diff --git a/DESCRIPTION b/DESCRIPTION
index cba26e65..0d40fd3a 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: modelbased
 Title: Estimation of Model-Based Predictions, Contrasts and Means
-Version: 0.10.0.31
+Version: 0.10.0.32
 Authors@R:
     c(person(given = "Dominique",
              family = "Makowski",
diff --git a/R/clean_names.R b/R/clean_names.R
index 067d338c..f74e877f 100644
--- a/R/clean_names.R
+++ b/R/clean_names.R
@@ -28,13 +28,7 @@
 
   if (length(vars) == 1) {
     if (type == "contrast") {
-      if (minfo$is_logit && predict == "response") {
-        names(means)[names(means) == vars] <- "Odds_ratio"
-      } else if (minfo$is_poisson && predict == "response") {
-        names(means)[names(means) == vars] <- "Ratio"
-      } else {
-        names(means)[names(means) == vars] <- "Difference"
-      }
+      names(means)[names(means) == vars] <- "Difference"
     } else if (type == "mean") {
       if (minfo$is_logit && predict == "response") {
         names(means)[names(means) == vars] <- "Probability"
diff --git a/R/estimate_contrast_methods.R b/R/estimate_contrast_methods.R
index 26f4eb14..a05f0a33 100644
--- a/R/estimate_contrast_methods.R
+++ b/R/estimate_contrast_methods.R
@@ -31,7 +31,11 @@ estimate_contrasts.estimate_predicted <- function(model,
   minfo <- insight::model_info(model, response = 1)
 
   # model df
-  dof <- insight::get_df(model, type = "wald", verbose = FALSE)
+  if (minfo$is_bayesian) {
+    dof <- Inf
+  } else {
+    dof <- insight::get_df(model, type = "wald", verbose = FALSE)
+  }
   crit_factor <- (1 + ci) / 2
 
   ## TODO: For Bayesian models, we always use the returned standard errors
diff --git a/R/get_emcontrasts.R b/R/get_emcontrasts.R
index 223ea89a..a41ab5f6 100644
--- a/R/get_emcontrasts.R
+++ b/R/get_emcontrasts.R
@@ -41,6 +41,22 @@ get_emcontrasts <- function(model,
   # extract first focal term
   first_focal <- my_args$contrast[1]
 
+  # setup arguments
+  fun_args <- list(model)
+
+  # handle distributional parameters
+  if (predict %in% .brms_aux_elements(model) && inherits(model, "brmsfit")) {
+    dpars <- TRUE
+    fun_args$dpar <- predict
+  } else {
+    dpars <- FALSE
+    fun_args$type <- predict
+  }
+
+  # add dots
+  dots <- list(...)
+  fun_args <- insight::compact_list(c(fun_args, dots))
+
   # if first focal term is numeric, we contrast slopes
   if (is.numeric(model_data[[first_focal]]) &&
     !first_focal %in% on_the_fly_factors &&
@@ -51,23 +67,13 @@ get_emcontrasts <- function(model,
       insight::format_error("Please specify the `by` argument to calculate contrasts of slopes.") # nolint
     }
     # Run emmeans
-    estimated <- suppressMessages(emmeans::emtrends(
-      model,
-      specs = my_args$by,
-      var = my_args$contrast,
-      type = predict,
-      ...
-    ))
+    fun_args <- c(fun_args, list(specs = my_args$by, var = my_args$contrast))
+    estimated <- suppressMessages(do.call(emmeans::emtrends, fun_args))
     emm_by <- NULL
   } else {
     # Run emmeans
-    estimated <- suppressMessages(emmeans::emmeans(
-      model,
-      specs = my_args$emmeans_specs,
-      at = my_args$emmeans_at,
-      type = predict,
-      ...
-    ))
+    fun_args <- c(fun_args, list(specs = my_args$emmeans_specs, at = my_args$emmeans_at))
+    estimated <- suppressMessages(do.call(emmeans::emmeans, fun_args))
     # Find by variables
     emm_by <- my_args$emmeans_specs[!my_args$emmeans_specs %in% my_args$contrast]
     if (length(emm_by) == 0) {
@@ -76,7 +82,7 @@ get_emcontrasts <- function(model,
   }
 
