diff --git a/.Rbuildignore b/.Rbuildignore
index aa0489d0..70a566d2 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -18,3 +18,4 @@
 ^revdep$
 ^cran-comments\.md$
 ^CRAN-SUBMISSION$
+^.claude$
diff --git a/.github/workflows/no-suggest-cmd-check.yml b/.github/workflows/no-suggest-cmd-check.yml
index 7a62e61d..4761fc72 100644
--- a/.github/workflows/no-suggest-cmd-check.yml
+++ b/.github/workflows/no-suggest-cmd-check.yml
@@ -1,4 +1,4 @@
-# r cmd check workflow without suggests of the mlr3 ecosystem v0.2.0
+# r cmd check workflow without suggests of the mlr3 ecosystem v0.3.1
 # https://github.com/mlr-org/actions
 on:
   workflow_dispatch:
@@ -33,7 +33,7 @@ jobs:
           - {os: ubuntu-latest,   r: 'release'}
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v5
 
       - uses: r-lib/actions/setup-pandoc@v2
 
@@ -43,9 +43,14 @@ jobs:
 
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::rcmdcheck
+          extra-packages: |
+            any::rcmdcheck
+            any::testthat
+            any::knitr
+            any::rmarkdown
           needs: check
           dependencies: '"hard"'
+          cache: false
 
       - uses: mxschmitt/action-tmate@v3
         if: ${{ github.event_name == 'workflow_dispatch' && inputs.debug_enabled }}
@@ -54,5 +59,4 @@ jobs:
 
       - uses: r-lib/actions/check-r-package@v2
         with:
-          error-on: '"note"'
           args: 'c("--no-manual", "--as-cran")'
diff --git a/DESCRIPTION b/DESCRIPTION
index 9601ab12..c0e65d59 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -26,7 +26,8 @@ License: LGPL-3
 URL: https://mlr3viz.mlr-org.com, https://github.com/mlr-org/mlr3viz
 BugReports: https://github.com/mlr-org/mlr3viz/issues
 Depends:
-    R (>= 3.3.0)
+    R (>= 3.3.0),
+    mlr3 (>= 1.3.0)
 Imports:
     checkmate,
     data.table,
@@ -37,6 +38,7 @@ Imports:
     viridis
 Suggests:
     bbotk (>= 1.0.0),
+    clue,
     cluster,
     GGally,
     ggdendro,
@@ -45,7 +47,6 @@ Suggests:
     glmnet,
     knitr,
     lgr,
-    mlr3 (>= 0.6.0),
     mlr3cluster,
     mlr3filters,
     mlr3fselect (>= 1.3.0),
diff --git a/NAMESPACE b/NAMESPACE
index 376d2250..476c31b3 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -54,6 +54,7 @@ export(plot_learner_prediction)
 import(checkmate)
 import(data.table)
 import(ggplot2)
+import(mlr3)
 import(mlr3misc)
 importFrom(ggplot2,autoplot)
 importFrom(ggplot2,fortify)
diff --git a/R/BenchmarkResult.R b/R/BenchmarkResult.R
index 5ee06be0..2f1ff46a 100644
--- a/R/BenchmarkResult.R
+++ b/R/BenchmarkResult.R
@@ -28,19 +28,15 @@
 #'
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3viz)
+#' if (mlr3misc::require_namespaces("precrec", quietly = TRUE)) {
+#' tasks = tsks(c("pima", "sonar"))
+#' learner = lrns(c("classif.featureless", "classif.rpart"), predict_type = "prob")
+#' resampling = rsmps("cv")
+#' object = benchmark(benchmark_grid(tasks, learner, resampling))
 #'
-#'   tasks = tsks(c("pima", "sonar"))
-#'   learner = lrns(c("classif.featureless", "classif.rpart"),
-#'     predict_type = "prob")
-#'   resampling = rsmps("cv")
-#'   object = benchmark(benchmark_grid(tasks, learner, resampling))
-#'
-#'   head(fortify(object))
-#'   autoplot(object)
-#'   autoplot(object$clone(deep = TRUE)$filter(task_ids = "pima"), type = "roc")
+#' head(fortify(object))
+#' autoplot(object)
+#' autoplot(object$clone(deep = TRUE)$filter(task_ids = "pima"), type = "roc")
 #' }
 autoplot.BenchmarkResult = function(object, type = "boxplot", measure = NULL, theme = theme_minimal(), ...) {
   assert_choice(type, choices = c("boxplot", "roc", "prc", "ci"), null.ok = FALSE)
diff --git a/R/EnsembleFSResult.R b/R/EnsembleFSResult.R
index 905258f1..8d32045f 100644
--- a/R/EnsembleFSResult.R
+++ b/R/EnsembleFSResult.R
@@ -35,12 +35,11 @@
 #'
 #' @return [ggplot2::ggplot()].
 #' @examples
+#' if (mlr3misc::require_namespaces("mlr3fselect", quietly = TRUE)) {
 #' \donttest{
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
+#'   set.seed (42)
 #'   library(mlr3fselect)
 #'
-#'   set.seed (42)
 #'   efsr = ensemble_fselect(
 #'     fselector = fs("random_search"),
 #'     task = tsk("sonar"),
diff --git a/R/Filter.R b/R/Filter.R
index 83146dfd..7f6927c9 100644
--- a/R/Filter.R
+++ b/R/Filter.R
@@ -18,17 +18,15 @@
 #' @return [ggplot2::ggplot()].
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3viz)
-#'   library(mlr3filters)
+#' if (mlr3misc::require_namespaces("mlr3filters", quietly = TRUE)) {
+#' library(mlr3filters)
 #'
-#'   task = tsk("mtcars")
-#'   f = flt("correlation")
-#'   f$calculate(task)
+#' task = tsk("mtcars")
+#' f = flt("correlation")
+#' f$calculate(task)
 #'
-#'   head(fortify(f))
-#'   autoplot(f, n = 5)
+#' head(fortify(f))
+#' autoplot(f, n = 5)
 #' }
 autoplot.Filter = function(object, type = "boxplot", n = Inf, theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("boxplot"), null.ok = FALSE)
diff --git a/R/LearnerClassif.R b/R/LearnerClassif.R
index 797427a4..16534fae 100644
--- a/R/LearnerClassif.R
+++ b/R/LearnerClassif.R
@@ -21,17 +21,12 @@
 #' @export
 #' @examples
 #' \donttest{
-#' if (requireNamespace("mlr3")) {
-#' library(mlr3)
-#' library(mlr3viz)
-#'
 #' task = tsk("pima")$select(c("age", "pedigree"))
 #' learner = lrn("classif.rpart", predict_type = "prob")
 #' learner$train(task)
 #'
 #' autoplot(learner, type = "prediction", task)
 #' }
-#' }
 autoplot.LearnerClassif = function(object, type = "prediction", task, grid_points = 100L, expand_range = 0, theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("prediction"), null.ok = FALSE)
 
