diff --git a/R/select_parameters.R b/R/select_parameters.R
index 0061fe552..93c697017 100644
--- a/R/select_parameters.R
+++ b/R/select_parameters.R
@@ -6,21 +6,17 @@
 #' @param model A statistical model (of class `lm`, `glm`, or `merMod`).
 #' @param ... Arguments passed to or from other methods.
 #'
-#' @details
-#'   \subsection{Classical lm and glm}{
-#'     For frequentist GLMs, `select_parameters()` performs an AIC-based
-#'     stepwise selection.
-#'   }
+#' @section Classical lm and glm:
+#' For frequentist GLMs, `select_parameters()` performs an AIC-based stepwise
+#' selection.
 #'
-#'   \subsection{Mixed models}{
-#'     For mixed-effects models of class `merMod`, stepwise selection is
-#'     based on [cAIC4::stepcAIC()]. This step function
-#'     only searches the "best" model based on the random-effects structure,
-#'     i.e. `select_parameters()` adds or excludes random-effects until
-#'     the cAIC can't be improved further.
-#'   }
+#' @section Mixed models:
+#' For mixed-effects models of class `merMod`, stepwise selection is based on
+#' [`cAIC4::stepcAIC()`]. This step function only searches the "best" model
+#' based on the random-effects structure, i.e. `select_parameters()` adds or
+#' excludes random-effects until the cAIC can't be improved further.
 #'
-#' @examples
+#' @examplesIf requireNamespace("lme4")
 #' model <- lm(mpg ~ ., data = mtcars)
 #' select_parameters(model)
 #'
@@ -28,13 +24,11 @@
 #' select_parameters(model)
 #' \donttest{
 #' # lme4 -------------------------------------------
-#' if (require("lme4")) {
-#'   model <- lmer(
-#'     Sepal.Width ~ Sepal.Length * Petal.Width * Petal.Length + (1 | Species),
-#'     data = iris
-#'   )
-#'   select_parameters(model)
-#' }
+#' model <- lme4::lmer(
+#'   Sepal.Width ~ Sepal.Length * Petal.Width * Petal.Length + (1 | Species),
+#'   data = iris
+#' )
+#' select_parameters(model)
 #' }
 #'
 #' @return The model refitted with optimal number of parameters.
diff --git a/man/select_parameters.Rd b/man/select_parameters.Rd
index 733f6bfe5..12a880eac 100644
--- a/man/select_parameters.Rd
+++ b/man/select_parameters.Rd
@@ -41,21 +41,22 @@ The model refitted with optimal number of parameters.
 This function performs an automated selection of the 'best' parameters,
 updating and returning the "best" model.
 }
-\details{
-\subsection{Classical lm and glm}{
-For frequentist GLMs, \code{select_parameters()} performs an AIC-based
-stepwise selection.
-}
+\section{Classical lm and glm}{
 
-\subsection{Mixed models}{
-For mixed-effects models of class \code{merMod}, stepwise selection is
-based on \code{\link[cAIC4:stepcAIC]{cAIC4::stepcAIC()}}. This step function
-only searches the "best" model based on the random-effects structure,
-i.e. \code{select_parameters()} adds or excludes random-effects until
-the cAIC can't be improved further.
+For frequentist GLMs, \code{select_parameters()} performs an AIC-based stepwise
+selection.
 }
+
+\section{Mixed models}{
+
+For mixed-effects models of class \code{merMod}, stepwise selection is based on
+\code{\link[cAIC4:stepcAIC]{cAIC4::stepcAIC()}}. This step function only searches the "best" model
+based on the random-effects structure, i.e. \code{select_parameters()} adds or
+excludes random-effects until the cAIC can't be improved further.
 }
+
 \examples{
+\dontshow{if (requireNamespace("lme4")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 model <- lm(mpg ~ ., data = mtcars)
 select_parameters(model)
 
@@ -63,13 +64,11 @@ model <- lm(mpg ~ cyl * disp * hp * wt, data = mtcars)
 select_parameters(model)
 \donttest{
 # lme4 -------------------------------------------
-if (require("lme4")) {
-  model <- lmer(
-    Sepal.Width ~ Sepal.Length * Petal.Width * Petal.Length + (1 | Species),
-    data = iris
-  )
-  select_parameters(model)
-}
+model <- lme4::lmer(
+  Sepal.Width ~ Sepal.Length * Petal.Width * Petal.Length + (1 | Species),
+  data = iris
+)
+select_parameters(model)
 }
-
+\dontshow{\}) # examplesIf}
 }