diff --git a/R/check_distribution.R b/R/check_distribution.R
index 976fedf7c..77cc19db6 100644
--- a/R/check_distribution.R
+++ b/R/check_distribution.R
@@ -48,15 +48,11 @@ NULL
 #' There is a `plot()` method, which shows the probabilities of all predicted
 #' distributions, however, only if the probability is greater than zero.
 #'
-#' @examples
-#' if (require("lme4") && require("parameters") &&
-#'   require("see") && require("patchwork") && require("randomForest")) {
-#'   data(sleepstudy)
-#'
-#'   model <<- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
-#'   check_distribution(model)
-#'   plot(check_distribution(model))
-#' }
+#' @examplesIf require("lme4") && require("parameters") && require("see") && require("patchwork") && require("randomForest")
+#' data(sleepstudy, package = "lme4")
+#' model <<- lme4::lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
+#' check_distribution(model)
+#' plot(check_distribution(model))
 #' @export
 check_distribution <- function(model) {
   UseMethod("check_distribution")
diff --git a/R/check_itemscale.R b/R/check_itemscale.R
index 182b681bc..8d8b71082 100644
--- a/R/check_itemscale.R
+++ b/R/check_itemscale.R
@@ -43,17 +43,16 @@
 #' - Trochim WMK (2008) Types of Reliability.
 #'   ([web](https://conjointly.com/kb/types-of-reliability/))
 #'
-#' @examples
+#' @examplesIf require("parameters") && require("psych")
 #' # data generation from '?prcomp', slightly modified
 #' C <- chol(S <- toeplitz(0.9^(0:15)))
 #' set.seed(17)
 #' X <- matrix(rnorm(1600), 100, 16)
 #' Z <- X %*% C
-#' if (require("parameters") && require("psych")) {
-#'   pca <- principal_components(as.data.frame(Z), rotation = "varimax", n = 3)
-#'   pca
-#'   check_itemscale(pca)
-#' }
+#'
+#' pca <- principal_components(as.data.frame(Z), rotation = "varimax", n = 3)
+#' pca
+#' check_itemscale(pca)
 #' @export
 check_itemscale <- function(x) {
   if (!inherits(x, "parameters_pca")) {
diff --git a/R/check_multimodal.R b/R/check_multimodal.R
index 9223ee59a..6285af02f 100644
--- a/R/check_multimodal.R
+++ b/R/check_multimodal.R
@@ -10,33 +10,29 @@
 #' @param x A numeric vector or a data frame.
 #' @param ... Arguments passed to or from other methods.
 #'
-#' @examples
+#' @examplesIf require("multimode") && require("mclust")
 #' \dontrun{
-#' if (require("multimode")) {
-#'   # Univariate
-#'   x <- rnorm(1000)
-#'   check_multimodal(x)
-#' }
+#' # Univariate
+#' x <- rnorm(1000)
+#' check_multimodal(x)
 #'
-#' if (require("multimode") && require("mclust")) {
-#'   x <- c(rnorm(1000), rnorm(1000, 2))
-#'   check_multimodal(x)
+#' x <- c(rnorm(1000), rnorm(1000, 2))
+#' check_multimodal(x)
 #'
-#'   # Multivariate
-#'   m <- data.frame(
-#'     x = rnorm(200),
-#'     y = rbeta(200, 2, 1)
-#'   )
-#'   plot(m$x, m$y)
-#'   check_multimodal(m)
+#' # Multivariate
+#' m <- data.frame(
+#'   x = rnorm(200),
+#'   y = rbeta(200, 2, 1)
+#' )
+#' plot(m$x, m$y)
+#' check_multimodal(m)
 #'
-#'   m <- data.frame(
-#'     x = c(rnorm(100), rnorm(100, 4)),
-#'     y = c(rbeta(100, 2, 1), rbeta(100, 1, 4))
-#'   )
-#'   plot(m$x, m$y)
-#'   check_multimodal(m)
-#' }
+#' m <- data.frame(
+#'   x = c(rnorm(100), rnorm(100, 4)),
+#'   y = c(rbeta(100, 2, 1), rbeta(100, 1, 4))
+#' )
+#' plot(m$x, m$y)
+#' check_multimodal(m)
 #' }
 #' @references
 #' - Ameijeiras-Alonso, J., Crujeiras, R. M., and Rodríguez-Casal, A. (2019).
diff --git a/R/check_predictions.R b/R/check_predictions.R
index 2e3bc6462..d52b8378a 100644
--- a/R/check_predictions.R
+++ b/R/check_predictions.R
@@ -66,26 +66,21 @@
 #' - Gelman, A., Hill, J., and Vehtari, A. (2020). Regression and Other Stories.
 #'   Cambridge University Press.