diff --git a/R/LearnerClassifGlmnet.R b/R/LearnerClassifGlmnet.R
index 82af84f7..6d870c6f 100644
--- a/R/LearnerClassifGlmnet.R
+++ b/R/LearnerClassifGlmnet.R
@@ -24,9 +24,8 @@
 #'
 #' @export
 #' @examples
+#' if (mlr3misc::require_namespaces("mlr3learners", quietly = TRUE)) {
 #' \dontrun{
-#' library(mlr3)
-#' library(mlr3viz)
 #' library(mlr3learners)
 #'
 #' # classification
@@ -41,6 +40,7 @@
 #' learner$train(task)
 #' autoplot(learner, type = "ggfortify")
 #' }
+#' }
 autoplot.LearnerClassifGlmnet = function(object, type = "prediction", task = NULL, grid_points = 100L, expand_range = 0, theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("prediction", "ggfortify"), null.ok = FALSE)
   assert_has_model(object)
diff --git a/R/LearnerClassifRpart.R b/R/LearnerClassifRpart.R
index f2b588c9..523a0cd0 100644
--- a/R/LearnerClassifRpart.R
+++ b/R/LearnerClassifRpart.R
@@ -21,21 +21,18 @@
 #'
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3viz)
+#' if (mlr3misc::require_namespaces(c("partykit", "ggparty"), quietly = TRUE)) {
+#' # classification
+#' task = tsk("iris")
+#' learner = lrn("classif.rpart", keep_model = TRUE)
+#' learner$train(task)
+#' autoplot(learner, type = "ggparty")
 #'
-#'   # classification
-#'   task = tsk("iris")
-#'   learner = lrn("classif.rpart", keep_model = TRUE)
-#'   learner$train(task)
-#'   autoplot(learner, type = "ggparty")
-#'
-#'   # regression
-#'   task = tsk("mtcars")
-#'   learner = lrn("regr.rpart", keep_model = TRUE)
-#'   learner$train(task)
-#'   autoplot(learner, type = "ggparty")
+#' # regression
+#' task = tsk("mtcars")
+#' learner = lrn("regr.rpart", keep_model = TRUE)
+#' learner$train(task)
+#' autoplot(learner, type = "ggparty")
 #' }
 autoplot.LearnerClassifRpart = function(object, type = "prediction", task = NULL, grid_points = 100L, expand_range = 0, theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("prediction", "ggparty"), null.ok = FALSE)
diff --git a/R/LearnerClustHierarchical.R b/R/LearnerClustHierarchical.R
index 1b77516e..5ba9bcb6 100644
--- a/R/LearnerClustHierarchical.R
+++ b/R/LearnerClustHierarchical.R
@@ -23,27 +23,25 @@
 #'
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3cluster)
-#'   library(mlr3viz)
+#' if (mlr3misc::require_namespaces("mlr3cluster", quietly = TRUE)) {
+#' library(mlr3cluster)
 #'
-#'   task = tsk("usarrests")
+#' task = tsk("usarrests")
 #'
-#'   # agnes clustering
-#'   learner = lrn("clust.agnes")
-#'   learner$train(task)
-#'   autoplot(learner)
+#' # agnes clustering
+#' learner = lrn("clust.agnes")
+#' learner$train(task)
+#' autoplot(learner)
 #'
-#'   # diana clustering
-#'   learner = lrn("clust.diana")
-#'   learner$train(task)
-#'   autoplot(learner)
+#' # diana clustering
+#' learner = lrn("clust.diana")
+#' learner$train(task)
+#' autoplot(learner)
 #'
-#'   # hclust clustering
-#'   learner = lrn("clust.hclust")
-#'   learner$train(task)
-#'   autoplot(learner, type = "scree")
+#' # hclust clustering
+#' learner = lrn("clust.hclust")
+#' learner$train(task)
+#' autoplot(learner, type = "scree")
 #' }
 autoplot.LearnerClustHierarchical = function(object, type = "dend", task = NULL, theme = theme_minimal(), theme_dendro = TRUE, ...) { # nolint
   assert_choice(type, choices = c("dend", "scree"), null.ok = FALSE)
diff --git a/R/LearnerRegr.R b/R/LearnerRegr.R
index 32b51869..9ba8b94d 100644
--- a/R/LearnerRegr.R
+++ b/R/LearnerRegr.R
@@ -21,17 +21,12 @@
 #' @export
 #' @examples
 #' \donttest{
-#' if (requireNamespace("mlr3")) {
-#' library(mlr3)
-#' library(mlr3viz)
-#'
 #' task = tsk("mtcars")$select(c("am", "carb"))
 #' learner = lrn("regr.rpart")
 #' learner$train(task)
 #'
 #' autoplot(learner, type = "prediction", task)
 #' }
-#' }
 autoplot.LearnerRegr = function(object, type = "prediction", task, grid_points = 100L, expand_range = 0, theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("prediction"), null.ok = FALSE)
 
