diff --git a/DESCRIPTION b/DESCRIPTION
index 52131b894..721c4040f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: bayestestR
 Title: Understand and Describe Bayesian Models and Posterior Distributions
-Version: 0.13.2.3
+Version: 0.13.2.4
 Authors@R:
     c(person(given = "Dominique",
              family = "Makowski",
diff --git a/NEWS.md b/NEWS.md
index 88d65e273..333f8f6a3 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -8,14 +8,19 @@
 
   * `estimate_density()`
 
+## Changes
+
+* `bayesian_as_frequentist()` now supports more model families from Bayesian
+  models that can be successfully converted to their frequentists counterparts.
+
 # bayestestR 0.13.2
 
 ## Breaking Changes
 
 * `pd_to_p()` now returns 1 and a warning for values smaller than 0.5.
 
-* `map_estimate()`, `p_direction()`, `p_map()`, and `p_significance()` now 
-  return a data-frame when the input is a numeric vector. (making the output 
+* `map_estimate()`, `p_direction()`, `p_map()`, and `p_significance()` now
+  return a data-frame when the input is a numeric vector. (making the output
   consistently a data frame for all inputs.)
 
 * Argument `posteriors` was renamed into `posterior`. Before, there were a mix
@@ -43,7 +48,7 @@
 
 * Improved speed performance to `bayesfactor_models()` for `brmsfit` objects
   that already included a `marglik` element in the model object.
-  
+
 ## New functionality
 
 * `as.logical()` for `bayesfactor_restricted()` results, extracts the boolean
diff --git a/R/convert_bayesian_to_frequentist.R b/R/convert_bayesian_to_frequentist.R
index 83cc6adf5..29f2c59bf 100644
--- a/R/convert_bayesian_to_frequentist.R
+++ b/R/convert_bayesian_to_frequentist.R
@@ -45,26 +45,45 @@ convert_bayesian_as_frequentist <- function(model, data = NULL, REML = TRUE) {
 
   info <- insight::model_info(model, verbose = FALSE)
   formula <- insight::find_formula(model)
-  family <- insight::get_family(model)
-  if (inherits(family, "brmsfamily")) {
-    family <- get(family$family)(link = family$link)
+  model_family <- insight::get_family(model)
+  if (inherits(model_family, "brmsfamily")) {
+    insight::check_if_installed("glmmTMB")
+    # exception: ordbetareg()
+    if ("custom" %in% model_family$family && all(model_family$name == "ord_beta_reg")) {
+      model_family <- glmmTMB::ordbeta()
+    } else {
+      # not all families return proper objects from "get", so we capture
+      # some families via switch here...
+      model_family <- .safe(switch(
+        model_family$family,
+        "beta" = glmmTMB::beta_family(link = model_family$link),
+        "beta_binomial" = glmmTMB::betabinomial(link = model_family$link),
+        "negbinomial" = glmmTMB::nbinom1(link = model_family$link),
+        "lognormal" = glmmTMB::lognormal(link = model_family$link),
+        "student" = glmmTMB::t_family(link = model_family$link),
+        get(model_family$family)(link = model_family$link)
+      ))
+      if (is.null(model_family)) {
+        insight::format_error("Model could not be automatically converted to frequentist model.")
+      }
+    }
   }
 
   freq <- tryCatch(.convert_bayesian_as_frequentist(
-    info = info, formula = formula, data = data, family = family, REML = REML
+    info = info, formula = formula, data = data, family = model_family, REML = REML
   ), error = function(e) e)
 
   if (inherits(freq, "error")) {
-    family <- get(family$family)(link = family$link)
+    model_family <- get(model_family$family)(link = model_family$link)
     freq <- .convert_bayesian_as_frequentist(
-      info = info, formula = formula, data = data, family = family, REML = REML
+      info = info, formula = formula, data = data, family = model_family, REML = REML
     )
   }
 
   if (inherits(freq, "error")) {
     insight::format_error("Model could not be automatically converted to frequentist model.")
   } else {
-    return(freq)
+    freq
   }
 }
 
@@ -79,7 +98,7 @@ convert_bayesian_as_frequentist <- function(model, data = NULL, REML = TRUE) {
   # subset,
   # knots,
   # meta-analysis
-  if (info$is_dispersion || info$is_zero_inflated || info$is_zeroinf || info$is_hurdle) {
+  if (info$is_dispersion || info$is_beta || info$is_zero_inflated || info$is_zeroinf || info$is_hurdle) {
     insight::check_if_installed("glmmTMB")
 
     cond_formula <- .rebuild_cond_formula(formula)