 #'
-#' @examples
-#' library(performance)
+#' @examplesIf require("see")
 #' # linear model
-#' if (require("see")) {
-#'   model <- lm(mpg ~ disp, data = mtcars)
-#'   check_predictions(model)
-#' }
+#' model <- lm(mpg ~ disp, data = mtcars)
+#' check_predictions(model)
 #'
 #' # discrete/integer outcome
-#' if (require("see")) {
-#'   set.seed(99)
-#'   d <- iris
-#'   d$skewed <- rpois(150, 1)
-#'   model <- glm(
-#'     skewed ~ Species + Petal.Length + Petal.Width,
-#'     family = poisson(),
-#'     data = d
-#'   )
-#'   check_predictions(model, type = "discrete_both")
-#' }
+#' set.seed(99)
+#' d <- iris
+#' d$skewed <- rpois(150, 1)
+#' model <- glm(
+#'   skewed ~ Species + Petal.Length + Petal.Width,
+#'   family = poisson(),
+#'   data = d
+#' )
+#' check_predictions(model, type = "discrete_both")
 #'
 #' @export
 check_predictions <- function(object, ...) {
diff --git a/R/check_sphericity.R b/R/check_sphericity.R
index 5a9ebba95..a087a1a5f 100644
--- a/R/check_sphericity.R
+++ b/R/check_sphericity.R
@@ -10,15 +10,14 @@
 #' @return Invisibly returns the p-values of the test statistics. A p-value <
 #'   0.05 indicates a violation of sphericity.
 #'
-#' @examples
-#' if (require("car")) {
-#'   soils.mod <- lm(
-#'     cbind(pH, N, Dens, P, Ca, Mg, K, Na, Conduc) ~ Block + Contour * Depth,
-#'     data = Soils
-#'   )
+#' @examplesIf require("car") && require("carData")
+#' data(Soils, package = "carData")
+#' soils.mod <- lm(
+#'   cbind(pH, N, Dens, P, Ca, Mg, K, Na, Conduc) ~ Block + Contour * Depth,
+#'   data = Soils
+#' )
 #'
-#'   check_sphericity(Manova(soils.mod))
-#' }
+#' check_sphericity(Manova(soils.mod))
 #' @export
 check_sphericity <- function(x, ...) {
   UseMethod("check_sphericity")
diff --git a/R/icc.R b/R/icc.R
index 8ce19288e..9155a7f40 100644
--- a/R/icc.R
+++ b/R/icc.R
@@ -143,29 +143,25 @@
 #' very large, the variance ratio in the output makes no sense, e.g. because
 #' it is negative. In such cases, it might help to use `robust = TRUE`.
 #'
-#' @examples
-#' if (require("lme4")) {
-#'   model <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
-#'   icc(model)
-#' }
+#' @examplesIf require("lme4")
+#' model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
+#' icc(model)
 #'
 #' # ICC for specific group-levels
-#' if (require("lme4")) {
-#'   data(sleepstudy)
-#'   set.seed(12345)
-#'   sleepstudy$grp <- sample(1:5, size = 180, replace = TRUE)
-#'   sleepstudy$subgrp <- NA
-#'   for (i in 1:5) {
-#'     filter_group <- sleepstudy$grp == i
-#'     sleepstudy$subgrp[filter_group] <-
-#'       sample(1:30, size = sum(filter_group), replace = TRUE)
-#'   }
-#'   model <- lmer(
-#'     Reaction ~ Days + (1 | grp / subgrp) + (1 | Subject),
-#'     data = sleepstudy
-#'   )
-#'   icc(model, by_group = TRUE)
+#' data(sleepstudy, package = "lme4")
+#' set.seed(12345)
+#' sleepstudy$grp <- sample(1:5, size = 180, replace = TRUE)
+#' sleepstudy$subgrp <- NA
+#' for (i in 1:5) {
+#'   filter_group <- sleepstudy$grp == i
+#'   sleepstudy$subgrp[filter_group] <-
+#'     sample(1:30, size = sum(filter_group), replace = TRUE)
 #' }
+#' model <- lme4::lmer(
+#'   Reaction ~ Days + (1 | grp / subgrp) + (1 | Subject),
+#'   data = sleepstudy
+#' )
+#' icc(model, by_group = TRUE)
 #' @export
 icc <- function(model,
                 by_group = FALSE,
diff --git a/R/r2_nakagawa.R b/R/r2_nakagawa.R
index cdf1c8e02..9b751c843 100644
--- a/R/r2_nakagawa.R
+++ b/R/r2_nakagawa.R
@@ -46,12 +46,10 @@
 #'    generalized linear mixed-effects models revisited and expanded. Journal of
 #'    The Royal Society Interface, 14(134), 20170213.