diff --git a/R/OptimInstanceBatchSingleCrit.R b/R/OptimInstanceBatchSingleCrit.R
index 3f3e57e6..bde32267 100644
--- a/R/OptimInstanceBatchSingleCrit.R
+++ b/R/OptimInstanceBatchSingleCrit.R
@@ -44,45 +44,44 @@
 #'
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3") && requireNamespace("bbotk") && requireNamespace("patchwork")) {
-#'   library(bbotk)
-#'   library(paradox)
+#' if (mlr3misc::require_namespaces(c("paradox", "bbotk", "patchwork"), quietly = TRUE)) {
+#' library(bbotk)
 #'
-#'   fun = function(xs) {
-#'     c(y = -(xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
-#'   }
-#'   domain = ps(
-#'     x1 = p_dbl(-10, 10),
-#'     x2 = p_dbl(-5, 5)
-#'   )
-#'   codomain = ps(
-#'     y = p_dbl(tags = "maximize")
-#'   )
-#'   obfun = ObjectiveRFun$new(
-#'     fun = fun,
-#'     domain = domain,
-#'     codomain = codomain
-#'   )
+#' fun = function(xs) {
+#'   c(y = -(xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
+#' }
+#' domain = ps(
+#'   x1 = p_dbl(-10, 10),
+#'   x2 = p_dbl(-5, 5)
+#' )
+#' codomain = ps(
+#'   y = p_dbl(tags = "maximize")
+#' )
+#' obfun = ObjectiveRFun$new(
+#'   fun = fun,
+#'   domain = domain,
+#'   codomain = codomain
+#' )
 #'
-#'   instance = oi(objective = obfun, terminator = trm("evals", n_evals = 20))
+#' instance = oi(objective = obfun, terminator = trm("evals", n_evals = 20))
 #'
-#'   optimizer = opt("random_search", batch_size = 2)
-#'   optimizer$optimize(instance)
+#' optimizer = opt("random_search", batch_size = 2)
+#' optimizer$optimize(instance)
 #'
-#'   # plot y versus batch number
-#'   print(autoplot(instance, type = "performance"))
+#' # plot y versus batch number
+#' print(autoplot(instance, type = "performance"))
 #'
-#'   # plot x1 values versus performance
-#'   print(autoplot(instance, type = "marginal", cols_x = "x1"))
+#' # plot x1 values versus performance
+#' print(autoplot(instance, type = "marginal", cols_x = "x1"))
 #'
-#'   # plot parallel coordinates plot
-#'   print(autoplot(instance, type = "parallel"))
+#' # plot parallel coordinates plot
+#' print(autoplot(instance, type = "parallel"))
 #'
-#'   # plot pairs
-#'   print(autoplot(instance, type = "pairs"))
+#' # plot pairs
+#' print(autoplot(instance, type = "pairs"))
 #'
-#'   # plot incumbent
-#'   print(autoplot(instance, type = "incumbent"))
+#' # plot incumbent
+#' print(autoplot(instance, type = "incumbent"))
 #' }
 autoplot.OptimInstanceBatchSingleCrit = function(object, type = "marginal", cols_x = NULL, trafo = FALSE, learner = mlr3::lrn("regr.ranger"), grid_resolution = 100, batch = NULL, theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("marginal", "performance", "parameter", "parallel",
diff --git a/R/PredictionClassif.R b/R/PredictionClassif.R
index 0b271440..36389de4 100644
--- a/R/PredictionClassif.R
+++ b/R/PredictionClassif.R
@@ -26,18 +26,15 @@
 #' @export
 #' @examples
 #' \donttest{
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3viz)
+#' if (mlr3misc::require_namespaces("precrec", quietly = TRUE)) {
+#' task = tsk("spam")
+#' learner = lrn("classif.rpart", predict_type = "prob")
+#' object = learner$train(task)$predict(task)
 #'
-#'   task = tsk("spam")
-#'   learner = lrn("classif.rpart", predict_type = "prob")
-#'   object = learner$train(task)$predict(task)
-#'
-#'   head(fortify(object))
-#'   autoplot(object)
-#'   autoplot(object, type = "roc")
-#'   autoplot(object, type = "prc")
+#' head(fortify(object))
+#' autoplot(object)
+#' autoplot(object, type = "roc")
+#' autoplot(object, type = "prc")
 #' }
 #' }
 autoplot.PredictionClassif = function(object, type = "stacked", measure = NULL, theme = theme_minimal(), ...) { # nolint
diff --git a/R/PredictionClust.R b/R/PredictionClust.R
index 6ebb76b1..b8580df2 100644
--- a/R/PredictionClust.R
+++ b/R/PredictionClust.R
@@ -26,17 +26,15 @@
 #'
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3cluster)
-#'   library(mlr3viz)
+#' if (mlr3misc::require_namespaces(c("clue", "mlr3cluster"), quietly = TRUE)) {
+#' library(mlr3cluster)
 #'
-#'   task = tsk("usarrests")
-#'   learner = lrn("clust.kmeans", centers = 3)
-#'   object = learner$train(task)$predict(task)
+#' task = tsk("usarrests")
+#' learner = lrn("clust.kmeans", centers = 3)
+#' object = learner$train(task)$predict(task)
 #'
-#'   head(fortify(object))
-#'   autoplot(object, task)
+#' head(fortify(object))
+#' autoplot(object, task)
 #' }
 autoplot.PredictionClust = function(object, task, row_ids = NULL, type = "scatter", theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("scatter", "sil", "pca"), null.ok = FALSE)
