238395   \n",
+       "992  25.255768  23.499000  21.543405  19.672211  17.999540  16.557743   \n",
+       "993  28.496529  26.791664  24.708677  22.598020  20.627476  18.867701   \n",
+       "994  22.470856  19.932870  17.816867  16.109586  14.756549  13.694667   \n",
+       "995  25.222384  23.684687  21.803961  19.940765  18.247341  16.774514   \n",
+       "996  23.649885  21.812372  19.978453  18.302041  16.836961  15.589270   \n",
+       "997  22.256693  20.398623  18.699419  17.205901  15.924374  14.841916   \n",
+       "998  26.947307  25.928049  24.383327  22.646091  20.908875  19.277038   \n",
+       "999  26.289402  24.535316  22.694467  20.907669  19.253787  17.770828   \n",
        "\n",
        "          h.07       h.08       h.09       h.10  \n",
-       "990   8.604159   8.785702   8.951361   9.098587  \n",
-       "991  10.796170  10.534729  10.344082  10.206572  \n",
-       "992  10.372811  10.160282  10.019659   9.929719  \n",
-       "993   9.477417   9.409382   9.396233   9.415842  \n",
-       "994   7.700787   8.031245   8.328115   8.587494  \n",
-       "995  10.808521  10.595274  10.429791  10.302121  \n",
-       "996  12.346048  11.807807  11.384995  11.054625  \n",
-       "997   9.916169   9.826251   9.777496   9.755103  \n",
-       "998   8.263492   8.421927   8.596161   8.769366  \n",
-       "999   9.374401   9.430951   9.489202   9.544653  "
+       "990  17.676570  16.413562  15.313296  14.371394  \n",
+       "991  16.048082  15.010730  14.122332  13.371071  \n",
+       "992  15.341305  14.328759  13.493443  12.808605  \n",
+       "993  17.338291  16.032817  14.932516  14.013745  \n",
+       "994  12.865441  12.219273  11.715944  11.323618  \n",
+       "995  15.525438  14.482448  13.620310  12.912569  \n",
+       "996  14.543655  13.676611  12.962878  12.378435  \n",
+       "997  13.937519  13.187852  12.570141  12.063518  \n",
+       "998  17.802222  16.503543  15.381047  14.424210  \n",
+       "999  16.470867  15.350302  14.396729  13.593420  "
       ]
      },
      "execution_count": 8,
@@ -658,10 +658,10 @@
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-     "shell.execute_reply": "2024-11-04T11:35:22.611328Z"
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@@ -673,7 +673,7 @@
    "outputs": [
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",
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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -699,10 +699,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:22.637780Z",
-     "iopub.status.busy": "2024-11-04T11:35:22.626628Z",
-     "iopub.status.idle": "2024-11-04T11:35:22.705524Z",
-     "shell.execute_reply": "2024-11-04T11:35:22.704605Z"
+     "iopub.execute_input": "2024-11-04T12:31:16.173305Z",
+     "iopub.status.busy": "2024-11-04T12:31:16.172208Z",
+     "iopub.status.idle": "2024-11-04T12:31:16.242469Z",
+     "shell.execute_reply": "2024-11-04T12:31:16.241749Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -757,10 +757,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:22.710216Z",
-     "iopub.status.busy": "2024-11-04T11:35:22.709045Z",
-     "iopub.status.idle": "2024-11-04T11:35:22.774226Z",
-     "shell.execute_reply": "2024-11-04T11:35:22.773344Z"
+     "iopub.execute_input": "2024-11-04T12:31:16.246290Z",
+     "iopub.status.busy": "2024-11-04T12:31:16.245377Z",
+     "iopub.status.idle": "2024-11-04T12:31:16.307188Z",
+     "shell.execute_reply": "2024-11-04T12:31:16.306493Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -814,10 +814,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:22.778929Z",
-     "iopub.status.busy": "2024-11-04T11:35:22.777809Z",
-     "iopub.status.idle": "2024-11-04T11:35:22.847843Z",
-     "shell.execute_reply": "2024-11-04T11:35:22.846993Z"
+     "iopub.execute_input": "2024-11-04T12:31:16.310920Z",
+     "iopub.status.busy": "2024-11-04T12:31:16.309979Z",
+     "iopub.status.idle": "2024-11-04T12:31:16.353220Z",
+     "shell.execute_reply": "2024-11-04T12:31:16.352592Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -833,38 +833,31 @@
      "text": [
       "                          AR-X - GARCH Model Results                          \n",
       "==============================================================================\n",
-      "Dep. Variable:                   data   R-squared:                       0.938\n",
-      "Mean Model:                      AR-X   Adj. R-squared:                  0.937\n",
-      "Vol Model:                      GARCH   Log-Likelihood:               -2349.72\n",
-      "Distribution:                  Normal   AIC:                           4711.43\n",
-      "Method:            Maximum Likelihood   BIC:                           4740.87\n",
+      "Dep. Variable:                   data   R-squared:                       0.948\n",
+      "Mean Model:                      AR-X   Adj. R-squared:                  0.948\n",
+      "Vol Model:                      GARCH   Log-Likelihood:               -2292.38\n",
+      "Distribution:                  Normal   AIC:                           4596.75\n",
+      "Method:            Maximum Likelihood   BIC:                           4626.19\n",
       "                                        No. Observations:                  999\n",
       "Date:                Mon, Nov 04 2024   Df Residuals:                      996\n",
-      "Time:                        11:35:22   Df Model:                            3\n",
+      "Time:                        12:31:16   Df Model:                            3\n",
       "                               Mean Model                               \n",
       "========================================================================\n",
       "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
       "------------------------------------------------------------------------\n",
-      "Const         -6.7136      0.273    -24.608 1.043e-133 [ -7.248, -6.179]\n",
-      "data[1]        0.7515  1.102e-02     68.217      0.000 [  0.730,  0.773]\n",
-      "x0             1.8446  6.348e-02     29.056 1.288e-185 [  1.720,  1.969]\n",
+      "Const         -6.7803      0.298    -22.777 7.717e-115 [ -7.364, -6.197]\n",
+      "data[1]        0.7648  1.025e-02     74.582      0.000 [  0.745,  0.785]\n",
+      "x0             1.7949  6.373e-02     28.166 1.507e-174 [  1.670,  1.920]\n",
       "                             Volatility Model                             \n",
       "==========================================================================\n",
       "                 coef    std err          t      P>|t|    95.0% Conf. Int.\n",
       "--------------------------------------------------------------------------\n",
-      "omega          5.6886      2.414      2.357  1.843e-02   [  0.958, 10.419]\n",
-      "alpha[1]       0.1317  3.924e-02      3.355  7.927e-04 [5.475e-02,  0.209]\n",
-      "beta[1]        0.0000      0.376      0.000      1.000   [ -0.737,  0.737]\n",
+      "omega          2.9062      0.752      3.866  1.108e-04   [  1.433,  4.380]\n",
+      "alpha[1]       0.1131  3.655e-02      3.093  1.979e-03 [4.143e-02,  0.185]\n",
+      "beta[1]        0.3896      0.139      2.810  4.960e-03   [  0.118,  0.661]\n",
       "==========================================================================\n",
       "\n",
-      "Covariance estimator: robust"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
+      "Covariance estimator: robust\n"
      ]
     }
    ],
@@ -887,10 +880,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:22.852648Z",
-     "iopub.status.busy": "2024-11-04T11:35:22.851490Z",
-     "iopub.status.idle": "2024-11-04T11:35:22.924128Z",
-     "shell.execute_reply": "2024-11-04T11:35:22.923132Z"
+     "iopub.execute_input": "2024-11-04T12:31:16.356819Z",
+     "iopub.status.busy": "2024-11-04T12:31:16.355807Z",
+     "iopub.status.idle": "2024-11-04T12:31:16.424069Z",
+     "shell.execute_reply": "2024-11-04T12:31:16.423270Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -938,10 +931,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:22.929277Z",
-     "iopub.status.busy": "2024-11-04T11:35:22.928112Z",
-     "iopub.status.idle": "2024-11-04T11:35:22.940714Z",
-     "shell.execute_reply": "2024-11-04T11:35:22.939843Z"
+     "iopub.execute_input": "2024-11-04T12:31:16.427698Z",
+     "iopub.status.busy": "2024-11-04T12:31:16.427418Z",
+     "iopub.status.idle": "2024-11-04T12:31:16.438171Z",
+     "shell.execute_reply": "2024-11-04T12:31:16.437462Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -954,8 +947,8 @@
     {
      "data": {
       "text/plain": [
-       "array([[0.85155627, 1.09166573, 1.27068516, 1.33525325, 1.34994696,\n",
-       "        1.45983758, 1.33623444, 1.23021183, 1.35506573, 1.50930572]])"
+       "array([[0.92855758, 1.03764756, 1.16284144, 1.33985331, 1.43067754,\n",
+       "        1.32388539, 1.28793327, 1.31390094, 1.42633071, 1.44665818]])"
       ]
      },
      "execution_count": 14,
@@ -984,10 +977,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:22.945491Z",
-     "iopub.status.busy": "2024-11-04T11:35:22.944304Z",
-     "iopub.status.idle": "2024-11-04T11:35:23.162523Z",
-     "shell.execute_reply": "2024-11-04T11:35:23.161472Z"
+     "iopub.execute_input": "2024-11-04T12:31:16.442141Z",
+     "iopub.status.busy": "2024-11-04T12:31:16.441189Z",
+     "iopub.status.idle": "2024-11-04T12:31:16.641007Z",
+     "shell.execute_reply": "2024-11-04T12:31:16.640143Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -1033,10 +1026,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:23.165906Z",
-     "iopub.status.busy": "2024-11-04T11:35:23.165575Z",
-     "iopub.status.idle": "2024-11-04T11:35:23.171654Z",
-     "shell.execute_reply": "2024-11-04T11:35:23.170935Z"
+     "iopub.execute_input": "2024-11-04T12:31:16.645610Z",
+     "iopub.status.busy": "2024-11-04T12:31:16.644571Z",
+     "iopub.status.idle": "2024-11-04T12:31:16.652601Z",
+     "shell.execute_reply": "2024-11-04T12:31:16.651835Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -1049,8 +1042,8 @@
     {
      "data": {
       "text/plain": [
-       "array([[2.95222892, 4.83130147, 6.11237289, 7.39444145, 8.15827407,\n",
-       "        8.60962017, 8.69458872, 8.83858878, 8.90767985, 9.28254572]])"
+       "array([[2.99830288, 4.92854111, 6.48452188, 7.57968677, 8.51120738,\n",
+       "        8.72819469, 8.73398988, 9.06111255, 9.11376726, 9.29242535]])"
       ]
      },
      "execution_count": 16,
@@ -1083,10 +1076,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:23.175221Z",
-     "iopub.status.busy": "2024-11-04T11:35:23.174583Z",
-     "iopub.status.idle": "2024-11-04T11:35:23.234920Z",
-     "shell.execute_reply": "2024-11-04T11:35:23.234096Z"
+     "iopub.execute_input": "2024-11-04T12:31:16.656753Z",
+     "iopub.status.busy": "2024-11-04T12:31:16.655754Z",
+     "iopub.status.idle": "2024-11-04T12:31:16.707804Z",
+     "shell.execute_reply": "2024-11-04T12:31:16.706965Z"
     }
    },
    "outputs": [
@@ -1094,16 +1087,16 @@
      "data": {
       "text/plain": [
        "0            NaN\n",
-       "1      16.574996\n",
-       "2      13.793593\n",
-       "3       9.113307\n",
-       "4      10.637110\n",
+       "1      -4.350539\n",
+       "2      -4.554188\n",
+       "3      -8.151305\n",
+       "4      -8.699591\n",
        "         ...    \n",
-       "995    12.242438\n",
-       "996    16.861830\n",
-       "997    14.810902\n",
-       "998    12.375235\n",
-       "999    10.505238\n",
+       "995    27.033458\n",
+       "996    25.174675\n",
+       "997    23.623694\n",
