diff --git a/R/methods_aod.R b/R/methods_aod.R
index 1243e984f..1e6945960 100644
--- a/R/methods_aod.R
+++ b/R/methods_aod.R
@@ -19,7 +19,7 @@
 #' `"conditional"` or `"full"` (for `MuMIn::model.avg()`) or `"all"`. See section
 #' _Model components_ for an overview of possible options for `component`.
 #' @inheritParams model_parameters.default
-#' @inheritParams model_parameters.stanreg
+#' @inheritParams model_parameters.brmsfit
 #' @inheritParams simulate_model
 #'
 #' @seealso [insight::standardize_names()] to rename columns into a consistent,
diff --git a/R/methods_base.R b/R/methods_base.R
index 826a39e06..1e071ca52 100644
--- a/R/methods_base.R
+++ b/R/methods_base.R
@@ -1,3 +1,4 @@
+#' @rdname model_parameters.brmsfit
 #' @export
 model_parameters.data.frame <- function(model,
                                         as_draws = FALSE,
diff --git a/R/methods_brms.R b/R/methods_brms.R
index 3ec843058..6ed2dcdab 100644
--- a/R/methods_brms.R
+++ b/R/methods_brms.R
@@ -1,4 +1,69 @@
+#' @title Parameters from Bayesian Models
+#' @name model_parameters.brmsfit
+#'
+#' @description
+#' Model parameters from Bayesian models. This function internally calls
+#' [`bayestestR::describe_posterior()`] to get the relvant information for
+#' the output.
+#'
+#' @param model Bayesian model (including SEM from **blavaan**. May also be
+#' a data frame with posterior samples, however, `as_draws` must be set to
+#' `TRUE` (else, for data frames `NULL` is returned).
+#' @param ci Credible Interval (CI) level. Default to `0.95` (`95%`). See
+#' [bayestestR::ci()] for further details.
+#' @param group_level Logical, for multilevel models (i.e. models with random
+#' effects) and when `effects = "all"` or `effects = "random"`,
+#' include the parameters for each group level from random effects. If
+#' `group_level = FALSE` (the default), only information on SD and COR
+#' are shown.
+#' @param component Which type of parameters to return, such as parameters for the
+#' conditional model, the zero-inflation part of the model, the dispersion
+#' term, or other auxiliary parameters be returned? Applies to models with
+#' zero-inflation and/or dispersion formula, or if parameters such as `sigma`
+#' should be included. May be abbreviated. Note that the *conditional*
+#' component is also called *count* or *mean* component, depending on the
+#' model. There are three convenient shortcuts: `component = "all"` returns
+#' all possible parameters. If `component = "location"`, location parameters
+#' such as `conditional`, `zero_inflated`, or `smooth_terms`, are returned
+#' (everything that are fixed or random effects - depending on the `effects`
+#' argument - but no auxiliary parameters). For `component = "distributional"`
+#' (or `"auxiliary"`), components like `sigma`, `dispersion`, or `beta`
+#' (and other auxiliary parameters) are returned.
+#' @param as_draws Logical, if `TRUE` and `model` is of class `data.frame`,
+#' the data frame is treated as posterior samples and handled similar to
+#' Bayesian models. All arguments in `...` are passed to
+#' `model_parameters.draws()`.
+#' @inheritParams model_parameters.default
+#' @inheritParams bayestestR::describe_posterior
 #' @inheritParams insight::get_parameters
+#'
+#' @seealso [insight::standardize_names()] to rename columns into a consistent,
+#' standardized naming scheme.
+#'
+#' @note When `standardize = "refit"`, columns `diagnostic`, `bf_prior` and
+#' `priors` refer to the *original* `model`. If `model` is a data frame,
+#' arguments `diagnostic`, `bf_prior` and `priors` are ignored.
+#'
+#' There is also a
+#' [`plot()`-method](https://easystats.github.io/see/articles/parameters.html)
+#' implemented in the [**see**-package](https://easystats.github.io/see/).