diff --git a/R/PredictionRegr.R b/R/PredictionRegr.R
index 072ea24e..ea99df66 100644
--- a/R/PredictionRegr.R
+++ b/R/PredictionRegr.R
@@ -29,25 +29,21 @@
 #'
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3viz)
+#' if (mlr3misc::require_namespaces("mlr3learners", quietly = TRUE)) {
+#' library(mlr3learners)
 #'
-#'   task = tsk("mtcars")
-#'   learner = lrn("regr.rpart")
-#'   object = learner$train(task)$predict(task)
+#' task = tsk("mtcars")
+#' learner = lrn("regr.rpart")
+#' object = learner$train(task)$predict(task)
 #'
-#'   head(fortify(object))
-#'   autoplot(object)
-#'   autoplot(object, type = "histogram", binwidth = 1)
-#'   autoplot(object, type = "residual")
+#' head(fortify(object))
+#' autoplot(object)
+#' autoplot(object, type = "histogram", binwidth = 1)
+#' autoplot(object, type = "residual")
 #'
-#'  if (requireNamespace("mlr3learners")) {
-#'   library(mlr3learners)
-#'   learner = lrn("regr.ranger", predict_type = "se")
-#'   object = learner$train(task)$predict(task)
-#'   autoplot(object, type = "confidence")
-#'  }
+#' learner = lrn("regr.ranger", predict_type = "se")
+#' object = learner$train(task)$predict(task)
+#' autoplot(object, type = "confidence")
 #' }
 autoplot.PredictionRegr = function(object, type = "xy", binwidth = NULL, theme = theme_minimal(), quantile = 1.96, ...) {
   assert_choice(type, choices = c("xy", "histogram", "residual", "confidence"), null.ok = FALSE)
diff --git a/R/ResampleResult.R b/R/ResampleResult.R
index 5dfb51b2..47614e93 100644
--- a/R/ResampleResult.R
+++ b/R/ResampleResult.R
@@ -40,37 +40,34 @@
 #' @export
 #' @examples
 #' \donttest{
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3viz)
+#' if (mlr3misc::require_namespaces("precrec", quietly = TRUE)) {
+#' task = tsk("sonar")
+#' learner = lrn("classif.rpart", predict_type = "prob")
+#' resampling = rsmp("cv", folds = 3)
+#' object = resample(task, learner, resampling)
 #'
-#'   task = tsk("sonar")
-#'   learner = lrn("classif.rpart", predict_type = "prob")
-#'   resampling = rsmp("cv", folds = 3)
-#'   object = resample(task, learner, resampling)
+#' head(fortify(object))
 #'
-#'   head(fortify(object))
+#' # Default: boxplot
+#' autoplot(object)
 #'
-#'   # Default: boxplot
-#'   autoplot(object)
+#' # Histogram
+#' autoplot(object, type = "histogram", bins = 30)
 #'
-#'   # Histogram
-#'   autoplot(object, type = "histogram", bins = 30)
+#' # ROC curve, averaged over resampling folds:
+#' autoplot(object, type = "roc")
 #'
-#'   # ROC curve, averaged over resampling folds:
-#'   autoplot(object, type = "roc")
+#' # ROC curve of joint prediction object:
+#' autoplot(object$prediction(), type = "roc")
 #'
-#'   # ROC curve of joint prediction object:
-#'   autoplot(object$prediction(), type = "roc")
+#' # Precision Recall Curve
+#' autoplot(object, type = "prc")
 #'
-#'   # Precision Recall Curve
-#'   autoplot(object, type = "prc")
-#'
-#'   # Prediction Plot
-#'   task = tsk("iris")$select(c("Sepal.Length", "Sepal.Width"))
-#'   resampling = rsmp("cv", folds = 3)
-#'   object = resample(task, learner, resampling, store_models = TRUE)
-#'   autoplot(object, type = "prediction")
+#' # Prediction Plot
+#' task = tsk("iris")$select(c("Sepal.Length", "Sepal.Width"))
+#' resampling = rsmp("cv", folds = 3)
+#' object = resample(task, learner, resampling, store_models = TRUE)
+#' autoplot(object, type = "prediction")
 #' }
 #' }
 autoplot.ResampleResult = function(object, type = "boxplot", measure = NULL, predict_sets = "test", binwidth = NULL, theme = theme_minimal(), ...) {
diff --git a/R/TaskClassif.R b/R/TaskClassif.R
index b13e55ac..e4703eb4 100644
--- a/R/TaskClassif.R
+++ b/R/TaskClassif.R
@@ -20,17 +20,13 @@
 #'
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3viz)
+#' if (mlr3misc::require_namespaces("GGally", quietly = TRUE)) {
+#' task = tsk("iris")
 #'
-#'   task = tsk("iris")
-#'
-#'   head(fortify(task))
-#'   autoplot(task)
-#'   autoplot(task$clone()$select(c("Sepal.Length", "Sepal.Width")),
-#'     type = "pairs")
-#'   autoplot(task, type = "duo")
+#' head(fortify(task))
+#' autoplot(task)
+#' autoplot(task$clone()$select(c("Sepal.Length", "Sepal.Width")), type = "pairs")
+#' autoplot(task, type = "duo")
 #' }
 autoplot.TaskClassif = function(object, type = "target", theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("target", "duo", "pairs"), null.ok = FALSE)
diff --git a/R/TaskClust.R b/R/TaskClust.R
index f289a6cf..174798d5 100644
--- a/R/TaskClust.R
+++ b/R/TaskClust.R
@@ -16,15 +16,12 @@
 #'
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3cluster)
-#'   library(mlr3viz)