+       "998    26.342809\n",
+       "999    26.506233\n",
        "Length: 1000, dtype: float64"
       ]
      },
@@ -1123,14 +1116,14 @@
    "metadata": {},
    "source": [
     "### Conditional Mean uses target alignment\n",
-    "When modeling the conditional mean in an AR-X, HAR-X, or LS model, the $X$ data is target-aligned.  This requires that when modeling the mean of ``y[t]``, the correct values of $X$ must appear in ``x[t]``. Mathematically, the $X$ matrix used when estimating a model should have the structure\n",
+    "When modeling the conditional mean in an AR-X, HAR-X, or LS model, the $X$ data is target-aligned.  This requires that when modeling the mean of ``y[t]``, the correct values of $X$ must appear in ``x[t]``. Mathematically, the $X$ matrix used when estimating a model should have the structure (using the Python indexing convention of a T-element data set having indices 0, 1, ..., T-1):\n",
     "\n",
     "$$\n",
     "\\left[\\begin{array}{c}\n",
     "X_{0}\\\\\n",
     "X_{1}\\\\\n",
     "\\vdots\\\\\n",
-    "X_{t-1}\n",
+    "X_{T-1}\n",
     "\\end{array}\\right]\n",
     "$$\n",
     "\n",
@@ -1143,22 +1136,24 @@
     "\n",
     "$$\n",
     "\\left[\\begin{array}{cccc}\n",
-    "E\\left[X_{1|0}\\right] & E\\left[X_{2|0}\\right] & \\ldots & E\\left[X_{h|0}\\right]\\\\\n",
-    "E\\left[X_{2|1}\\right] & E\\left[X_{3|1}\\right] & \\ldots & E\\left[X_{h+1|1}\\right]\\\\\n",
+    "E\\left[X_{1}|\\mathcal{F}_0\\right] & E\\left[X_{2}|\\mathcal{F}_0\\right] & \\ldots & E\\left[X_{h|\\mathcal{F}_0}\\right]\\\\\n",
+    "E\\left[X_{2}|\\mathcal{F}_1\\right] & E\\left[X_{3}|\\mathcal{F}_0\\right] & \\ldots & E\\left[X_{h+1}|\\mathcal{F}_1\\right]\\\\\n",
     "\\vdots & \\vdots & \\vdots & \\vdots\\\\\n",
-    "E\\left[X_{T|T-1}\\right] & E\\left[X_{T+1|T-1}\\right] & \\ldots & E\\left[X_{T+h-1|T-1}\\right]\n",
+    "E\\left[X_{T}|\\mathcal{F}_{T-1}\\right] & E\\left[X_{T+1}|\\mathcal{F}_{T-1}\\right] & \\ldots & E\\left[X_{T+h-1}|\\mathcal{F}_{T-1}\\right]\n",
     "\\end{array}\\right]\n",
     "$$\n",
     "\n",
+    "where $|\\mathcal{F}_{s}$ is the time-$s$ information set.\n",
+    "\n",
     "If you use the same ``x`` value in the model when forecasting, you will see different values due to this alignment difference. Naively using the same ``x`` values ie equivalent to setting\n",
     "\n",
-    "$$ E\\left[X_{j|j-1}] \\right] = X_{j-1} $$\n",
+    "$$ E\\left[X_{s}|\\mathcal{F}_{s-1} \\right] = X_{s-1} $$\n",
     "\n",
     "In general this would not be correct when forecasting, and will always produce forecasts that differ from the conditional mean.  In order to recover the conditional mean using the forecast function, it is necessary to ``shift`` the $X$ values by -1, so that once shifted, the ``x`` values will have the relationship\n",
     "\n",
-    "$$ E\\left[X_{j|j-1}] \\right] = X_{j} .$$\n",
+    "$$ E\\left[X_{s}|\\mathcal{F}_{s-1} \\right] = X_{s} .$$\n",
     "\n",
-    "Here we shift the $X$ data by ``-1`` so ``x[j]`` is treated as being in the information set for ``y[j-1]``. Also note that the final forecast is ``NaN``. Conceptually this must be the case because the value of $X$ at 999 should be ahead of 999 (i.e., at observation 1,000), and we do not have this value. "
+    "Here we shift the $X$ data by ``-1`` so ``x[s]`` is treated as being in the information set for ``y[s-1]``. Also, note that the final forecast is ``NaN``. Conceptually this must be the case because the value of $X$ at 999 should be ahead of 999 (i.e., at observation 1,000), and we do not have this value. "
    ]
   },
   {
@@ -1166,10 +1161,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:23.238941Z",
-     "iopub.status.busy": "2024-11-04T11:35:23.238279Z",
-     "iopub.status.idle": "2024-11-04T11:35:23.254993Z",
-     "shell.execute_reply": "2024-11-04T11:35:23.254147Z"
+     "iopub.execute_input": "2024-11-04T12:31:16.712187Z",
+     "iopub.status.busy": "2024-11-04T12:31:16.711156Z",
+     "iopub.status.idle": "2024-11-04T12:31:16.726307Z",
+     "shell.execute_reply": "2024-11-04T12:31:16.725520Z"
     }
    },
    "outputs": [
@@ -1200,23 +1195,23 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>16.574996</td>\n",
+       "      <td>-4.350539</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>13.793593</td>\n",
+       "      <td>-4.554188</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>9.113307</td>\n",
+       "      <td>-8.151305</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>10.637110</td>\n",
+       "      <td>-8.699591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>4.460672</td>\n",
+       "      <td>-6.732064</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1224,19 +1219,19 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>995</th>\n",
-       "      <td>16.861830</td>\n",
+       "      <td>25.174675</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>996</th>\n",
-       "      <td>14.810902</td>\n",
+       "      <td>23.623694</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>997</th>\n",
-       "      <td>12.375235</td>\n",
+       "      <td>26.342809</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
-       "      <td>10.505238</td>\n",
+       "      <td>26.506233</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
@@ -1249,16 +1244,16 @@
       ],
       "text/plain": [
        "           h.1\n",
-       "0    16.574996\n",
-       "1    13.793593\n",
-       "2     9.113307\n",
-       "3    10.637110\n",
-       "4     4.460672\n",
+       "0    -4.350539\n",
+       "1    -4.554188\n",
+       "2    -8.151305\n",
+       "3    -8.699591\n",
+       "4    -6.732064\n",
        "..         ...\n",
-       "995  16.861830\n",
-       "996  14.810902\n",
-       "997  12.375235\n",
-       "998  10.505238\n",
+       "995  25.174675\n",
+       "996  23.623694\n",
+       "997  26.342809\n",
+       "998  26.506233\n",
        "999        NaN\n",
        "\n",
        "[1000 rows x 1 columns]"
@@ -1291,10 +1286,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:23.259041Z",
-     "iopub.status.busy": "2024-11-04T11:35:23.258372Z",
-     "iopub.status.idle": "2024-11-04T11:35:23.283504Z",
-     "shell.execute_reply": "2024-11-04T11:35:23.282674Z"
+     "iopub.execute_input": "2024-11-04T12:31:16.730559Z",
+     "iopub.status.busy": "2024-11-04T12:31:16.729511Z",
+     "iopub.status.idle": "2024-11-04T12:31:16.762290Z",
+     "shell.execute_reply": "2024-11-04T12:31:16.761260Z"
     }
    },
    "outputs": [
@@ -1325,23 +1320,23 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>19.510179</td>\n",
+       "      <td>-4.184553</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>12.789513</td>\n",
+       "      <td>-4.413541</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>11.858034</td>\n",
+       "      <td>-4.477862</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>7.775498</td>\n",
+       "      <td>-5.679386</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>10.912252</td>\n",
+       "      <td>-9.066428</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1349,19 +1344,19 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>995</th>\n",
-       "      <td>13.939124</td>\n",
+       "      <td>24.556576</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>996</th>\n",
-       "      <td>15.857640</td>\n",
+       "      <td>23.259324</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>997</th>\n",
-       "      <td>13.418830</td>\n",
+       "      <td>22.261996</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
-       "      <td>9.751022</td>\n",
+       "      <td>26.724375</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
@@ -1374,16 +1369,16 @@
       ],
       "text/plain": [
        "           h.1\n",
-       "0    19.510179\n",
-       "1    12.789513\n",
-       "2    11.858034\n",
-       "3     7.775498\n",
-       "4    10.912252\n",
+       "0    -4.184553\n",
+       "1    -4.413541\n",
+       "2    -4.477862\n",
+       "3    -5.679386\n",
+       "4    -9.066428\n",
        "..         ...\n",
-       "995  13.939124\n",
-       "996  15.857640\n",
-       "997  13.418830\n",
-       "998   9.751022\n",
+       "995  24.556576\n",
+       "996  23.259324\n",
+       "997  22.261996\n",
+       "998  26.724375\n",
        "999        NaN\n",
        "\n",
        "[1000 rows x 1 columns]"
@@ -1420,10 +1415,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:23.287298Z",
-     "iopub.status.busy": "2024-11-04T11:35:23.286768Z",
-     "iopub.status.idle": "2024-11-04T11:35:23.352376Z",
-     "shell.execute_reply": "2024-11-04T11:35:23.351558Z"
+     "iopub.execute_input": "2024-11-04T12:31:16.765900Z",
+     "iopub.status.busy": "2024-11-04T12:31:16.765428Z",
+     "iopub.status.idle": "2024-11-04T12:31:16.825898Z",
+     "shell.execute_reply": "2024-11-04T12:31:16.824979Z"
     }
    },
    "outputs": [
@@ -1463,27 +1458,27 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
-       "      <td>10.395148</td>\n",
-       "      <td>9.878159</td>\n",
-       "      <td>9.650317</td>\n",
-       "      <td>9.580907</td>\n",
-       "      <td>9.596394</td>\n",
-       "      <td>9.654862</td>\n",
-       "      <td>9.732314</td>\n",
-       "      <td>9.815125</td>\n",
-       "      <td>9.895773</td>\n",
-       "      <td>9.970364</td>\n",
+       "      <td>26.310775</td>\n",
+       "      <td>24.667118</td>\n",
+       "      <td>22.981429</td>\n",
+       "      <td>21.365651</td>\n",
+       "      <td>19.880924</td>\n",
+       "      <td>18.55521</td>\n",
+       "      <td>17.395624</td>\n",
+       "      <td>16.396835</td>\n",
+       "      <td>15.546659</td>\n",
+       "      <td>14.829674</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "          h.01      h.02      h.03      h.04      h.05      h.06      h.07  \\\n",
-       "999  10.395148  9.878159  9.650317  9.580907  9.596394  9.654862  9.732314   \n",
+       "          h.01       h.02       h.03       h.04       h.05      h.06  \\\n",
+       "999  26.310775  24.667118  22.981429  21.365651  19.880924  18.55521   \n",
        "\n",
-       "         h.08      h.09      h.10  \n",
-       "999  9.815125  9.895773  9.970364  "
+       "          h.07       h.08       h.09       h.10  \n",
+       "999  17.395624  16.396835  15.546659  14.829674  "
       ]
      },
      "execution_count": 20,
diff --git a/devel/univariate/univariate_using_fixed_variance.html b/devel/univariate/univariate_using_fixed_variance.html
index f330e0e547..de9a91f016 100644
--- a/devel/univariate/univariate_using_fixed_variance.html
+++ b/devel/univariate/univariate_using_fixed_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Using the Fixed Variance process - arch v7.1.1 (+16)</title>
+        <title>Using the Fixed Variance process - arch v7.1.1 (+18)</title>
       
     
 
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
@@ -850,7 +850,7 @@ <h3 id="Initial-Mean-Model-Estimation">Initial Mean Model Estimation<a class="he
 Method:            Maximum Likelihood   BIC:                           4571.50
                                         No. Observations:                 1237
 Date:                Mon, Nov 04 2024   Df Residuals:                     1233
-Time:                        11:37:09   Df Model:                            4
+Time:                        12:33:04   Df Model:                            4
                                    Mean Model
 ================================================================================
                       coef    std err          t      P&gt;|t|     95.0% Conf. Int.
