Prepare CRAN release

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.23.0.12
+Version: 0.24.0
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",
@@ -80,7 +80,7 @@ Depends:
 Imports:
     bayestestR (>= 0.15.0),
     datawizard (>= 0.13.0),
-    insight (>= 0.20.5),
+    insight (>= 1.0.0),
     graphics,
     methods,
     stats,
@@ -155,7 +155,7 @@ Suggests:
     logspline,
     lqmm,
     M3C,
-    marginaleffects (>= 0.20.1),
+    marginaleffects (>= 0.20.4),
     MASS,
     Matrix,
     mclogit,
@@ -224,4 +224,3 @@ Config/testthat/edition: 3
 Config/testthat/parallel: true
 Config/Needs/website: easystats/easystatstemplate
 Config/rcmdcheck/ignore-inconsequential-notes: true
-Remotes: easystats/insight

R/1_model_parameters.R

@@ -13,7 +13,7 @@
 #'   **scam**, **VGAM**, `Gam` (although the output of `Gam` is more Anova-alike),
 #'   `gamm`, ...
 #' - [ANOVA][model_parameters.aov()]: **afex**, `aov`, `anova`, `Gam`, ...
-#' - [Bayesian][model_parameters.stanreg()]: **BayesFactor**, **blavaan**, **brms**,
+#' - [Bayesian][model_parameters.brmsfit()]: **BayesFactor**, **blavaan**, **brms**,
 #'   **MCMCglmm**, **posterior**, **rstanarm**, `bayesQR`, `bcplm`, `BGGM`, `blmrm`,
 #'   `blrm`, `mcmc.list`, `MCMCglmm`, ...
 #' - [Clustering][model_parameters.hclust()]: **hclust**, **kmeans**, **mclust**, **pam**, ...
@@ -184,7 +184,7 @@
 #'
 #' Compared to fixed effects (or single-level) models, determining appropriate
 #' df for Wald-based inference in mixed models is more difficult.
 #' See [the R GLMM FAQ](https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#what-are-the-p-values-listed-by-summaryglmerfit-etc.-are-they-reliable)
 #' for a discussion.
 #'
 #' Several approximate methods for computing df are available, but you should
@@ -426,10 +426,9 @@
 #'
 #' @param model Model object.
 #' @param ci Confidence Interval (CI) level. Default to `0.95` (`95%`).
-#' @param bootstrap Should estimates be based on bootstrapped model? If
-#'   `TRUE`, then arguments of [Bayesian
-#'   regressions][model_parameters.stanreg] apply (see also
-#'   [`bootstrap_parameters()`]).
+#' @param bootstrap Should estimates be based on bootstrapped model? If `TRUE`,
+#'   then arguments of [Bayesian regressions][model_parameters.brmsfit] apply
+#'   (see also [`bootstrap_parameters()`]).
 #' @param iterations The number of bootstrap replicates. This only apply in the
 #'   case of bootstrapped frequentist models.
 #' @param standardize The method used for standardizing the parameters. Can be