+#'
+#' @inheritSection model_parameters Confidence intervals and approximation of degrees of freedom
+#'
+#' @inheritSection model_parameters.zcpglm Model components
+#'
+#' @examples
+#' \donttest{
+#' library(parameters)
+#' if (require("rstanarm")) {
+#'   model <- suppressWarnings(stan_glm(
+#'     Sepal.Length ~ Petal.Length * Species,
+#'     data = iris, iter = 500, refresh = 0
+#'   ))
+#'   model_parameters(model)
+#' }
+#' }
+#' @return A data frame of indices related to the model's parameters.
 #' @export
 model_parameters.brmsfit <- function(model,
                                      centrality = "median",
diff --git a/R/methods_glmmTMB.R b/R/methods_glmmTMB.R
index edc7a30fe..044af85c4 100644
--- a/R/methods_glmmTMB.R
+++ b/R/methods_glmmTMB.R
@@ -55,7 +55,7 @@
 #'   up computation of the summary table.
 #'
 #' @inheritParams model_parameters.default
-#' @inheritParams model_parameters.stanreg
+#' @inheritParams model_parameters.brmsfit
 #' @inheritParams simulate_model
 #'
 #' @inheritSection model_parameters.zcpglm Model components
diff --git a/R/methods_hclust.R b/R/methods_hclust.R
index a4a0773e2..cd9bf93b0 100644
--- a/R/methods_hclust.R
+++ b/R/methods_hclust.R
@@ -3,7 +3,9 @@
 #' Format cluster models obtained for example by [kmeans()].
 #'
 #' @param model Cluster model.
-#' @inheritParams model_parameters.default
+#' @param data A data frame.
+#' @param clusters A vector with clusters assignments (must be same length as
+#' rows in data).
 #' @param ... Arguments passed to or from other methods.
 #'
 #' @examplesIf require("factoextra", quietly = TRUE) && require("dbscan", quietly = TRUE) && require("cluster", quietly = TRUE) && require("fpc", quietly = TRUE)
@@ -123,9 +125,6 @@ model_parameters.hclust <- function(model, data = NULL, clusters = NULL, ...) {
 }
 
 
-
-
-#' @inheritParams n_clusters
 #' @export
 model_parameters.pvclust <- function(model, data = NULL, clusters = NULL, ci = 0.95, ...) {
   if (is.null(data)) {
diff --git a/R/methods_rstanarm.R b/R/methods_rstanarm.R
index 50af6d454..21e49cd42 100644
--- a/R/methods_rstanarm.R
+++ b/R/methods_rstanarm.R
@@ -1,65 +1,3 @@
-#' Parameters from Bayesian Models
-#'
-#' Parameters from Bayesian models.
-#'
-#' @param model Bayesian model (including SEM from **blavaan**. May also be
-#'   a data frame with posterior samples, however, `as_draws` must be set to
-#'   `TRUE` (else, for data frames `NULL` is returned).
-#' @param ci Credible Interval (CI) level. Default to `0.95` (`95%`). See
-#'   [bayestestR::ci()] for further details.
-#' @param group_level Logical, for multilevel models (i.e. models with random
-#'   effects) and when `effects = "all"` or `effects = "random"`,
-#'   include the parameters for each group level from random effects. If
-#'   `group_level = FALSE` (the default), only information on SD and COR
-#'   are shown.
-#' @param component Which type of parameters to return, such as parameters for the
-#'   conditional model, the zero-inflation part of the model, the dispersion
-#'   term, or other auxiliary parameters be returned? Applies to models with
-#'   zero-inflation and/or dispersion formula, or if parameters such as `sigma`
-#'   should be included. May be abbreviated. Note that the *conditional*
-#'   component is also called *count* or *mean* component, depending on the
-#'   model. There are three convenient shortcuts: `component = "all"` returns
-#'   all possible parameters. If `component = "location"`, location parameters
-#'   such as `conditional`, `zero_inflated`, or `smooth_terms`, are returned
-#'   (everything that are fixed or random effects - depending on the `effects`
-#'   argument - but no auxiliary parameters). For `component = "distributional"`
-#'   (or `"auxiliary"`), components like `sigma`, `dispersion`, or `beta`
-#'   (and other auxiliary parameters) are returned.