@@ -895,7 +895,7 @@ <h3 id="Initial-Volatility-Model-Estimation">Initial Volatility Model Estimation
 Method:            Maximum Likelihood   BIC:                           3902.35
                                         No. Observations:                 1237
 Date:                Mon, Nov 04 2024   Df Residuals:                     1237
-Time:                        11:37:09   Df Model:                            0
+Time:                        12:33:04   Df Model:                            0
                               Volatility Model
 ===========================================================================
                  coef    std err          t      P&gt;|t|     95.0% Conf. Int.
@@ -968,7 +968,7 @@ <h3 id="Re-estimating-the-mean-with-a-FixedVariance">Re-estimating the mean with
 Method:            Maximum Likelihood   BIC:                           3907.50
                                         No. Observations:                 1237
 Date:                Mon, Nov 04 2024   Df Residuals:                     1233
-Time:                        11:37:10   Df Model:                            4
+Time:                        12:33:05   Df Model:                            4
                                     Mean Model
 ==================================================================================
                       coef    std err          t      P&gt;|t|       95.0% Conf. Int.
@@ -1024,7 +1024,7 @@ <h3 id="Zig-Zag-estimation">Zig-Zag estimation<a class="headerlink" href="#Zig-Z
 Method:                     Maximum Likelihood   BIC:                           3899.96
                                                  No. Observations:                 1237
 Date:                         Mon, Nov 04 2024   Df Residuals:                     1233
-Time:                                 11:37:10   Df Model:                            4
+Time:                                 12:33:05   Df Model:                            4
                                     Mean Model
 =================================================================================
                       coef    std err          t      P&gt;|t|      95.0% Conf. Int.
@@ -1064,7 +1064,7 @@ <h3 id="Direct-Estimation">Direct Estimation<a class="headerlink" href="#Direct-
 Method:            Maximum Likelihood   BIC:                           3922.19
                                         No. Observations:                 1237
 Date:                Mon, Nov 04 2024   Df Residuals:                     1233
-Time:                        11:37:10   Df Model:                            4
+Time:                        12:33:05   Df Model:                            4
                                    Mean Model
 ================================================================================
                       coef    std err          t      P&gt;|t|     95.0% Conf. Int.
diff --git a/devel/univariate/univariate_using_fixed_variance.ipynb b/devel/univariate/univariate_using_fixed_variance.ipynb
index 70bc681d2c..cd74246c8d 100644
--- a/devel/univariate/univariate_using_fixed_variance.ipynb
+++ b/devel/univariate/univariate_using_fixed_variance.ipynb
@@ -23,10 +23,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:26.505557Z",
-     "iopub.status.busy": "2024-11-04T11:35:26.505287Z",
-     "iopub.status.idle": "2024-11-04T11:35:28.097536Z",
-     "shell.execute_reply": "2024-11-04T11:35:28.096612Z"
+     "iopub.execute_input": "2024-11-04T12:31:20.056428Z",
+     "iopub.status.busy": "2024-11-04T12:31:20.056156Z",
+     "iopub.status.idle": "2024-11-04T12:31:21.618460Z",
+     "shell.execute_reply": "2024-11-04T12:31:21.617574Z"
     }
    },
    "outputs": [],
@@ -55,10 +55,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:28.101910Z",
-     "iopub.status.busy": "2024-11-04T11:35:28.100685Z",
-     "iopub.status.idle": "2024-11-04T11:35:28.105633Z",
-     "shell.execute_reply": "2024-11-04T11:35:28.104852Z"
+     "iopub.execute_input": "2024-11-04T12:31:21.623309Z",
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+     "iopub.status.idle": "2024-11-04T12:31:21.627101Z",
+     "shell.execute_reply": "2024-11-04T12:31:21.626324Z"
     }
    },
    "outputs": [],
@@ -79,10 +79,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:28.109723Z",
-     "iopub.status.busy": "2024-11-04T11:35:28.108676Z",
-     "iopub.status.idle": "2024-11-04T11:35:28.910078Z",
-     "shell.execute_reply": "2024-11-04T11:35:28.909295Z"
+     "iopub.execute_input": "2024-11-04T12:31:21.631182Z",
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+     "iopub.status.idle": "2024-11-04T12:31:22.433856Z",
+     "shell.execute_reply": "2024-11-04T12:31:22.432907Z"
     }
    },
    "outputs": [
@@ -119,10 +119,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:28.913468Z",
-     "iopub.status.busy": "2024-11-04T11:35:28.913146Z",
-     "iopub.status.idle": "2024-11-04T11:35:28.933287Z",
-     "shell.execute_reply": "2024-11-04T11:35:28.932392Z"
+     "iopub.execute_input": "2024-11-04T12:31:22.438102Z",
+     "iopub.status.busy": "2024-11-04T12:31:22.437732Z",
+     "iopub.status.idle": "2024-11-04T12:31:22.460082Z",
+     "shell.execute_reply": "2024-11-04T12:31:22.459110Z"
     }
    },
    "outputs": [
@@ -139,7 +139,7 @@
       "Method:            Maximum Likelihood   BIC:                           4571.50\n",
       "                                        No. Observations:                 1237\n",
       "Date:                Mon, Nov 04 2024   Df Residuals:                     1233\n",
-      "Time:                        11:35:28   Df Model:                            4\n",
+      "Time:                        12:31:22   Df Model:                            4\n",
       "                                   Mean Model                                   \n",
       "================================================================================\n",
       "                      coef    std err          t      P>|t|     95.0% Conf. Int.\n",
@@ -181,10 +181,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:28.938146Z",
-     "iopub.status.busy": "2024-11-04T11:35:28.936977Z",
-     "iopub.status.idle": "2024-11-04T11:35:29.002073Z",
-     "shell.execute_reply": "2024-11-04T11:35:29.001152Z"
+     "iopub.execute_input": "2024-11-04T12:31:22.465497Z",
+     "iopub.status.busy": "2024-11-04T12:31:22.464254Z",
+     "iopub.status.idle": "2024-11-04T12:31:22.530695Z",
+     "shell.execute_reply": "2024-11-04T12:31:22.529736Z"
     }
    },
    "outputs": [
@@ -201,7 +201,7 @@
       "Method:            Maximum Likelihood   BIC:                           3902.35\n",
       "                                        No. Observations:                 1237\n",
       "Date:                Mon, Nov 04 2024   Df Residuals:                     1237\n",
-      "Time:                        11:35:28   Df Model:                            0\n",
+      "Time:                        12:31:22   Df Model:                            0\n",
       "                              Volatility Model                             \n",
       "===========================================================================\n",
       "                 coef    std err          t      P>|t|     95.0% Conf. Int.\n",
@@ -227,10 +227,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:29.006967Z",
-     "iopub.status.busy": "2024-11-04T11:35:29.005787Z",
-     "iopub.status.idle": "2024-11-04T11:35:30.067409Z",
-     "shell.execute_reply": "2024-11-04T11:35:30.066620Z"
+     "iopub.execute_input": "2024-11-04T12:31:22.535664Z",
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+     "iopub.status.idle": "2024-11-04T12:31:23.617351Z",
+     "shell.execute_reply": "2024-11-04T12:31:23.616415Z"
     }
    },
    "outputs": [
@@ -265,10 +265,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:30.070052Z",
-     "iopub.status.busy": "2024-11-04T11:35:30.069598Z",
-     "iopub.status.idle": "2024-11-04T11:35:30.104566Z",
-     "shell.execute_reply": "2024-11-04T11:35:30.103809Z"
+     "iopub.execute_input": "2024-11-04T12:31:23.622381Z",
+     "iopub.status.busy": "2024-11-04T12:31:23.621202Z",
+     "iopub.status.idle": "2024-11-04T12:31:23.661008Z",
+     "shell.execute_reply": "2024-11-04T12:31:23.660096Z"
     }
    },
    "outputs": [
@@ -298,7 +298,7 @@
       "Method:            Maximum Likelihood   BIC:                           3907.50\n",
       "                                        No. Observations:                 1237\n",
       "Date:                Mon, Nov 04 2024   Df Residuals:                     1233\n",
-      "Time:                        11:35:30   Df Model:                            4\n",
+      "Time:                        12:31:23   Df Model:                            4\n",
       "                                    Mean Model                                    \n",
       "==================================================================================\n",
       "                      coef    std err          t      P>|t|       95.0% Conf. Int.\n",
@@ -341,10 +341,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:30.108772Z",
-     "iopub.status.busy": "2024-11-04T11:35:30.107749Z",
-     "iopub.status.idle": "2024-11-04T11:35:30.352833Z",
-     "shell.execute_reply": "2024-11-04T11:35:30.352047Z"
+     "iopub.execute_input": "2024-11-04T12:31:23.665805Z",
+     "iopub.status.busy": "2024-11-04T12:31:23.664663Z",
+     "iopub.status.idle": "2024-11-04T12:31:23.979505Z",
+     "shell.execute_reply": "2024-11-04T12:31:23.978579Z"
     }
    },
    "outputs": [
@@ -354,21 +354,21 @@
      "text": [
       "0\n",
       "1\n",
-      "2\n",
-      "3\n"
+      "2\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4\n"