   # If means are on the response scale (e.g., probabilities), need to regrid
-  if (predict == "response") {
+  if (predict == "response" || dpars) {
     estimated <- emmeans::regrid(estimated)
   }
 
diff --git a/R/get_emmeans.R b/R/get_emmeans.R
index 4bdefd74..8de4c8de 100644
--- a/R/get_emmeans.R
+++ b/R/get_emmeans.R
@@ -58,8 +58,10 @@ get_emmeans <- function(model,
 
   # handle distributional parameters
   if (predict %in% .brms_aux_elements(model) && inherits(model, "brmsfit")) {
+    dpars <- TRUE
     fun_args$dpar <- predict
   } else {
+    dpars <- FALSE
     fun_args$type <- predict
   }
 
@@ -70,6 +72,11 @@ get_emmeans <- function(model,
   # Run emmeans
   estimated <- suppressMessages(suppressWarnings(do.call(emmeans::emmeans, fun_args)))
 
+  # backtransform to response scale for dpars
+  if (dpars) {
+    estimated <- emmeans::regrid(estimated)
+  }
+
   # Special behaviour for transformations #138 (see below)
   if ("retransform" %in% names(my_args) && length(my_args$retransform) > 0) {
     for (var in names(my_args$retransform)) {
diff --git a/R/get_marginalmeans.R b/R/get_marginalmeans.R
index caa18f41..c32db2f6 100644
--- a/R/get_marginalmeans.R
+++ b/R/get_marginalmeans.R
@@ -131,7 +131,7 @@ get_marginalmeans <- function(model,
   dots[c("by", "conf_level", "type", "digits", "bias_correction", "sigma", "offset")] <- NULL
 
   # model df - can be passed via `...`
-  if (is.null(dots$df)) {
+  if (is.null(dots$df) && !model_info$is_bayesian) {
     dots$df <- insight::get_df(model, type = "wald", verbose = FALSE)
   }
 
@@ -225,7 +225,7 @@ get_marginalmeans <- function(model,
   # just need to add "hypothesis" argument
   means <- .call_marginaleffects(fun_args)
 
-  # Fifth step: post-processin marginal means----------------------------------
+  # Fifth step: post-processing marginal means----------------------------------
   # ---------------------------------------------------------------------------
 
   # filter "by" rows when we have "average" marginalization, because we don't
@@ -317,7 +317,7 @@ get_marginalmeans <- function(model,
     msg <- c(msg, "\nIt seems that not all required levels of the focal terms are available in the provided data. If you want predictions extrapolated to a hypothetical target population, try setting `estimate=\"population\".") # nolint
   }
   # we get this error for models with complex random effects structures in glmmTMB
-  if (grepl("map factor length must equal", out$message, fixed = TRUE)) {
+  if (grepl("map factor length must equal", out$message, fixed = TRUE) || grepl("cannot allocate", out$message, fixed = TRUE)) { # nolint
     msg <- c(
       msg,
       paste0(
@@ -335,7 +335,7 @@ get_marginalmeans <- function(model,
 }
 
 
-# filter datagrid foe `estimate = "average"`---------------------------------
+# filter datagrid for `estimate = "average"`---------------------------------
 
 .filter_datagrid_average <- function(means, estimate, datagrid, datagrid_info) {
   # filter "by" rows when we have "average" marginalization, because we don't
diff --git a/R/get_marginaltrends.R b/R/get_marginaltrends.R
index 0ac6a946..8c54f2be 100644
--- a/R/get_marginaltrends.R
+++ b/R/get_marginaltrends.R
@@ -85,7 +85,7 @@ get_marginaltrends <- function(model,
   }
 
   # model df - can be passed via `...`
-  if (is.null(dots$df)) {
+  if (is.null(dots$df) && !model_info$is_bayesian) {
     dots$df <- insight::get_df(model, type = "wald", verbose = FALSE)
   }