-#' @param as_draws Logical, if `TRUE` and `model` is of class `data.frame`,
-#'   the data frame is treated as posterior samples and handled similar to
-#'   Bayesian models. All arguments in `...` are passed to
-#'   `model_parameters.draws()`.
-#' @inheritParams model_parameters.default
-#' @inheritParams bayestestR::describe_posterior
-#' @inheritParams insight::get_parameters
-#'
-#' @seealso [insight::standardize_names()] to rename columns into a consistent,
-#' standardized naming scheme.
-#'
-#' @note When `standardize = "refit"`, columns `diagnostic`, `bf_prior` and
-#' `priors` refer to the *original* `model`. If `model` is a data frame,
-#' arguments `diagnostic`, `bf_prior` and `priors` are ignored.
-#'
-#' There is also a
-#' [`plot()`-method](https://easystats.github.io/see/articles/parameters.html)
-#' implemented in the [**see**-package](https://easystats.github.io/see/).
-#'
-#' @inheritSection model_parameters Confidence intervals and approximation of degrees of freedom
-#'
-#' @inheritSection model_parameters.zcpglm Model components
-#'
-#' @examples
-#' \donttest{
-#' library(parameters)
-#' if (require("rstanarm")) {
-#'   model <- suppressWarnings(stan_glm(
-#'     Sepal.Length ~ Petal.Length * Species,
-#'     data = iris, iter = 500, refresh = 0
-#'   ))
-#'   model_parameters(model)
-#' }
-#' }
-#' @return A data frame of indices related to the model's parameters.
 #' @export
 model_parameters.stanreg <- function(model,
                                      centrality = "median",
diff --git a/man/cluster_performance.Rd b/man/cluster_performance.Rd
index 025a3edb8..0051ef67c 100644
--- a/man/cluster_performance.Rd
+++ b/man/cluster_performance.Rd
@@ -13,6 +13,11 @@ cluster_performance(model, ...)
 \item{model}{Cluster model.}
 
 \item{...}{Arguments passed to or from other methods.}
+
+\item{data}{A data frame.}
+
+\item{clusters}{A vector with clusters assignments (must be same length as
+rows in data).}
 }
 \description{
 Compute performance indices for clustering solutions.
diff --git a/man/model_parameters.stanreg.Rd b/man/model_parameters.brmsfit.Rd
similarity index 97%
rename from man/model_parameters.stanreg.Rd
rename to man/model_parameters.brmsfit.Rd
index 54558a6f7..11e2337d3 100644
--- a/man/model_parameters.stanreg.Rd
+++ b/man/model_parameters.brmsfit.Rd
@@ -1,10 +1,19 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/methods_rstanarm.R
-\name{model_parameters.stanreg}
-\alias{model_parameters.stanreg}
+% Please edit documentation in R/methods_base.R, R/methods_brms.R
+\name{model_parameters.data.frame}
+\alias{model_parameters.data.frame}
+\alias{model_parameters.brmsfit}
 \title{Parameters from Bayesian Models}
 \usage{
-\method{model_parameters}{stanreg}(
+\method{model_parameters}{data.frame}(
+  model,
+  as_draws = FALSE,
+  exponentiate = FALSE,
+  verbose = TRUE,
+  ...