+      "3\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "4\n",
       "                    HAR - Fixed Variance (Unit Scale) Model Results                    \n",
       "=======================================================================================\n",
       "Dep. Variable:                       VIX Index   R-squared:                       0.876\n",
@@ -378,7 +378,7 @@
       "Method:                     Maximum Likelihood   BIC:                           3899.96\n",
       "                                                 No. Observations:                 1237\n",
       "Date:                         Mon, Nov 04 2024   Df Residuals:                     1233\n",
-      "Time:                                 11:35:30   Df Model:                            4\n",
+      "Time:                                 12:31:23   Df Model:                            4\n",
       "                                    Mean Model                                   \n",
       "=================================================================================\n",
       "                      coef    std err          t      P>|t|      95.0% Conf. Int.\n",
@@ -419,10 +419,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:30.356627Z",
-     "iopub.status.busy": "2024-11-04T11:35:30.355869Z",
-     "iopub.status.idle": "2024-11-04T11:35:30.462370Z",
-     "shell.execute_reply": "2024-11-04T11:35:30.461457Z"
+     "iopub.execute_input": "2024-11-04T12:31:23.984605Z",
+     "iopub.status.busy": "2024-11-04T12:31:23.983305Z",
+     "iopub.status.idle": "2024-11-04T12:31:24.091979Z",
+     "shell.execute_reply": "2024-11-04T12:31:24.091067Z"
     },
     "tags": []
    },
@@ -440,7 +440,7 @@
       "Method:            Maximum Likelihood   BIC:                           3922.19\n",
       "                                        No. Observations:                 1237\n",
       "Date:                Mon, Nov 04 2024   Df Residuals:                     1233\n",
-      "Time:                        11:35:30   Df Model:                            4\n",
+      "Time:                        12:31:24   Df Model:                            4\n",
       "                                   Mean Model                                   \n",
       "================================================================================\n",
       "                      coef    std err          t      P>|t|     95.0% Conf. Int.\n",
diff --git a/devel/univariate/univariate_volatility_forecasting.html b/devel/univariate/univariate_volatility_forecasting.html
index 5b3a760197..58d3029323 100644
--- a/devel/univariate/univariate_volatility_forecasting.html
+++ b/devel/univariate/univariate_volatility_forecasting.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Volatility Forecasting - arch v7.1.1 (+16)</title>
+        <title>Volatility Forecasting - arch v7.1.1 (+18)</title>
       
     
 
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/univariate_volatility_forecasting.ipynb b/devel/univariate/univariate_volatility_forecasting.ipynb
index 30a19a5333..37201c0c38 100644
--- a/devel/univariate/univariate_volatility_forecasting.ipynb
+++ b/devel/univariate/univariate_volatility_forecasting.ipynb
@@ -19,10 +19,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:33.110765Z",
-     "iopub.status.busy": "2024-11-04T11:35:33.110459Z",
-     "iopub.status.idle": "2024-11-04T11:35:34.759958Z",
-     "shell.execute_reply": "2024-11-04T11:35:34.759034Z"
+     "iopub.execute_input": "2024-11-04T12:31:26.697765Z",
+     "iopub.status.busy": "2024-11-04T12:31:26.697443Z",
+     "iopub.status.idle": "2024-11-04T12:31:28.496785Z",
+     "shell.execute_reply": "2024-11-04T12:31:28.495824Z"
     }
    },
    "outputs": [],
@@ -50,10 +50,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:34.764282Z",
-     "iopub.status.busy": "2024-11-04T11:35:34.763136Z",
-     "iopub.status.idle": "2024-11-04T11:35:35.103695Z",
-     "shell.execute_reply": "2024-11-04T11:35:35.102809Z"
+     "iopub.execute_input": "2024-11-04T12:31:28.502206Z",
+     "iopub.status.busy": "2024-11-04T12:31:28.500899Z",
+     "iopub.status.idle": "2024-11-04T12:31:28.878498Z",
+     "shell.execute_reply": "2024-11-04T12:31:28.877461Z"
     }
    },
    "outputs": [],
@@ -92,10 +92,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2024-11-04T11:35:35.107777Z",
-     "iopub.status.idle": "2024-11-04T11:35:35.180260Z",
-     "shell.execute_reply": "2024-11-04T11:35:35.179555Z"
+     "iopub.execute_input": "2024-11-04T12:31:28.883803Z",
+     "iopub.status.busy": "2024-11-04T12:31:28.882541Z",
+     "iopub.status.idle": "2024-11-04T12:31:28.971789Z",
+     "shell.execute_reply": "2024-11-04T12:31:28.970649Z"
     }
    },
    "outputs": [
@@ -123,10 +123,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:35.184090Z",
-     "iopub.status.busy": "2024-11-04T11:35:35.183154Z",
-     "iopub.status.idle": "2024-11-04T11:35:35.189224Z",
-     "shell.execute_reply": "2024-11-04T11:35:35.188523Z"
+     "iopub.execute_input": "2024-11-04T12:31:28.977476Z",
+     "iopub.status.busy": "2024-11-04T12:31:28.976158Z",
+     "iopub.status.idle": "2024-11-04T12:31:28.983869Z",
+     "shell.execute_reply": "2024-11-04T12:31:28.982956Z"
     }
    },
    "outputs": [],
@@ -155,10 +155,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:35.193230Z",
-     "iopub.status.busy": "2024-11-04T11:35:35.192305Z",
-     "iopub.status.idle": "2024-11-04T11:35:35.202158Z",
-     "shell.execute_reply": "2024-11-04T11:35:35.201521Z"
+     "iopub.execute_input": "2024-11-04T12:31:28.989191Z",
+     "iopub.status.busy": "2024-11-04T12:31:28.987950Z",
+     "iopub.status.idle": "2024-11-04T12:31:29.001434Z",
+     "shell.execute_reply": "2024-11-04T12:31:29.000522Z"
     }
    },
    "outputs": [
@@ -196,10 +196,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:35.205840Z",
-     "iopub.status.busy": "2024-11-04T11:35:35.204910Z",
-     "iopub.status.idle": "2024-11-04T11:35:35.214152Z",
-     "shell.execute_reply": "2024-11-04T11:35:35.213520Z"
+     "iopub.execute_input": "2024-11-04T12:31:29.006487Z",
+     "iopub.status.busy": "2024-11-04T12:31:29.005255Z",
+     "iopub.status.idle": "2024-11-04T12:31:29.017808Z",
+     "shell.execute_reply": "2024-11-04T12:31:29.016873Z"
     }
    },
    "outputs": [
@@ -241,10 +241,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:35.217643Z",
-     "iopub.status.busy": "2024-11-04T11:35:35.216804Z",
-     "iopub.status.idle": "2024-11-04T11:35:35.377627Z",
-     "shell.execute_reply": "2024-11-04T11:35:35.376758Z"
+     "iopub.execute_input": "2024-11-04T12:31:29.022975Z",
+     "iopub.status.busy": "2024-11-04T12:31:29.021766Z",
+     "iopub.status.idle": "2024-11-04T12:31:29.202354Z",
+     "shell.execute_reply": "2024-11-04T12:31:29.201423Z"
     }
    },
    "outputs": [
@@ -271,7 +271,14 @@
       "2011-01-03  0.451724  0.460381  0.468976  0.477512  0.485987\n",
       "2011-01-04  0.428416  0.437236  0.445994  0.454691  0.463326\n",
       "2011-01-05  0.420554  0.429429  0.438242  0.446993  0.455683\n",
-      "2011-01-06  0.402483  0.411486  0.420425  0.429301  0.438115\n"
+      "2011-01-06  0.402483  0.411486  0.420425  0.429301  0.438115"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
      ]
     }
    ],
@@ -295,10 +302,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:35.382077Z",
-     "iopub.status.busy": "2024-11-04T11:35:35.380990Z",
-     "iopub.status.idle": "2024-11-04T11:35:37.876600Z",
-     "shell.execute_reply": "2024-11-04T11:35:37.875777Z"
+     "iopub.execute_input": "2024-11-04T12:31:29.207476Z",
+     "iopub.status.busy": "2024-11-04T12:31:29.206217Z",
+     "iopub.status.idle": "2024-11-04T12:31:31.758662Z",
+     "shell.execute_reply": "2024-11-04T12:31:31.757818Z"
     }
    },
    "outputs": [
@@ -501,10 +508,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:37.881145Z",
-     "iopub.status.busy": "2024-11-04T11:35:37.880072Z",
-     "iopub.status.idle": "2024-11-04T11:35:40.043175Z",
-     "shell.execute_reply": "2024-11-04T11:35:40.042493Z"
+     "iopub.execute_input": "2024-11-04T12:31:31.763004Z",
+     "iopub.status.busy": "2024-11-04T12:31:31.761937Z",
+     "iopub.status.idle": "2024-11-04T12:31:34.251500Z",
+     "shell.execute_reply": "2024-11-04T12:31:34.250640Z"
     }
    },
    "outputs": [
@@ -717,10 +724,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:40.045946Z",
-     "iopub.status.busy": "2024-11-04T11:35:40.045450Z",
-     "iopub.status.idle": "2024-11-04T11:35:40.103842Z",
-     "shell.execute_reply": "2024-11-04T11:35:40.103045Z"
+     "iopub.execute_input": "2024-11-04T12:31:34.256334Z",
+     "iopub.status.busy": "2024-11-04T12:31:34.255100Z",
+     "iopub.status.idle": "2024-11-04T12:31:34.330250Z",
+     "shell.execute_reply": "2024-11-04T12:31:34.329460Z"
     }
    },
    "outputs": [
@@ -734,7 +741,13 @@
       "            Current function value: 6822.882823437632\n",
       "            Iterations: 13\n",
       "            Function evaluations: 93\n",
-      "            Gradient evaluations: 13\n",
+      "            Gradient evaluations: 13\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "h.1    3.010188\n",
       "Name: 2018-12-31 00:00:00, dtype: float64\n"
      ]
@@ -762,10 +775,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:40.108526Z",
-     "iopub.status.busy": "2024-11-04T11:35:40.107022Z",
-     "iopub.status.idle": "2024-11-04T11:35:40.678854Z",
-     "shell.execute_reply": "2024-11-04T11:35:40.678039Z"
+     "iopub.execute_input": "2024-11-04T12:31:34.334796Z",
+     "iopub.status.busy": "2024-11-04T12:31:34.333731Z",
+     "iopub.status.idle": "2024-11-04T12:31:34.958989Z",
+     "shell.execute_reply": "2024-11-04T12:31:34.956503Z"