+#' if (mlr3misc::require_namespaces("mlr3cluster", quietly = TRUE)) {
+#' library(mlr3cluster)
+#' task = tsk("usarrests")
 #'
-#'   task = mlr_tasks$get("usarrests")
-#'
-#'   head(fortify(task))
-#'   autoplot(task)
+#' head(fortify(task))
+#' autoplot(task)
 #' }
 autoplot.TaskClust = function(object, type = "pairs", theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("pairs"), null.ok = FALSE)
diff --git a/R/TaskRegr.R b/R/TaskRegr.R
index 60597b9a..82efefde 100644
--- a/R/TaskRegr.R
+++ b/R/TaskRegr.R
@@ -18,16 +18,13 @@
 #'
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3")) {
-#'   library(mlr3)
-#'   library(mlr3viz)
+#' if (mlr3misc::require_namespaces("GGally", quietly = TRUE)) {
+#' task = tsk("mtcars")
+#' task$select(c("am", "carb"))
 #'
-#'   task = tsk("mtcars")
-#'   task$select(c("am", "carb"))
-#'
-#'   head(fortify(task))
-#'   autoplot(task)
-#'   autoplot(task, type = "pairs")
+#' head(fortify(task))
+#' autoplot(task)
+#' autoplot(task, type = "pairs")
 #' }
 autoplot.TaskRegr = function(object, type = "target", theme = theme_minimal(), ...) { # nolint
   assert_choice(type, choices = c("target", "pairs"), null.ok = FALSE)
diff --git a/R/TuningInstanceBatchSingleCrit.R b/R/TuningInstanceBatchSingleCrit.R
index 0d0de20d..6070c00a 100644
--- a/R/TuningInstanceBatchSingleCrit.R
+++ b/R/TuningInstanceBatchSingleCrit.R
@@ -39,38 +39,38 @@
 #'
 #' @export
 #' @examples
-#' if (requireNamespace("mlr3tuning") && requireNamespace("patchwork")) {
-#'   library(mlr3tuning)
+#' if (mlr3misc::require_namespaces(c("mlr3tuning", "patchwork", "paradox"), quietly = TRUE)) {
+#' library(mlr3tuning)
 #'
-#'   learner = lrn("classif.rpart")
-#'   learner$param_set$values$cp = to_tune(0.001, 0.1)
-#'   learner$param_set$values$minsplit = to_tune(1, 10)
+#' learner = lrn("classif.rpart")
+#' learner$param_set$values$cp = to_tune(0.001, 0.1)
+#' learner$param_set$values$minsplit = to_tune(1, 10)
 #'
-#'   instance = ti(
-#'     task = tsk("iris"),
-#'     learner = learner,
-#'     resampling = rsmp("holdout"),
-#'     measure = msr("classif.ce"),
-#'     terminator = trm("evals", n_evals = 10))
+#' instance = ti(
+#'   task = tsk("iris"),
+#'   learner = learner,
+#'   resampling = rsmp("holdout"),
+#'   measure = msr("classif.ce"),
+#'   terminator = trm("evals", n_evals = 10))
 #'
-#'   tuner = tnr("random_search")
+#' tuner = tnr("random_search")
 #'
-#'   tuner$optimize(instance)
+#' tuner$optimize(instance)
 #'
-#'   # plot performance versus batch number
-#'   autoplot(instance, type = "performance")
+#' # plot performance versus batch number
+#' autoplot(instance, type = "performance")
 #'
-#'   # plot cp values versus performance
-#'   autoplot(instance, type = "marginal", cols_x = "cp")
+#' # plot cp values versus performance
+#' autoplot(instance, type = "marginal", cols_x = "cp")
 #'
-#'   # plot transformed parameter values versus batch number
-#'   autoplot(instance, type = "parameter", trafo = TRUE)
+#' # plot transformed parameter values versus batch number
+#' autoplot(instance, type = "parameter", trafo = TRUE)
 #'
-#'   # plot parallel coordinates plot
-#'   autoplot(instance, type = "parallel")
+#' # plot parallel coordinates plot
+#' autoplot(instance, type = "parallel")
 #'
-#'   # plot pairs
-#'   autoplot(instance, type = "pairs")
+#' # plot pairs
+#' autoplot(instance, type = "pairs")
 #' }
 autoplot.TuningInstanceBatchSingleCrit = function(object, type = "marginal", cols_x = NULL, trafo = FALSE, learner = mlr3::lrn("regr.ranger"), grid_resolution = 100, theme = theme_minimal(), ...) {
   autoplot.OptimInstanceBatchSingleCrit(object = object, type = type, cols_x = cols_x, trafo = trafo, learner = learner, grid_resolution = grid_resolution, theme = theme, ...)
diff --git a/R/zzz.R b/R/zzz.R
index a3f1a54a..e3b078bb 100644
--- a/R/zzz.R
+++ b/R/zzz.R
@@ -2,6 +2,7 @@
 #' @import checkmate
 #' @import data.table
 #' @import ggplot2
+#' @import mlr3
 #' @importFrom utils head
 #' @importFrom graphics plot
 #' @importFrom stats sd as.dendrogram
diff --git a/man/autoplot.BenchmarkResult.Rd b/man/autoplot.BenchmarkResult.Rd
index db2f9b34..811fecf3 100644
--- a/man/autoplot.BenchmarkResult.Rd
+++ b/man/autoplot.BenchmarkResult.Rd
@@ -46,19 +46,15 @@ See \code{"roc"}.
 }
 }
 \examples{
-if (requireNamespace("mlr3")) {
-  library(mlr3)
-  library(mlr3viz)
+if (mlr3misc::require_namespaces("precrec", quietly = TRUE)) {
+tasks = tsks(c("pima", "sonar"))
+learner = lrns(c("classif.featureless", "classif.rpart"), predict_type = "prob")
+resampling = rsmps("cv")
+object = benchmark(benchmark_grid(tasks, learner, resampling))
 
