diff --git a/R/map_estimate.R b/R/map_estimate.R
index b0e4ebd8c..f06628403 100644
--- a/R/map_estimate.R
+++ b/R/map_estimate.R
@@ -26,7 +26,7 @@
 #' map_estimate(posterior)
 #'
 #' plot(density(posterior))
-#' abline(v = map_estimate(posterior), col = "red")
+#' abline(v = as.numeric(map_estimate(posterior)), col = "red")
 #'
 #' model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)
 #' map_estimate(model)
diff --git a/R/p_significance.R b/R/p_significance.R
index 0ff802a54..15d54b682 100644
--- a/R/p_significance.R
+++ b/R/p_significance.R
@@ -2,8 +2,9 @@
 #'
 #' Compute the probability of **Practical Significance** (***ps***), which can be conceptualized as a unidirectional equivalence test. It returns the probability that effect is above a given threshold corresponding to a negligible effect in the median's direction. Mathematically, it is defined as the proportion of the posterior distribution of the median sign above the threshold.
 #'
-#' @inheritParams rope
 #' @param threshold The threshold value that separates significant from negligible effect. If `"default"`, the range is set to `0.1` if input is a vector, and based on [`rope_range()`][rope_range] if a Bayesian model is provided.
+#' @inheritParams rope
+#' @inheritParams hdi
 #'
 #' @return Values between 0 and 1 corresponding to the probability of practical significance (ps).
 #'
diff --git a/R/si.R b/R/si.R
index 6e8fb7c32..fd353c52f 100644
--- a/R/si.R
+++ b/R/si.R
@@ -84,7 +84,7 @@
 #' The Support Interval. \doi{10.31234/osf.io/zwnxb}
 #'
 #' @export
-si <- function(posterior, prior = NULL, BF = 1, verbose = TRUE, ...) {
+si <- function(posterior, ...) {
   UseMethod("si")
 }
 
diff --git a/man/map_estimate.Rd b/man/map_estimate.Rd
index 79eb95dab..41b8f6ba5 100644
--- a/man/map_estimate.Rd
+++ b/man/map_estimate.Rd
@@ -106,7 +106,7 @@ posterior <- rnorm(10000)
 map_estimate(posterior)
 
 plot(density(posterior))
-abline(v = map_estimate(posterior), col = "red")
+abline(v = as.numeric(map_estimate(posterior)), col = "red")
 
 model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)
 map_estimate(model)
diff --git a/man/p_significance.Rd b/man/p_significance.Rd
index bd53edd5e..f275550ae 100644
--- a/man/p_significance.Rd
+++ b/man/p_significance.Rd
@@ -49,6 +49,11 @@ p_significance(x, ...)
 
 \item{threshold}{The threshold value that separates significant from negligible effect. If \code{"default"}, the range is set to \code{0.1} if input is a vector, and based on \code{\link[=rope_range]{rope_range()}} if a Bayesian model is provided.}
 
+\item{use_iterations}{Logical, if \code{TRUE} and \code{x} is a \code{get_predicted} object,
+(returned by \code{\link[insight:get_predicted]{insight::get_predicted()}}), the function is applied to the
+iterations instead of the predictions. This only applies to models that return
+iterations for predicted values (e.g., \code{brmsfit} models).}
+
 \item{verbose}{Toggle off warnings.}
 
 \item{effects}{Should results for fixed effects, random effects or both be
diff --git a/man/si.Rd b/man/si.Rd
index 2d132000d..5fea658df 100644
--- a/man/si.Rd
+++ b/man/si.Rd
@@ -11,7 +11,7 @@
 \alias{si.data.frame}
 \title{Compute Support Intervals}
 \usage{
-si(posterior, prior = NULL, BF = 1, verbose = TRUE, ...)
+si(posterior, ...)
 
 \method{si}{numeric}(posterior, prior = NULL, BF = 1, verbose = TRUE, ...)
 
@@ -69,15 +69,15 @@ si(posterior, prior = NULL, BF = 1, verbose = TRUE, ...)
 \code{emmGrid} or a data frame - representing a posterior distribution(s)
 from (see 'Details').}
 
+\item{...}{Arguments passed to and from other methods. (Can be used to pass
+arguments to internal \code{\link[logspline:logspline]{logspline::logspline()}}.)}
+
 \item{prior}{An object representing a prior distribution (see 'Details').}
 
 \item{BF}{The amount of support required to be included in the support interval.}
 
 \item{verbose}{Toggle off warnings.}
 
-\item{...}{Arguments passed to and from other methods. (Can be used to pass
-arguments to internal \code{\link[logspline:logspline]{logspline::logspline()}}.)}
-
 \item{effects}{Should results for fixed effects, random effects or both be
 returned? Only applies to mixed models. May be abbreviated.}