     }
    },
    "outputs": [
@@ -804,16 +817,16 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:40.682285Z",
-     "iopub.status.busy": "2024-11-04T11:35:40.681983Z",
-     "iopub.status.idle": "2024-11-04T11:35:41.165534Z",
-     "shell.execute_reply": "2024-11-04T11:35:41.164762Z"
+     "iopub.execute_input": "2024-11-04T12:31:34.962098Z",
+     "iopub.status.busy": "2024-11-04T12:31:34.961744Z",
+     "iopub.status.idle": "2024-11-04T12:31:35.690485Z",
+     "shell.execute_reply": "2024-11-04T12:31:35.689486Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -850,16 +863,16 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:41.168522Z",
-     "iopub.status.busy": "2024-11-04T11:35:41.168106Z",
-     "iopub.status.idle": "2024-11-04T11:35:41.922417Z",
-     "shell.execute_reply": "2024-11-04T11:35:41.921493Z"
+     "iopub.execute_input": "2024-11-04T12:31:35.694779Z",
+     "iopub.status.busy": "2024-11-04T12:31:35.694342Z",
+     "iopub.status.idle": "2024-11-04T12:31:36.518861Z",
+     "shell.execute_reply": "2024-11-04T12:31:36.517889Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -900,10 +913,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:41.925881Z",
-     "iopub.status.busy": "2024-11-04T11:35:41.925573Z",
-     "iopub.status.idle": "2024-11-04T11:35:42.038125Z",
-     "shell.execute_reply": "2024-11-04T11:35:42.037354Z"
+     "iopub.execute_input": "2024-11-04T12:31:36.523890Z",
+     "iopub.status.busy": "2024-11-04T12:31:36.522711Z",
+     "iopub.status.idle": "2024-11-04T12:31:36.648631Z",
+     "shell.execute_reply": "2024-11-04T12:31:36.647515Z"
     }
    },
    "outputs": [],
@@ -927,10 +940,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:42.042506Z",
-     "iopub.status.busy": "2024-11-04T11:35:42.041239Z",
-     "iopub.status.idle": "2024-11-04T11:35:42.055540Z",
-     "shell.execute_reply": "2024-11-04T11:35:42.054738Z"
+     "iopub.execute_input": "2024-11-04T12:31:36.653864Z",
+     "iopub.status.busy": "2024-11-04T12:31:36.652675Z",
+     "iopub.status.idle": "2024-11-04T12:31:36.668955Z",
+     "shell.execute_reply": "2024-11-04T12:31:36.667902Z"
     }
    },
    "outputs": [
@@ -962,10 +975,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:42.059814Z",
-     "iopub.status.busy": "2024-11-04T11:35:42.058804Z",
-     "iopub.status.idle": "2024-11-04T11:35:42.746826Z",
-     "shell.execute_reply": "2024-11-04T11:35:42.746090Z"
+     "iopub.execute_input": "2024-11-04T12:31:36.673872Z",
+     "iopub.status.busy": "2024-11-04T12:31:36.672697Z",
+     "iopub.status.idle": "2024-11-04T12:31:37.405523Z",
+     "shell.execute_reply": "2024-11-04T12:31:37.404563Z"
     }
    },
    "outputs": [
@@ -1029,10 +1042,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:42.751118Z",
-     "iopub.status.busy": "2024-11-04T11:35:42.750101Z",
-     "iopub.status.idle": "2024-11-04T11:35:42.762610Z",
-     "shell.execute_reply": "2024-11-04T11:35:42.761666Z"
+     "iopub.execute_input": "2024-11-04T12:31:37.410711Z",
+     "iopub.status.busy": "2024-11-04T12:31:37.409482Z",
+     "iopub.status.idle": "2024-11-04T12:31:37.421992Z",
+     "shell.execute_reply": "2024-11-04T12:31:37.421119Z"
     }
    },
    "outputs": [
@@ -1058,10 +1071,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:42.767519Z",
-     "iopub.status.busy": "2024-11-04T11:35:42.766275Z",
-     "iopub.status.idle": "2024-11-04T11:35:43.400079Z",
-     "shell.execute_reply": "2024-11-04T11:35:43.399216Z"
+     "iopub.execute_input": "2024-11-04T12:31:37.426613Z",
+     "iopub.status.busy": "2024-11-04T12:31:37.425453Z",
+     "iopub.status.idle": "2024-11-04T12:31:38.159515Z",
+     "shell.execute_reply": "2024-11-04T12:31:38.158490Z"
     }
    },
    "outputs": [
diff --git a/devel/univariate/univariate_volatility_modeling.html b/devel/univariate/univariate_volatility_modeling.html
index 91232361e5..87bde4d71e 100644
--- a/devel/univariate/univariate_volatility_modeling.html
+++ b/devel/univariate/univariate_volatility_modeling.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>ARCH Modeling - arch v7.1.1 (+16)</title>
+        <title>ARCH Modeling - arch v7.1.1 (+18)</title>
       
     
 
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
@@ -955,7 +955,7 @@ <h3 id="GARCH-(with-a-Constant-Mean)">GARCH (with a Constant Mean)<a class="head
 Method:            Maximum Likelihood   BIC:                           13907.5
                                         No. Observations:                 5030
 Date:                Mon, Nov 04 2024   Df Residuals:                     5029
-Time:                        11:37:16   Df Model:                            1
+Time:                        12:33:10   Df Model:                            1
                                  Mean Model
 ============================================================================
                  coef    std err          t      P&gt;|t|      95.0% Conf. Int.
@@ -1019,7 +1019,7 @@ <h3 id="GJR-GARCH">GJR-GARCH<a class="headerlink" href="#GJR-GARCH" title="Link
 Method:            Maximum Likelihood   BIC:                           13688.4
                                         No. Observations:                 5030
 Date:                Mon, Nov 04 2024   Df Residuals:                     5029
-Time:                        11:37:17   Df Model:                            1
+Time:                        12:33:12   Df Model:                            1
                                   Mean Model
 =============================================================================
                  coef    std err          t      P&gt;|t|       95.0% Conf. Int.
@@ -1079,7 +1079,7 @@ <h3 id="TARCH/ZARCH">TARCH/ZARCH<a class="headerlink" href="#TARCH/ZARCH" title=
 Method:            Maximum Likelihood   BIC:                           13641.0
                                         No. Observations:                 5030
 Date:                Mon, Nov 04 2024   Df Residuals:                     5029
-Time:                        11:37:17   Df Model:                            1
+Time:                        12:33:12   Df Model:                            1
                                   Mean Model
 =============================================================================
                  coef    std err          t      P&gt;|t|       95.0% Conf. Int.
@@ -1132,7 +1132,7 @@ <h3 id="Student's-T-Errors">Student’s T Errors<a class="headerlink" href="#Stu
 Method:                  Maximum Likelihood   BIC:                           13495.4
                                               No. Observations:                 5030
 Date:                      Mon, Nov 04 2024   Df Residuals:                     5029
-Time:                              11:37:17   Df Model:                            1
+Time:                              12:33:12   Df Model:                            1
                                  Mean Model
 ============================================================================
                  coef    std err          t      P&gt;|t|      95.0% Conf. Int.
@@ -1182,7 +1182,7 @@ <h2 id="Fixing-Parameters">Fixing Parameters<a class="headerlink" href="#Fixing-
 Method:            User-specified Parameters   BIC:                           13869.0
                                                No. Observations:                 5030
 Date:                       Mon, Nov 04 2024
-Time:                               11:37:17
+Time:                               12:33:12
       Mean Model
 =====================
                  coef
@@ -1319,7 +1319,7 @@ <h3 id="Mean-Models">Mean Models<a class="headerlink" href="#Mean-Models" title=
 Method:            Maximum Likelihood   BIC:                           6632.57
                                         No. Observations:                  719
 Date:                Mon, Nov 04 2024   Df Residuals:                      715
-Time:                        11:37:18   Df Model:                            4
+Time:                        12:33:13   Df Model:                            4
                                    Mean Model
 ===============================================================================
                    coef    std err          t      P&gt;|t|       95.0% Conf. Int.
@@ -1366,7 +1366,7 @@ <h3 id="Volatility-Processes">Volatility Processes<a class="headerlink" href="#V
 Method:            Maximum Likelihood   BIC:                           6414.97
                                         No. Observations:                  719
 Date:                Mon, Nov 04 2024   Df Residuals:                      715
-Time:                        11:37:18   Df Model:                            4
+Time:                        12:33:13   Df Model:                            4
                                    Mean Model
 ===============================================================================
                    coef    std err          t      P&gt;|t|       95.0% Conf. Int.
@@ -1435,7 +1435,7 @@ <h3 id="Distributions">Distributions<a class="headerlink" href="#Distributions"
 Method:                  Maximum Likelihood   BIC:                           6408.86
                                               No. Observations:                  719
 Date:                      Mon, Nov 04 2024   Df Residuals:                      715
-Time:                              11:37:19   Df Model:                            4
+Time:                              12:33:15   Df Model:                            4
                                    Mean Model
 ===============================================================================
                    coef    std err          t      P&gt;|t|       95.0% Conf. Int.
