diff --git a/R/tm_outliers.R b/R/tm_outliers.R
index 926367fe9..34cccf2b2 100644
--- a/R/tm_outliers.R
+++ b/R/tm_outliers.R
@@ -1,22 +1,24 @@
-#' Outliers Module
+#' Outliers module
 #'
 #' Module to analyze and identify outliers using different methods
+#' such as IQR, Z-score, and Percentiles, and offers visualizations including
+#' box plots, density plots, and cumulative distribution plots to help interpret the outliers.
 #'
 #' @inheritParams teal::module
 #' @inheritParams shared_params
 #'
 #' @param outlier_var (`data_extract_spec` or `list` of multiple `data_extract_spec`)
-#'  variable to consider for the outliers analysis.
-#' @param categorical_var (`data_extract_spec` or `list` of multiple `data_extract_spec`)
-#'   categorical factor to split the selected outlier variables on.
+#' Specifies variable(s) to be analyzed for outliers.
+#' @param categorical_var (`data_extract_spec` or `list` of multiple `data_extract_spec`, optional)
+#' Specifies the categorical variable(s) to split the selected outlier variables on.
 #'
 #' @templateVar ggnames "Boxplot","Density Plot","Cumulative Distribution Plot"
 #' @template ggplot2_args_multi
 #'
 #' @examples
-#' # general data example
 #' library(teal.widgets)
 #'
+#' # general data example
 #' data <- teal_data()
 #' data <- within(data, {
 #'   CO2 <- CO2
@@ -67,8 +69,6 @@
 #' }
 #'
 #' # CDISC data example
-#' library(teal.widgets)
-#'
 #' data <- teal_data()
 #' data <- within(data, {
 #'   ADSL <- rADSL
@@ -169,6 +169,7 @@ tm_outliers <- function(label = "Outliers Module",
   )
 }
 
+# UI function for the outliers module
 ui_outliers <- function(id, ...) {
   args <- list(...)
   ns <- NS(id)
@@ -300,6 +301,7 @@ ui_outliers <- function(id, ...) {
   )
 }
 
+# Server function for the outliers module
 srv_outliers <- function(id, data, reporter, filter_panel_api, outlier_var,
                          categorical_var, plot_height, plot_width, ggplot2_args) {
   with_reporter <- !missing(reporter) && inherits(reporter, "Reporter")
diff --git a/man/tm_outliers.Rd b/man/tm_outliers.Rd
index d6efe45fa..b0898aa19 100644
--- a/man/tm_outliers.Rd
+++ b/man/tm_outliers.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/tm_outliers.R
 \name{tm_outliers}
 \alias{tm_outliers}
-\title{Outliers Module}
+\title{Outliers module}
 \usage{
 tm_outliers(
   label = "Outliers Module",
@@ -21,10 +21,10 @@ tm_outliers(
 For \code{modules()} defaults to \code{"root"}. See \code{Details}.}
 
 \item{outlier_var}{(\code{data_extract_spec} or \code{list} of multiple \code{data_extract_spec})
-variable to consider for the outliers analysis.}
+Specifies variable(s) to be analyzed for outliers.}
 
-\item{categorical_var}{(\code{data_extract_spec} or \code{list} of multiple \code{data_extract_spec})
-categorical factor to split the selected outlier variables on.}
+\item{categorical_var}{(\code{data_extract_spec} or \code{list} of multiple \code{data_extract_spec}, optional)
+Specifies the categorical variable(s) to split the selected outlier variables on.}
 
 \item{ggtheme}{optional, (\code{character}) \code{ggplot2} theme to be used by default. Defaults to \code{"gray"}.}
 
@@ -50,11 +50,13 @@ into context. For example the \code{\link[shiny:helpText]{shiny::helpText()}} el
 }
 \description{
 Module to analyze and identify outliers using different methods
+such as IQR, Z-score, and Percentiles, and offers visualizations including
+box plots, density plots, and cumulative distribution plots to help interpret the outliers.
 }
 \examples{
-# general data example
 library(teal.widgets)
 
+# general data example
 data <- teal_data()
 data <- within(data, {
   CO2 <- CO2
@@ -105,8 +107,6 @@ if (interactive()) {
 }
 
 # CDISC data example
-library(teal.widgets)
-
 data <- teal_data()
 data <- within(data, {
   ADSL <- rADSL