 #'
-#' @examples
-#' if (require("lme4")) {
-#'   model <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
-#'   r2_nakagawa(model)
-#'   r2_nakagawa(model, by_group = TRUE)
-#' }
+#' @examplesIf require("lme4")
+#' model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
+#' r2_nakagawa(model)
+#' r2_nakagawa(model, by_group = TRUE)
 #' @export
 r2_nakagawa <- function(model,
                         by_group = FALSE,
diff --git a/man/check_distribution.Rd b/man/check_distribution.Rd
index 83ece551a..9ccc8ccc1 100644
--- a/man/check_distribution.Rd
+++ b/man/check_distribution.Rd
@@ -45,12 +45,10 @@ implemented in the
 \href{https://easystats.github.io/see/}{\pkg{see}-package}.
 }
 \examples{
-if (require("lme4") && require("parameters") &&
-  require("see") && require("patchwork") && require("randomForest")) {
-  data(sleepstudy)
-
-  model <<- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
-  check_distribution(model)
-  plot(check_distribution(model))
-}
+\dontshow{if (require("lme4") && require("parameters") && require("see") && require("patchwork") && require("randomForest")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+data(sleepstudy, package = "lme4")
+model <<- lme4::lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
+check_distribution(model)
+plot(check_distribution(model))
+\dontshow{\}) # examplesIf}
 }
diff --git a/man/check_itemscale.Rd b/man/check_itemscale.Rd
index dc6128bbf..7fa487ab5 100644
--- a/man/check_itemscale.Rd
+++ b/man/check_itemscale.Rd
@@ -44,16 +44,17 @@ acceptability. Satisfactory range lies between 0.2 and 0.4. See also
 }
 }
 \examples{
+\dontshow{if (require("parameters") && require("psych")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 # data generation from '?prcomp', slightly modified
 C <- chol(S <- toeplitz(0.9^(0:15)))
 set.seed(17)
 X <- matrix(rnorm(1600), 100, 16)
 Z <- X \%*\% C
-if (require("parameters") && require("psych")) {
-  pca <- principal_components(as.data.frame(Z), rotation = "varimax", n = 3)
-  pca
-  check_itemscale(pca)
-}
+
+pca <- principal_components(as.data.frame(Z), rotation = "varimax", n = 3)
+pca
+check_itemscale(pca)
+\dontshow{\}) # examplesIf}
 }
 \references{
 \itemize{
diff --git a/man/check_multimodal.Rd b/man/check_multimodal.Rd
index 43153734a..a53546a28 100644
--- a/man/check_multimodal.Rd
+++ b/man/check_multimodal.Rd
@@ -19,33 +19,31 @@ it always returns a significant result (suggesting that the distribution is
 multimodal). A better method might be needed here.
 }
 \examples{
+\dontshow{if (require("multimode") && require("mclust")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 \dontrun{
-if (require("multimode")) {
-  # Univariate
-  x <- rnorm(1000)
-  check_multimodal(x)
-}
+# Univariate
+x <- rnorm(1000)
+check_multimodal(x)
 
-if (require("multimode") && require("mclust")) {
-  x <- c(rnorm(1000), rnorm(1000, 2))
-  check_multimodal(x)
+x <- c(rnorm(1000), rnorm(1000, 2))
+check_multimodal(x)
 
-  # Multivariate
-  m <- data.frame(
-    x = rnorm(200),
-    y = rbeta(200, 2, 1)
-  )
-  plot(m$x, m$y)
-  check_multimodal(m)
+# Multivariate
+m <- data.frame(
+  x = rnorm(200),
+  y = rbeta(200, 2, 1)
+)
+plot(m$x, m$y)
+check_multimodal(m)
 
-  m <- data.frame(
-    x = c(rnorm(100), rnorm(100, 4)),
-    y = c(rbeta(100, 2, 1), rbeta(100, 1, 4))
-  )
-  plot(m$x, m$y)
-  check_multimodal(m)
-}
-}
+m <- data.frame(
+  x = c(rnorm(100), rnorm(100, 4)),
+  y = c(rbeta(100, 2, 1), rbeta(100, 1, 4))
+)
+plot(m$x, m$y)
+check_multimodal(m)
+}
+\dontshow{\}) # examplesIf}
 }
 \references{
 \itemize{
diff --git a/man/check_predictions.Rd b/man/check_predictions.Rd
index a2a15ab50..591c813da 100644
--- a/man/check_predictions.Rd
+++ b/man/check_predictions.Rd
@@ -86,26 +86,22 @@ package that imports \strong{bayesplot} such as \strong{rstanarm} or \strong{brm
 is loaded, \code{pp_check()} is also available as an alias for \code{check_predictions()}.