@@ -1726,33 +1726,33 @@ <h2 id="Simulation">Simulation<a class="headerlink" href="#Simulation" title="Li
   <tbody>
     <tr>
       <th>0</th>
-      <td>-0.303725</td>
-      <td>1.377338</td>
-      <td>-0.333090</td>
+      <td>-0.478716</td>
+      <td>4.196719</td>
+      <td>-0.508081</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>0.457676</td>
-      <td>1.349071</td>
-      <td>0.428311</td>
+      <td>-10.406313</td>
+      <td>4.057987</td>
+      <td>-10.435678</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>0.864983</td>
-      <td>1.321905</td>
-      <td>0.835618</td>
+      <td>-3.046437</td>
+      <td>4.913354</td>
+      <td>-3.075802</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>-0.517549</td>
-      <td>1.304870</td>
-      <td>-0.546914</td>
+      <td>2.398336</td>
+      <td>4.825715</td>
+      <td>2.368971</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>0.122408</td>
-      <td>1.286124</td>
-      <td>0.093042</td>
+      <td>2.117323</td>
+      <td>4.688330</td>
+      <td>2.087958</td>
     </tr>
   </tbody>
 </table>
diff --git a/devel/univariate/univariate_volatility_modeling.ipynb b/devel/univariate/univariate_volatility_modeling.ipynb
index 0fcca934de..ac3d7bfd00 100644
--- a/devel/univariate/univariate_volatility_modeling.ipynb
+++ b/devel/univariate/univariate_volatility_modeling.ipynb
@@ -19,10 +19,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:47.368997Z",
-     "iopub.status.busy": "2024-11-04T11:35:47.368776Z",
-     "iopub.status.idle": "2024-11-04T11:35:49.482309Z",
-     "shell.execute_reply": "2024-11-04T11:35:49.481436Z"
+     "iopub.execute_input": "2024-11-04T12:31:41.929808Z",
+     "iopub.status.busy": "2024-11-04T12:31:41.929511Z",
+     "iopub.status.idle": "2024-11-04T12:31:43.761755Z",
+     "shell.execute_reply": "2024-11-04T12:31:43.760718Z"
     },
     "pycharm": {
      "is_executing": false
@@ -59,10 +59,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:49.486142Z",
-     "iopub.status.busy": "2024-11-04T11:35:49.485253Z",
-     "iopub.status.idle": "2024-11-04T11:35:50.399053Z",
-     "shell.execute_reply": "2024-11-04T11:35:50.398260Z"
+     "iopub.execute_input": "2024-11-04T12:31:43.766899Z",
+     "iopub.status.busy": "2024-11-04T12:31:43.765498Z",
+     "iopub.status.idle": "2024-11-04T12:31:44.832227Z",
+     "shell.execute_reply": "2024-11-04T12:31:44.831287Z"
     },
     "pycharm": {
      "is_executing": false
@@ -137,10 +137,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:50.402737Z",
-     "iopub.status.busy": "2024-11-04T11:35:50.402405Z",
-     "iopub.status.idle": "2024-11-04T11:35:50.476300Z",
-     "shell.execute_reply": "2024-11-04T11:35:50.475374Z"
+     "iopub.execute_input": "2024-11-04T12:31:44.836567Z",
+     "iopub.status.busy": "2024-11-04T12:31:44.836211Z",
+     "iopub.status.idle": "2024-11-04T12:31:44.912376Z",
+     "shell.execute_reply": "2024-11-04T12:31:44.911466Z"
     },
     "pycharm": {
      "is_executing": false
@@ -167,7 +167,7 @@
       "Method:            Maximum Likelihood   BIC:                           13907.5\n",
       "                                        No. Observations:                 5030\n",
       "Date:                Mon, Nov 04 2024   Df Residuals:                     5029\n",
-      "Time:                        11:35:50   Df Model:                            1\n",
+      "Time:                        12:31:44   Df Model:                            1\n",
       "                                 Mean Model                                 \n",
       "============================================================================\n",
       "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
@@ -206,10 +206,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:50.481306Z",
-     "iopub.status.busy": "2024-11-04T11:35:50.480120Z",
-     "iopub.status.idle": "2024-11-04T11:35:51.469482Z",
-     "shell.execute_reply": "2024-11-04T11:35:51.468555Z"
+     "iopub.execute_input": "2024-11-04T12:31:44.917303Z",
+     "iopub.status.busy": "2024-11-04T12:31:44.916140Z",
+     "iopub.status.idle": "2024-11-04T12:31:45.944230Z",
+     "shell.execute_reply": "2024-11-04T12:31:45.943251Z"
     },
     "pycharm": {
      "is_executing": false
@@ -258,10 +258,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:51.474331Z",
-     "iopub.status.busy": "2024-11-04T11:35:51.473168Z",
-     "iopub.status.idle": "2024-11-04T11:35:51.569674Z",
-     "shell.execute_reply": "2024-11-04T11:35:51.568747Z"
+     "iopub.execute_input": "2024-11-04T12:31:45.949523Z",
+     "iopub.status.busy": "2024-11-04T12:31:45.948302Z",
+     "iopub.status.idle": "2024-11-04T12:31:46.046338Z",
+     "shell.execute_reply": "2024-11-04T12:31:46.045415Z"
     },
     "pycharm": {
      "is_executing": false
@@ -281,7 +281,7 @@
       "Method:            Maximum Likelihood   BIC:                           13688.4\n",
       "                                        No. Observations:                 5030\n",
       "Date:                Mon, Nov 04 2024   Df Residuals:                     5029\n",
-      "Time:                        11:35:51   Df Model:                            1\n",
+      "Time:                        12:31:46   Df Model:                            1\n",
       "                                  Mean Model                                 \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
@@ -342,10 +342,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:51.574630Z",
-     "iopub.status.busy": "2024-11-04T11:35:51.573462Z",
-     "iopub.status.idle": "2024-11-04T11:35:51.669022Z",
-     "shell.execute_reply": "2024-11-04T11:35:51.668115Z"
+     "iopub.execute_input": "2024-11-04T12:31:46.051486Z",
+     "iopub.status.busy": "2024-11-04T12:31:46.050168Z",
+     "iopub.status.idle": "2024-11-04T12:31:46.150912Z",
+     "shell.execute_reply": "2024-11-04T12:31:46.149875Z"
     },
     "pycharm": {
      "is_executing": false
@@ -372,7 +372,7 @@
       "Method:            Maximum Likelihood   BIC:                           13641.0\n",
       "                                        No. Observations:                 5030\n",
       "Date:                Mon, Nov 04 2024   Df Residuals:                     5029\n",
-      "Time:                        11:35:51   Df Model:                            1\n",
+      "Time:                        12:31:46   Df Model:                            1\n",
       "                                  Mean Model                                 \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
@@ -419,10 +419,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:51.674127Z",
-     "iopub.status.busy": "2024-11-04T11:35:51.672909Z",
-     "iopub.status.idle": "2024-11-04T11:35:51.809680Z",
-     "shell.execute_reply": "2024-11-04T11:35:51.808738Z"
+     "iopub.execute_input": "2024-11-04T12:31:46.156276Z",
+     "iopub.status.busy": "2024-11-04T12:31:46.155003Z",
+     "iopub.status.idle": "2024-11-04T12:31:46.297366Z",
+     "shell.execute_reply": "2024-11-04T12:31:46.296455Z"
     },
     "pycharm": {
      "is_executing": false
@@ -434,18 +434,18 @@
      "output_type": "stream",
      "text": [
       "Iteration:      5,   Func. Count:     50,   Neg. LLF: 6728.995165345449\n",
-      "Iteration:     10,   Func. Count:     90,   Neg. LLF: 6722.151187114983\n",
-      "Optimization terminated successfully    (Exit mode 0)\n",
-      "            Current function value: 6722.1511835327055\n",
-      "            Iterations: 14\n",
-      "            Function evaluations: 117\n",
-      "            Gradient evaluations: 13\n"
+      "Iteration:     10,   Func. Count:     90,   Neg. LLF: 6722.151187114983\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Optimization terminated successfully    (Exit mode 0)\n",
+      "            Current function value: 6722.1511835327055\n",
+      "            Iterations: 14\n",
+      "            Function evaluations: 117\n",
+      "            Gradient evaluations: 13\n",
       "                     Constant Mean - TARCH/ZARCH Model Results                      \n",
       "====================================================================================\n",
       "Dep. Variable:                    Adj Close   R-squared:                       0.000\n",
@@ -455,7 +455,7 @@
       "Method:                  Maximum Likelihood   BIC:                           13495.4\n",
       "                                              No. Observations:                 5030\n",
       "Date:                      Mon, Nov 04 2024   Df Residuals:                     5029\n",
-      "Time:                              11:35:51   Df Model:                            1\n",
+      "Time:                              12:31:46   Df Model:                            1\n",
       "                                 Mean Model                                 \n",
       "============================================================================\n",
       "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
@@ -501,10 +501,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:51.814567Z",
-     "iopub.status.busy": "2024-11-04T11:35:51.813393Z",
-     "iopub.status.idle": "2024-11-04T11:35:51.827857Z",
-     "shell.execute_reply": "2024-11-04T11:35:51.826949Z"
+     "iopub.execute_input": "2024-11-04T12:31:46.302540Z",
+     "iopub.status.busy": "2024-11-04T12:31:46.301309Z",
+     "iopub.status.idle": "2024-11-04T12:31:46.316005Z",
+     "shell.execute_reply": "2024-11-04T12:31:46.315128Z"
     },
     "pycharm": {
      "is_executing": false
@@ -524,7 +524,7 @@
       "Method:            User-specified Parameters   BIC:                           13869.0\n",
       "                                               No. Observations:                 5030\n",
       "Date:                       Mon, Nov 04 2024                                         \n",
-      "Time:                               11:35:51                                         \n",
+      "Time:                               12:31:46                                         \n",
       "      Mean Model     \n",
       "=====================\n",
       "                 coef\n",
@@ -560,10 +560,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:51.832808Z",
-     "iopub.status.busy": "2024-11-04T11:35:51.831609Z",
-     "iopub.status.idle": "2024-11-04T11:35:52.463764Z",
-     "shell.execute_reply": "2024-11-04T11:35:52.462791Z"
+     "iopub.execute_input": "2024-11-04T12:31:46.320985Z",
+     "iopub.status.busy": "2024-11-04T12:31:46.319807Z",
+     "iopub.status.idle": "2024-11-04T12:31:46.981496Z",
+     "shell.execute_reply": "2024-11-04T12:31:46.980560Z"
     },
     "pycharm": {
      "is_executing": false
@@ -654,10 +654,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:52.469124Z",
-     "iopub.status.busy": "2024-11-04T11:35:52.467909Z",
-     "iopub.status.idle": "2024-11-04T11:35:52.810689Z",
-     "shell.execute_reply": "2024-11-04T11:35:52.809792Z"
+     "iopub.execute_input": "2024-11-04T12:31:46.986958Z",
+     "iopub.status.busy": "2024-11-04T12:31:46.985564Z",