-  tasks = tsks(c("pima", "sonar"))
-  learner = lrns(c("classif.featureless", "classif.rpart"),
-    predict_type = "prob")
-  resampling = rsmps("cv")
-  object = benchmark(benchmark_grid(tasks, learner, resampling))
-
-  head(fortify(object))
-  autoplot(object)
-  autoplot(object$clone(deep = TRUE)$filter(task_ids = "pima"), type = "roc")
+head(fortify(object))
+autoplot(object)
+autoplot(object$clone(deep = TRUE)$filter(task_ids = "pima"), type = "roc")
 }
 }
 \references{
diff --git a/man/autoplot.EnsembleFSResult.Rd b/man/autoplot.EnsembleFSResult.Rd
index ff710fa4..14efe40f 100644
--- a/man/autoplot.EnsembleFSResult.Rd
+++ b/man/autoplot.EnsembleFSResult.Rd
@@ -61,12 +61,11 @@ sets from each learner across the different resamplings.
 }
 }
 \examples{
+if (mlr3misc::require_namespaces("mlr3fselect", quietly = TRUE)) {
 \donttest{
-if (requireNamespace("mlr3")) {
-  library(mlr3)
+  set.seed (42)
   library(mlr3fselect)
 
-  set.seed (42)
   efsr = ensemble_fselect(
     fselector = fs("random_search"),
     task = tsk("sonar"),
diff --git a/man/autoplot.Filter.Rd b/man/autoplot.Filter.Rd
index d5487d0e..38aee598 100644
--- a/man/autoplot.Filter.Rd
+++ b/man/autoplot.Filter.Rd
@@ -33,16 +33,14 @@ Possible choices are:
 }
 }
 \examples{
-if (requireNamespace("mlr3")) {
-  library(mlr3)
-  library(mlr3viz)
-  library(mlr3filters)
+if (mlr3misc::require_namespaces("mlr3filters", quietly = TRUE)) {
+library(mlr3filters)
 