+)
+
+\method{model_parameters}{brmsfit}(
   model,
   centrality = "median",
   dispersion = FALSE,
@@ -15,8 +24,9 @@
   rope_ci = 0.95,
   bf_prior = NULL,
   diagnostic = c("ESS", "Rhat"),
-  priors = TRUE,
+  priors = FALSE,
   effects = "fixed",
+  component = "all",
   exponentiate = FALSE,
   standardize = NULL,
   group_level = FALSE,
@@ -31,6 +41,48 @@
 a data frame with posterior samples, however, \code{as_draws} must be set to
 \code{TRUE} (else, for data frames \code{NULL} is returned).}
 
+\item{as_draws}{Logical, if \code{TRUE} and \code{model} is of class \code{data.frame},
+the data frame is treated as posterior samples and handled similar to
+Bayesian models. All arguments in \code{...} are passed to
+\code{model_parameters.draws()}.}
+
+\item{exponentiate}{Logical, indicating whether or not to exponentiate the
+coefficients (and related confidence intervals). This is typical for
+logistic regression, or more generally speaking, for models with log or
+logit links. It is also recommended to use \code{exponentiate = TRUE} for models
+with log-transformed response values. For models with a log-transformed
+response variable, when \code{exponentiate = TRUE}, a one-unit increase in the
+predictor is associated with multiplying the outcome by that predictor's
+coefficient. \strong{Note:} Delta-method standard errors are also computed (by
+multiplying the standard errors by the transformed coefficients). This is
+to mimic behaviour of other software packages, such as Stata, but these
+standard errors poorly estimate uncertainty for the transformed
+coefficient. The transformed confidence interval more clearly captures this
+uncertainty. For \code{compare_parameters()}, \code{exponentiate = "nongaussian"}
+will only exponentiate coefficients from non-Gaussian families.}
+
+\item{verbose}{Toggle warnings and messages.}
+
+\item{...}{Arguments passed to or from other methods. For instance, when
+\code{bootstrap = TRUE}, arguments like \code{type} or \code{parallel} are passed down to
+\code{bootstrap_model()}.
+
+Further non-documented arguments are:
+\itemize{
+\item \code{digits}, \code{p_digits}, \code{ci_digits} and \code{footer_digits} to set the number of
+digits for the output. \code{groups} can be used to group coefficients. These
+arguments will be passed to the print-method, or can directly be used in
+\code{print()}, see documentation in \code{\link[=print.parameters_model]{print.parameters_model()}}.
+\item If \code{s_value = TRUE}, the p-value will be replaced by the S-value in the
+output (cf. \emph{Rafi and Greenland 2020}).
+\item \code{pd} adds an additional column with the \emph{probability of direction} (see
+\code{\link[bayestestR:p_direction]{bayestestR::p_direction()}} for details). Furthermore, see 'Examples' for
+this function.
+\item For developers, whose interest mainly is to get a "tidy" data frame of
+model summaries, it is recommended to set \code{pretty_names = FALSE} to speed
+up computation of the summary table.
+}}
+
 \item{centrality}{The point-estimates (centrality indices) to compute. Character
 (vector) or list with one or more of these options: \code{"median"}, \code{"mean"}, \code{"MAP"}
 (see \code{\link[bayestestR:map_estimate]{map_estimate()}}), \code{"trimmed"} (which is just \code{mean(x, trim = threshold)}),
@@ -81,20 +133,19 @@ with one or more of these options: \code{"ESS"}, \code{"Rhat"}, \code{"MCSE"} or
 \item{effects}{Should results for fixed effects, random effects or both be
 returned? Only applies to mixed models. May be abbreviated.}
 
-\item{exponentiate}{Logical, indicating whether or not to exponentiate the
-coefficients (and related confidence intervals). This is typical for
-logistic regression, or more generally speaking, for models with log or
-logit links. It is also recommended to use \code{exponentiate = TRUE} for models
-with log-transformed response values. For models with a log-transformed
-response variable, when \code{exponentiate = TRUE}, a one-unit increase in the
-predictor is associated with multiplying the outcome by that predictor's
-coefficient. \strong{Note:} Delta-method standard errors are also computed (by
-multiplying the standard errors by the transformed coefficients). This is
-to mimic behaviour of other software packages, such as Stata, but these
-standard errors poorly estimate uncertainty for the transformed
-coefficient. The transformed confidence interval more clearly captures this
-uncertainty. For \code{compare_parameters()}, \code{exponentiate = "nongaussian"}
-will only exponentiate coefficients from non-Gaussian families.}
+\item{component}{Which type of parameters to return, such as parameters for the
+conditional model, the zero-inflation part of the model, the dispersion
+term, or other auxiliary parameters be returned? Applies to models with
+zero-inflation and/or dispersion formula, or if parameters such as \code{sigma}
+should be included. May be abbreviated. Note that the \emph{conditional}
+component is also called \emph{count} or \emph{mean} component, depending on the
+model. There are three convenient shortcuts: \code{component = "all"} returns
+all possible parameters. If \code{component = "location"}, location parameters
+such as \code{conditional}, \code{zero_inflated}, or \code{smooth_terms}, are returned
+(everything that are fixed or random effects - depending on the \code{effects}
+argument - but no auxiliary parameters). For \code{component = "distributional"}
+(or \code{"auxiliary"}), components like \code{sigma}, \code{dispersion}, or \code{beta}
+(and other auxiliary parameters) are returned.}
 
 \item{standardize}{The method used for standardizing the parameters. Can be
 \code{NULL} (default; no standardization), \code{"refit"} (for re-fitting the model
@@ -140,53 +191,14 @@ frame, which can be different from how they are printed. Inspect the
 names.}
 
 \item{drop}{See \code{keep}.}
-
-\item{verbose}{Toggle warnings and messages.}
-
-\item{...}{Arguments passed to or from other methods. For instance, when
-\code{bootstrap = TRUE}, arguments like \code{type} or \code{parallel} are passed down to
-\code{bootstrap_model()}.