+     "iopub.status.idle": "2024-11-04T12:31:47.334708Z",
+     "shell.execute_reply": "2024-11-04T12:31:47.333763Z"
     },
     "pycharm": {
      "is_executing": false
@@ -695,10 +695,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:52.815523Z",
-     "iopub.status.busy": "2024-11-04T11:35:52.814372Z",
-     "iopub.status.idle": "2024-11-04T11:35:52.833227Z",
-     "shell.execute_reply": "2024-11-04T11:35:52.832375Z"
+     "iopub.execute_input": "2024-11-04T12:31:47.339647Z",
+     "iopub.status.busy": "2024-11-04T12:31:47.338470Z",
+     "iopub.status.idle": "2024-11-04T12:31:47.357937Z",
+     "shell.execute_reply": "2024-11-04T12:31:47.357038Z"
     },
     "pycharm": {
      "is_executing": false
@@ -718,7 +718,7 @@
       "Method:            Maximum Likelihood   BIC:                           6632.57\n",
       "                                        No. Observations:                  719\n",
       "Date:                Mon, Nov 04 2024   Df Residuals:                      715\n",
-      "Time:                        11:35:52   Df Model:                            4\n",
+      "Time:                        12:31:47   Df Model:                            4\n",
       "                                   Mean Model                                  \n",
       "===============================================================================\n",
       "                   coef    std err          t      P>|t|       95.0% Conf. Int.\n",
@@ -764,10 +764,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:52.838082Z",
-     "iopub.status.busy": "2024-11-04T11:35:52.836912Z",
-     "iopub.status.idle": "2024-11-04T11:35:52.988995Z",
-     "shell.execute_reply": "2024-11-04T11:35:52.988117Z"
+     "iopub.execute_input": "2024-11-04T12:31:47.362897Z",
+     "iopub.status.busy": "2024-11-04T12:31:47.361710Z",
+     "iopub.status.idle": "2024-11-04T12:31:47.519644Z",
+     "shell.execute_reply": "2024-11-04T12:31:47.518721Z"
     },
     "pycharm": {
      "is_executing": false
@@ -787,7 +787,7 @@
       "Method:            Maximum Likelihood   BIC:                           6414.97\n",
       "                                        No. Observations:                  719\n",
       "Date:                Mon, Nov 04 2024   Df Residuals:                      715\n",
-      "Time:                        11:35:52   Df Model:                            4\n",
+      "Time:                        12:31:47   Df Model:                            4\n",
       "                                   Mean Model                                  \n",
       "===============================================================================\n",
       "                   coef    std err          t      P>|t|       95.0% Conf. Int.\n",
@@ -832,10 +832,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:52.993793Z",
-     "iopub.status.busy": "2024-11-04T11:35:52.992620Z",
-     "iopub.status.idle": "2024-11-04T11:35:53.881665Z",
-     "shell.execute_reply": "2024-11-04T11:35:53.880737Z"
+     "iopub.execute_input": "2024-11-04T12:31:47.524721Z",
+     "iopub.status.busy": "2024-11-04T12:31:47.523503Z",
+     "iopub.status.idle": "2024-11-04T12:31:48.436425Z",
+     "shell.execute_reply": "2024-11-04T12:31:48.435495Z"
     },
     "pycharm": {
      "is_executing": false
@@ -878,10 +878,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:53.886424Z",
-     "iopub.status.busy": "2024-11-04T11:35:53.885271Z",
-     "iopub.status.idle": "2024-11-04T11:35:54.265243Z",
-     "shell.execute_reply": "2024-11-04T11:35:54.264273Z"
+     "iopub.execute_input": "2024-11-04T12:31:48.441827Z",
+     "iopub.status.busy": "2024-11-04T12:31:48.440424Z",
+     "iopub.status.idle": "2024-11-04T12:31:48.853012Z",
+     "shell.execute_reply": "2024-11-04T12:31:48.852098Z"
     },
     "pycharm": {
      "is_executing": false
@@ -901,7 +901,7 @@
       "Method:                  Maximum Likelihood   BIC:                           6408.86\n",
       "                                              No. Observations:                  719\n",
       "Date:                      Mon, Nov 04 2024   Df Residuals:                      715\n",
-      "Time:                              11:35:54   Df Model:                            4\n",
+      "Time:                              12:31:48   Df Model:                            4\n",
       "                                   Mean Model                                  \n",
       "===============================================================================\n",
       "                   coef    std err          t      P>|t|       95.0% Conf. Int.\n",
@@ -958,10 +958,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:54.270669Z",
-     "iopub.status.busy": "2024-11-04T11:35:54.269429Z",
-     "iopub.status.idle": "2024-11-04T11:35:54.628343Z",
-     "shell.execute_reply": "2024-11-04T11:35:54.627457Z"
+     "iopub.execute_input": "2024-11-04T12:31:48.858071Z",
+     "iopub.status.busy": "2024-11-04T12:31:48.856891Z",
+     "iopub.status.idle": "2024-11-04T12:31:49.221525Z",
+     "shell.execute_reply": "2024-11-04T12:31:49.220620Z"
     },
     "pycharm": {
      "is_executing": false
@@ -1111,10 +1111,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:54.633299Z",
-     "iopub.status.busy": "2024-11-04T11:35:54.632103Z",
-     "iopub.status.idle": "2024-11-04T11:35:55.486310Z",
-     "shell.execute_reply": "2024-11-04T11:35:55.485392Z"
+     "iopub.execute_input": "2024-11-04T12:31:49.226652Z",
+     "iopub.status.busy": "2024-11-04T12:31:49.225406Z",
+     "iopub.status.idle": "2024-11-04T12:31:50.206940Z",
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     },
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      "is_executing": false
@@ -1170,10 +1170,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:55.491160Z",
-     "iopub.status.busy": "2024-11-04T11:35:55.490012Z",
-     "iopub.status.idle": "2024-11-04T11:35:55.687239Z",
-     "shell.execute_reply": "2024-11-04T11:35:55.686235Z"
+     "iopub.execute_input": "2024-11-04T12:31:50.211923Z",
+     "iopub.status.busy": "2024-11-04T12:31:50.210749Z",
+     "iopub.status.idle": "2024-11-04T12:31:50.408794Z",
+     "shell.execute_reply": "2024-11-04T12:31:50.407833Z"
     },
     "pycharm": {
      "is_executing": false,
@@ -1264,10 +1264,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:55.692477Z",
-     "iopub.status.busy": "2024-11-04T11:35:55.691179Z",
-     "iopub.status.idle": "2024-11-04T11:35:55.730245Z",
-     "shell.execute_reply": "2024-11-04T11:35:55.729310Z"
+     "iopub.execute_input": "2024-11-04T12:31:50.413786Z",
+     "iopub.status.busy": "2024-11-04T12:31:50.412561Z",
+     "iopub.status.idle": "2024-11-04T12:31:50.451471Z",
+     "shell.execute_reply": "2024-11-04T12:31:50.450544Z"
     },
     "pycharm": {
      "is_executing": false,
@@ -1304,33 +1304,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-0.654894</td>\n",
-       "      <td>1.731423</td>\n",
-       "      <td>-0.684259</td>\n",
+       "      <td>0.706255</td>\n",
+       "      <td>1.283653</td>\n",
+       "      <td>0.676889</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-1.122509</td>\n",
-       "      <td>1.695245</td>\n",
-       "      <td>-1.151874</td>\n",
+       "      <td>-1.037100</td>\n",
+       "      <td>1.264603</td>\n",
+       "      <td>-1.066465</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-1.328236</td>\n",
-       "      <td>1.681510</td>\n",
-       "      <td>-1.357601</td>\n",
+       "      <td>-1.219343</td>\n",
+       "      <td>1.274831</td>\n",
+       "      <td>-1.248708</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-0.354939</td>\n",
-       "      <td>1.681017</td>\n",
-       "      <td>-0.384304</td>\n",
+       "      <td>-2.174715</td>\n",
+       "      <td>1.297430</td>\n",
+       "      <td>-2.204080</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-1.122396</td>\n",
-       "      <td>1.639477</td>\n",
-       "      <td>-1.151761</td>\n",
+       "      <td>0.681440</td>\n",
+       "      <td>1.415216</td>\n",
+       "      <td>0.652075</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1338,11 +1338,11 @@
       ],
       "text/plain": [
        "       data  volatility    errors\n",
-       "0 -0.654894    1.731423 -0.684259\n",
-       "1 -1.122509    1.695245 -1.151874\n",
-       "2 -1.328236    1.681510 -1.357601\n",
-       "3 -0.354939    1.681017 -0.384304\n",
-       "4 -1.122396    1.639477 -1.151761"
+       "0  0.706255    1.283653  0.676889\n",
+       "1 -1.037100    1.264603 -1.066465\n",
+       "2 -1.219343    1.274831 -1.248708\n",
+       "3 -2.174715    1.297430 -2.204080\n",
+       "4  0.681440    1.415216  0.652075"
       ]
      },
      "execution_count": 18,
@@ -1380,10 +1380,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:55.737165Z",
-     "iopub.status.busy": "2024-11-04T11:35:55.735427Z",
-     "iopub.status.idle": "2024-11-04T11:35:55.778023Z",
-     "shell.execute_reply": "2024-11-04T11:35:55.777044Z"
+     "iopub.execute_input": "2024-11-04T12:31:50.456335Z",
+     "iopub.status.busy": "2024-11-04T12:31:50.455107Z",
+     "iopub.status.idle": "2024-11-04T12:31:50.496323Z",
+     "shell.execute_reply": "2024-11-04T12:31:50.495402Z"
     },
     "pycharm": {
      "is_executing": false,
@@ -1500,10 +1500,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:55.783600Z",
-     "iopub.status.busy": "2024-11-04T11:35:55.782289Z",
-     "iopub.status.idle": "2024-11-04T11:35:55.821536Z",
-     "shell.execute_reply": "2024-11-04T11:35:55.820507Z"
+     "iopub.execute_input": "2024-11-04T12:31:50.501091Z",
+     "iopub.status.busy": "2024-11-04T12:31:50.499938Z",
+     "iopub.status.idle": "2024-11-04T12:31:50.537346Z",
+     "shell.execute_reply": "2024-11-04T12:31:50.536444Z"
     },
     "pycharm": {
      "is_executing": false,
diff --git a/devel/univariate/univariate_volatility_scenarios.html b/devel/univariate/univariate_volatility_scenarios.html
index 1f9e4792c8..e68e2c0bc3 100644
--- a/devel/univariate/univariate_volatility_scenarios.html
+++ b/devel/univariate/univariate_volatility_scenarios.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Volatility Scenarios - arch v7.1.1 (+16)</title>
+        <title>Volatility Scenarios - arch v7.1.1 (+18)</title>
       
     
 
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
@@ -829,7 +829,7 @@ <h1 id="Volatility-Scenarios">Volatility Scenarios<a class="headerlink" href="#V
 Method:            Maximum Likelihood   BIC:                           16436.1
                                         No. Observations:                 5030
 Date:                Mon, Nov 04 2024   Df Residuals:                     5029
-Time:                        11:37:16   Df Model:                            1
+Time:                        12:33:12   Df Model:                            1
                                  Mean Model
 ============================================================================
                  coef    std err          t      P&gt;|t|      95.0% Conf. Int.