-  task = tsk("mtcars")
-  f = flt("correlation")
-  f$calculate(task)
+task = tsk("mtcars")
+f = flt("correlation")
+f$calculate(task)
 
-  head(fortify(f))
-  autoplot(f, n = 5)
+head(fortify(f))
+autoplot(f, n = 5)
 }
 }
diff --git a/man/autoplot.LearnerClassif.Rd b/man/autoplot.LearnerClassif.Rd
index 451f1d3e..e03baac4 100644
--- a/man/autoplot.LearnerClassif.Rd
+++ b/man/autoplot.LearnerClassif.Rd
@@ -47,10 +47,6 @@ Possible choices are:
 }
 \examples{
 \donttest{
-if (requireNamespace("mlr3")) {
-library(mlr3)
-library(mlr3viz)
-
 task = tsk("pima")$select(c("age", "pedigree"))
 learner = lrn("classif.rpart", predict_type = "prob")
 learner$train(task)
@@ -58,4 +54,3 @@ learner$train(task)
 autoplot(learner, type = "prediction", task)
 }
 }
-}
diff --git a/man/autoplot.LearnerClassifGlmnet.Rd b/man/autoplot.LearnerClassifGlmnet.Rd
index 1d4e53a3..27e2260d 100644
--- a/man/autoplot.LearnerClassifGlmnet.Rd
+++ b/man/autoplot.LearnerClassifGlmnet.Rd
@@ -81,9 +81,8 @@ Possible choices are:
 }
 }
 \examples{
+if (mlr3misc::require_namespaces("mlr3learners", quietly = TRUE)) {
 \dontrun{
-library(mlr3)
-library(mlr3viz)
 library(mlr3learners)
 