 }
 \examples{
-library(performance)
+\dontshow{if (require("see")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 # linear model
-if (require("see")) {
-  model <- lm(mpg ~ disp, data = mtcars)
-  check_predictions(model)
-}
+model <- lm(mpg ~ disp, data = mtcars)
+check_predictions(model)
 
 # discrete/integer outcome
-if (require("see")) {
-  set.seed(99)
-  d <- iris
-  d$skewed <- rpois(150, 1)
-  model <- glm(
-    skewed ~ Species + Petal.Length + Petal.Width,
-    family = poisson(),
-    data = d
-  )
-  check_predictions(model, type = "discrete_both")
-}
-
+set.seed(99)
+d <- iris
+d$skewed <- rpois(150, 1)
+model <- glm(
+  skewed ~ Species + Petal.Length + Petal.Width,
+  family = poisson(),
+  data = d
+)
+check_predictions(model, type = "discrete_both")
+\dontshow{\}) # examplesIf}
 }
 \references{
 \itemize{
diff --git a/man/check_sphericity.Rd b/man/check_sphericity.Rd
index 531b745a2..6aaa53b3b 100644
--- a/man/check_sphericity.Rd
+++ b/man/check_sphericity.Rd
@@ -20,12 +20,13 @@ Check model for violation of sphericity. For \link[=check_factorstructure]{Bartl
 (used for correlation matrices and factor analyses), see \link{check_sphericity_bartlett}.
 }
 \examples{
-if (require("car")) {
-  soils.mod <- lm(
-    cbind(pH, N, Dens, P, Ca, Mg, K, Na, Conduc) ~ Block + Contour * Depth,
-    data = Soils
-  )
+\dontshow{if (require("car") && require("carData")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+data(Soils, package = "carData")
+soils.mod <- lm(
+  cbind(pH, N, Dens, P, Ca, Mg, K, Na, Conduc) ~ Block + Contour * Depth,
+  data = Soils
+)
 
-  check_sphericity(Manova(soils.mod))
-}
+check_sphericity(Manova(soils.mod))
+\dontshow{\}) # examplesIf}
 }
diff --git a/man/icc.Rd b/man/icc.Rd
index facc40653..d25004e07 100644
--- a/man/icc.Rd
+++ b/man/icc.Rd
@@ -176,28 +176,26 @@ it is negative. In such cases, it might help to use \code{robust = TRUE}.
 }
 }
 \examples{
-if (require("lme4")) {
-  model <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
-  icc(model)
-}
+\dontshow{if (require("lme4")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
+icc(model)
 
 # ICC for specific group-levels
-if (require("lme4")) {
-  data(sleepstudy)
-  set.seed(12345)
-  sleepstudy$grp <- sample(1:5, size = 180, replace = TRUE)
-  sleepstudy$subgrp <- NA
-  for (i in 1:5) {
-    filter_group <- sleepstudy$grp == i
-    sleepstudy$subgrp[filter_group] <-
-      sample(1:30, size = sum(filter_group), replace = TRUE)
-  }
-  model <- lmer(
-    Reaction ~ Days + (1 | grp / subgrp) + (1 | Subject),
-    data = sleepstudy
-  )
-  icc(model, by_group = TRUE)
-}
+data(sleepstudy, package = "lme4")
+set.seed(12345)
+sleepstudy$grp <- sample(1:5, size = 180, replace = TRUE)
+sleepstudy$subgrp <- NA
+for (i in 1:5) {
+  filter_group <- sleepstudy$grp == i
+  sleepstudy$subgrp[filter_group] <-
+    sample(1:30, size = sum(filter_group), replace = TRUE)
+}
+model <- lme4::lmer(
+  Reaction ~ Days + (1 | grp / subgrp) + (1 | Subject),
+  data = sleepstudy
+)
+icc(model, by_group = TRUE)
+\dontshow{\}) # examplesIf}
 }
 \references{
 \itemize{
diff --git a/man/r2_nakagawa.Rd b/man/r2_nakagawa.Rd
index 3bfa31fed..357c2a3e9 100644
--- a/man/r2_nakagawa.Rd
+++ b/man/r2_nakagawa.Rd
@@ -74,11 +74,11 @@ The contribution of random effects can be deduced by subtracting the
 marginal R2 from the conditional R2 or by computing the \code{\link[=icc]{icc()}}.
 }
 \examples{
-if (require("lme4")) {
-  model <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
-  r2_nakagawa(model)
-  r2_nakagawa(model, by_group = TRUE)
-}
+\dontshow{if (require("lme4")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
+r2_nakagawa(model)
+r2_nakagawa(model, by_group = TRUE)
+\dontshow{\}) # examplesIf}
 }
 \references{
 \itemize{