@@ -846,7 +846,7 @@ <h1 id="Volatility-Scenarios">Volatility Scenarios<a class="headerlink" href="#V
 =============================================================================
 
 Covariance estimator: robust
-ARCHModelResult, id: 0x7fa69c52dd50
+ARCHModelResult, id: 0x7f30167fc610
 </pre></div></div>
 </div>
 <p>GJR-GARCH models support analytical forecasts, which is the default. The forecasts are produced for all of 2017 using the estimated model parameters.</p>
diff --git a/devel/univariate/univariate_volatility_scenarios.ipynb b/devel/univariate/univariate_volatility_scenarios.ipynb
index 5293331275..57a78466ff 100644
--- a/devel/univariate/univariate_volatility_scenarios.ipynb
+++ b/devel/univariate/univariate_volatility_scenarios.ipynb
@@ -23,10 +23,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:35:59.517469Z",
-     "iopub.status.busy": "2024-11-04T11:35:59.517096Z",
-     "iopub.status.idle": "2024-11-04T11:36:01.540827Z",
-     "shell.execute_reply": "2024-11-04T11:36:01.539809Z"
+     "iopub.execute_input": "2024-11-04T12:31:54.399263Z",
+     "iopub.status.busy": "2024-11-04T12:31:54.398720Z",
+     "iopub.status.idle": "2024-11-04T12:31:56.068653Z",
+     "shell.execute_reply": "2024-11-04T12:31:56.067902Z"
     }
    },
    "outputs": [],
@@ -58,10 +58,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:36:01.544786Z",
-     "iopub.status.busy": "2024-11-04T11:36:01.544164Z",
-     "iopub.status.idle": "2024-11-04T11:36:01.596722Z",
-     "shell.execute_reply": "2024-11-04T11:36:01.595761Z"
+     "iopub.execute_input": "2024-11-04T12:31:56.073064Z",
+     "iopub.status.busy": "2024-11-04T12:31:56.071982Z",
+     "iopub.status.idle": "2024-11-04T12:31:56.121374Z",
+     "shell.execute_reply": "2024-11-04T12:31:56.120489Z"
     }
    },
    "outputs": [
@@ -107,10 +107,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:36:01.600514Z",
-     "iopub.status.busy": "2024-11-04T11:36:01.599951Z",
-     "iopub.status.idle": "2024-11-04T11:36:01.607249Z",
-     "shell.execute_reply": "2024-11-04T11:36:01.606424Z"
+     "iopub.execute_input": "2024-11-04T12:31:56.124911Z",
+     "iopub.status.busy": "2024-11-04T12:31:56.124117Z",
+     "iopub.status.idle": "2024-11-04T12:31:56.131932Z",
+     "shell.execute_reply": "2024-11-04T12:31:56.131106Z"
     }
    },
    "outputs": [],
@@ -137,10 +137,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:36:01.610502Z",
-     "iopub.status.busy": "2024-11-04T11:36:01.609957Z",
-     "iopub.status.idle": "2024-11-04T11:36:01.715008Z",
-     "shell.execute_reply": "2024-11-04T11:36:01.714120Z"
+     "iopub.execute_input": "2024-11-04T12:31:56.135055Z",
+     "iopub.status.busy": "2024-11-04T12:31:56.134557Z",
+     "iopub.status.idle": "2024-11-04T12:31:56.240723Z",
+     "shell.execute_reply": "2024-11-04T12:31:56.239815Z"
     }
    },
    "outputs": [
@@ -156,7 +156,7 @@
        "Method:            Maximum Likelihood   BIC:                           16436.1\n",
        "                                        No. Observations:                 5030\n",
        "Date:                Mon, Nov 04 2024   Df Residuals:                     5029\n",
-       "Time:                        11:36:01   Df Model:                            1\n",
+       "Time:                        12:31:56   Df Model:                            1\n",
        "                                 Mean Model                                 \n",
        "============================================================================\n",
        "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
@@ -173,7 +173,7 @@
        "=============================================================================\n",
        "\n",
        "Covariance estimator: robust\n",
-       "ARCHModelResult, id: 0x7f51800ff730"
+       "ARCHModelResult, id: 0x7fdf4c639d50"
       ]
      },
      "execution_count": 4,
@@ -207,10 +207,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:36:01.718516Z",
-     "iopub.status.busy": "2024-11-04T11:36:01.717916Z",
-     "iopub.status.idle": "2024-11-04T11:36:02.598259Z",
-     "shell.execute_reply": "2024-11-04T11:36:02.597394Z"
+     "iopub.execute_input": "2024-11-04T12:31:56.244450Z",
+     "iopub.status.busy": "2024-11-04T12:31:56.243915Z",
+     "iopub.status.idle": "2024-11-04T12:31:57.091158Z",
+     "shell.execute_reply": "2024-11-04T12:31:57.090325Z"
     }
    },
    "outputs": [
@@ -257,10 +257,10 @@
    "metadata": {
     "collapsed": false,
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:36:02.602008Z",
-     "iopub.status.busy": "2024-11-04T11:36:02.601433Z",
-     "iopub.status.idle": "2024-11-04T11:36:02.673183Z",
-     "shell.execute_reply": "2024-11-04T11:36:02.672325Z"
+     "iopub.execute_input": "2024-11-04T12:31:57.094856Z",
+     "iopub.status.busy": "2024-11-04T12:31:57.094331Z",
+     "iopub.status.idle": "2024-11-04T12:31:57.165471Z",
+     "shell.execute_reply": "2024-11-04T12:31:57.164589Z"
     },
     "jupyter": {
      "outputs_hidden": false
@@ -299,10 +299,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:36:02.676457Z",
-     "iopub.status.busy": "2024-11-04T11:36:02.675905Z",
-     "iopub.status.idle": "2024-11-04T11:36:03.559816Z",
-     "shell.execute_reply": "2024-11-04T11:36:03.558963Z"
+     "iopub.execute_input": "2024-11-04T12:31:57.168793Z",
+     "iopub.status.busy": "2024-11-04T12:31:57.168276Z",
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+     "shell.execute_reply": "2024-11-04T12:31:58.058915Z"
     }
    },
    "outputs": [
@@ -362,10 +362,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:36:03.563460Z",
-     "iopub.status.busy": "2024-11-04T11:36:03.562913Z",
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-     "shell.execute_reply": "2024-11-04T11:36:04.378261Z"
+     "iopub.execute_input": "2024-11-04T12:31:58.063696Z",
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+     "shell.execute_reply": "2024-11-04T12:31:58.884379Z"
     }
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    "outputs": [
@@ -426,10 +426,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-11-04T11:36:04.382717Z",
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-     "shell.execute_reply": "2024-11-04T11:36:04.693143Z"
+     "iopub.execute_input": "2024-11-04T12:31:58.888896Z",
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+     "shell.execute_reply": "2024-11-04T12:31:59.202289Z"
     }
    },
    "outputs": [
@@ -465,10 +465,10 @@
    "execution_count": 10,
    "metadata": {
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-     "shell.execute_reply": "2024-11-04T11:36:04.701388Z"
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+     "shell.execute_reply": "2024-11-04T12:31:59.210066Z"
     }
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    "outputs": [],
@@ -490,10 +490,10 @@
    "execution_count": 11,
    "metadata": {
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-     "iopub.execute_input": "2024-11-04T11:36:04.705632Z",
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-     "shell.execute_reply": "2024-11-04T11:36:07.608165Z"
+     "iopub.execute_input": "2024-11-04T12:31:59.213826Z",
+     "iopub.status.busy": "2024-11-04T12:31:59.213304Z",
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+     "shell.execute_reply": "2024-11-04T12:32:01.902389Z"
     }
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    "outputs": [
@@ -555,10 +555,10 @@
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    "metadata": {
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-     "iopub.status.idle": "2024-11-04T11:36:08.242120Z",
-     "shell.execute_reply": "2024-11-04T11:36:08.241209Z"
+     "iopub.execute_input": "2024-11-04T12:32:01.909025Z",
+     "iopub.status.busy": "2024-11-04T12:32:01.908433Z",
+     "iopub.status.idle": "2024-11-04T12:32:02.537812Z",
+     "shell.execute_reply": "2024-11-04T12:32:02.536909Z"
     }
    },
    "outputs": [
diff --git a/devel/univariate/utility.html b/devel/univariate/utility.html
index e6d1843c60..71cd2cc0c6 100644
--- a/devel/univariate/utility.html
+++ b/devel/univariate/utility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Utilities - arch v7.1.1 (+16)</title>
+        <title>Utilities - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/volatility.html b/devel/univariate/volatility.html
index 3627667b06..3ddd1e74a4 100644
--- a/devel/univariate/volatility.html
+++ b/devel/univariate/volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Volatility Processes - arch v7.1.1 (+16)</title>
+        <title>Volatility Processes - arch v7.1.1 (+18)</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-header__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v7.1.1 (+16)
+            arch v7.1.1 (+18)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v7.1.1 (+16)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+16)" data-md-component="logo">
+    <a href="../index.html" title="arch v7.1.1 (+18)" class="md-nav__button md-logo" aria-label="arch v7.1.1 (+18)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v7.1.1 (+16)
+    arch v7.1.1 (+18)
   </label>
   
     <div class="md-nav__source">