badges updates

Author: fradav

site/publications-new.ejs

@@ -18,10 +18,10 @@ for (const item of items) {
       <div class="col-12 mb-3">
         <div class="card">
           <div class="card-body">
-            <div class="row align-items-center">
+            <div class="row align-items-center border-left border-primary">
               <div class="col-md-2">
                 <a href="https://github.com/computorg/<%- item.repo %>" target="_blank">
-                  <img src="https://img.shields.io/github/actions/workflow/status/computorg/<%- item.repo %>/build.yml?label=build&logo=github" class="img-fluid" alt="Build Status">
+                  <img style="width:100px;" src=https://img.shields.io/github/actions/workflow/status/computorg/<%- item.repo %>/build.yml?label=build&logo=github" class="img-fluid" alt="Build Status">
                 </a>
               </div>
               <div class="col-md-10">

site/publications.ejs

@@ -1,3 +1,22 @@
+- abstract': >-
+    Model-based clustering provides a principled way of
+        developing clustering methods. We develop a new model-based
+        clustering methods for count data. The method combines clustering
+        and variable selection for improved clustering. The method is based
+        on conditionally independent Poisson mixture models and Poisson
+        generalized linear models. The method is demonstrated on simulated
+        data and data from an ultra running race, where the method yields
+        excellent clustering and variable selection performance.
+  authors: Julien Jacques and Thomas Brendan Murphy
+  date: 2025-07-01
+  description: ''
+  draft: false
+  journal: Computo
+  pdf: ''
+  repo: published-202507-jacques-count-data
+  title: Model-Based Clustering and Variable Selection for Multivariate Count Data
+  url: ''
+  year: 2025
 - abstract': >-
     Reservoir Computing (RC) is a machine learning method
         based on neural networks that efficiently process information
@@ -29,6 +48,33 @@
   title: 'Reservoir Computing in R: a Tutorial for Using reservoirnet to Predict Complex Time-Series'
   url: ''
   year: 2025
+- abstract': >-
+    The `R` Package `IBMPopSim` facilitates the simulation of
+        the random evolution of heterogeneous populations using stochastic
+        Individual-Based Models (IBMs). The package enables users to
+        simulate population evolution, in which individuals are
+        characterized by their age and some characteristics, and the
+        population is modified by different types of events, including
+        births/arrivals, death/exit events, or changes of characteristics.
+        The frequency at which an event can occur to an individual can
+        depend on their age and characteristics, but also on the
+        characteristics of other individuals (interactions). Such models
+        have a wide range of applications in fields including actuarial
+        science, biology, ecology or epidemiology. `IBMPopSim` overcomes the
+        limitations of time-consuming IBMs simulations by implementing new
+        efficient algorithms based on thinning methods, which are compiled
+        using the `Rcpp` package while providing a user-friendly interface.
+  authors: Daphné Giorgi, Sarah Kaakai and Vincent Lemaire
+  date: 2025-01-27
+  description: >
+    This document provides a full description of the Stochastic Individual-Based Models (IBMs) that can be implemented in the IBMPopSim package. A unified mathematical and simulation framework is given, with a detailed description of the simulation algorithm. Examples of applications for the package are also provided, showing the performance and flexibility of IBMPopSim. 
+  draft: false
+  journal: Computo
+  pdf: ''
+  repo: published-202412-giorgi-efficient
+  title: Efficient simulation of individual-based population models
+  url: ''
+  year: 2025
 - abstract': >-
     In this paper, Spectral Bridges, a novel clustering
         algorithm, is introduced. This algorithm builds upon the traditional
@@ -57,33 +103,6 @@
   title: Spectral Bridges
   url: https://computo.sfds.asso.fr/published-202412-ambroise-spectral/
   year: 2024
-- abstract': >-
-    The `R` Package `IBMPopSim` facilitates the simulation of
-        the random evolution of heterogeneous populations using stochastic
-        Individual-Based Models (IBMs). The package enables users to
-        simulate population evolution, in which individuals are
-        characterized by their age and some characteristics, and the
-        population is modified by different types of events, including
-        births/arrivals, death/exit events, or changes of characteristics.
-        The frequency at which an event can occur to an individual can
-        depend on their age and characteristics, but also on the
-        characteristics of other individuals (interactions). Such models
-        have a wide range of applications in fields including actuarial
-        science, biology, ecology or epidemiology. `IBMPopSim` overcomes the
-        limitations of time-consuming IBMs simulations by implementing new
-        efficient algorithms based on thinning methods, which are compiled
-        using the `Rcpp` package while providing a user-friendly interface.
-  authors: Daphné Giorgi, Sarah Kaakai and Vincent Lemaire
-  date: 2024-12-01
-  description: >
-    This document provides a full description of the Stochastic Individual-Based Models (IBMs) that can be implemented in the IBMPopSim package. A unified mathematical and simulation framework is given, with a detailed description of the simulation algorithm. Examples of applications for the package are also provided, showing the performance and flexibility of IBMPopSim. 
-  draft: false
-  journal: Computo
-  pdf: ''
-  repo: published-202412-giorgi-efficient
-  title: Efficient simulation of individual-based population models
-  url: https://computo.sfds.asso.fr/published-202412-giorgi-efficient
-  year: 2024
 - abstract': >-
     Conformal Inference (CI) is a popular approach for
         generating finite sample prediction intervals based on the output of
@@ -492,8 +511,8 @@
   description: ''
   draft: false
   journal: Computo
-  pdf: https://computo.sfds.asso.fr/published-202204-deeplearning-occupancy-lynx/published-202204-gimenez-lynx.pdf
+  pdf: ''
   repo: published-202204-deeplearning-occupancy-lynx
   title: Trade-off between deep learning for species identification and inference about predator-prey co-occurrence
-  url: https://computo.sfds.asso.fr/published-202204-deeplearning-occupancy-lynx/
+  url: ''
   year: 2022