-
-Further non-documented arguments are:
-\itemize{
-\item \code{digits}, \code{p_digits}, \code{ci_digits} and \code{footer_digits} to set the number of
-digits for the output. \code{groups} can be used to group coefficients. These
-arguments will be passed to the print-method, or can directly be used in
-\code{print()}, see documentation in \code{\link[=print.parameters_model]{print.parameters_model()}}.
-\item If \code{s_value = TRUE}, the p-value will be replaced by the S-value in the
-output (cf. \emph{Rafi and Greenland 2020}).
-\item \code{pd} adds an additional column with the \emph{probability of direction} (see
-\code{\link[bayestestR:p_direction]{bayestestR::p_direction()}} for details). Furthermore, see 'Examples' for
-this function.
-\item For developers, whose interest mainly is to get a "tidy" data frame of
-model summaries, it is recommended to set \code{pretty_names = FALSE} to speed
-up computation of the summary table.
-}}
-
-\item{component}{Which type of parameters to return, such as parameters for the
-conditional model, the zero-inflation part of the model, the dispersion
-term, or other auxiliary parameters be returned? Applies to models with
-zero-inflation and/or dispersion formula, or if parameters such as \code{sigma}
-should be included. May be abbreviated. Note that the \emph{conditional}
-component is also called \emph{count} or \emph{mean} component, depending on the
-model. There are three convenient shortcuts: \code{component = "all"} returns
-all possible parameters. If \code{component = "location"}, location parameters
-such as \code{conditional}, \code{zero_inflated}, or \code{smooth_terms}, are returned
-(everything that are fixed or random effects - depending on the \code{effects}
-argument - but no auxiliary parameters). For \code{component = "distributional"}
-(or \code{"auxiliary"}), components like \code{sigma}, \code{dispersion}, or \code{beta}
-(and other auxiliary parameters) are returned.}
-
-\item{as_draws}{Logical, if \code{TRUE} and \code{model} is of class \code{data.frame},
-the data frame is treated as posterior samples and handled similar to
-Bayesian models. All arguments in \code{...} are passed to
-\code{model_parameters.draws()}.}
 }
 \value{
 A data frame of indices related to the model's parameters.
 }
 \description{
-Parameters from Bayesian models.
+Model parameters from Bayesian models. This function internally calls
+\code{\link[bayestestR:describe_posterior]{bayestestR::describe_posterior()}} to get the relvant information for
+the output.
 }
 \note{
 When \code{standardize = "refit"}, columns \code{diagnostic}, \code{bf_prior} and
diff --git a/man/model_parameters.hclust.Rd b/man/model_parameters.hclust.Rd
index 39fe4c989..2593c9786 100644
--- a/man/model_parameters.hclust.Rd
+++ b/man/model_parameters.hclust.Rd
@@ -9,6 +9,11 @@
 \arguments{
 \item{model}{Cluster model.}
 
+\item{data}{A data frame.}
+
+\item{clusters}{A vector with clusters assignments (must be same length as
+rows in data).}
+
 \item{...}{Arguments passed to or from other methods.}
 }
 \description{