 # classification
@@ -99,6 +98,7 @@ learner$train(task)
 autoplot(learner, type = "ggfortify")
 }
 }
+}
 \references{
 Tang Y, Horikoshi M, Li W (2016).
 \dQuote{ggfortify: Unified Interface to Visualize Statistical Result of Popular R Packages.}
diff --git a/man/autoplot.LearnerClassifRpart.Rd b/man/autoplot.LearnerClassifRpart.Rd
index db145f51..85293a33 100644
--- a/man/autoplot.LearnerClassifRpart.Rd
+++ b/man/autoplot.LearnerClassifRpart.Rd
@@ -58,20 +58,17 @@ Possible choices are:
 }
 }
 \examples{
-if (requireNamespace("mlr3")) {
-  library(mlr3)
-  library(mlr3viz)
+if (mlr3misc::require_namespaces(c("partykit", "ggparty"), quietly = TRUE)) {
+# classification
+task = tsk("iris")
+learner = lrn("classif.rpart", keep_model = TRUE)
+learner$train(task)
+autoplot(learner, type = "ggparty")
 
-  # classification
-  task = tsk("iris")
-  learner = lrn("classif.rpart", keep_model = TRUE)
-  learner$train(task)
-  autoplot(learner, type = "ggparty")
-
-  # regression
-  task = tsk("mtcars")
-  learner = lrn("regr.rpart", keep_model = TRUE)
-  learner$train(task)
-  autoplot(learner, type = "ggparty")
+# regression
+task = tsk("mtcars")
+learner = lrn("regr.rpart", keep_model = TRUE)
+learner$train(task)
+autoplot(learner, type = "ggparty")
 }
 }
diff --git a/man/autoplot.LearnerClustHierarchical.Rd b/man/autoplot.LearnerClustHierarchical.Rd
index 0e6b885b..cc647cc2 100644
--- a/man/autoplot.LearnerClustHierarchical.Rd
+++ b/man/autoplot.LearnerClustHierarchical.Rd
@@ -45,26 +45,24 @@ Possible choices are:
 }
 }
 \examples{
-if (requireNamespace("mlr3")) {
-  library(mlr3)
-  library(mlr3cluster)
-  library(mlr3viz)
+if (mlr3misc::require_namespaces("mlr3cluster", quietly = TRUE)) {
+library(mlr3cluster)
 
-  task = tsk("usarrests")
+task = tsk("usarrests")
 
-  # agnes clustering
-  learner = lrn("clust.agnes")
-  learner$train(task)
-  autoplot(learner)
+# agnes clustering
+learner = lrn("clust.agnes")
+learner$train(task)
+autoplot(learner)
 
-  # diana clustering
-  learner = lrn("clust.diana")
-  learner$train(task)
-  autoplot(learner)
+# diana clustering
+learner = lrn("clust.diana")
+learner$train(task)
+autoplot(learner)
 
-  # hclust clustering
-  learner = lrn("clust.hclust")
-  learner$train(task)
-  autoplot(learner, type = "scree")
+# hclust clustering
+learner = lrn("clust.hclust")
+learner$train(task)
+autoplot(learner, type = "scree")
 }
 }
diff --git a/man/autoplot.LearnerRegr.Rd b/man/autoplot.LearnerRegr.Rd
index 8a6dff44..3462382c 100644
--- a/man/autoplot.LearnerRegr.Rd
+++ b/man/autoplot.LearnerRegr.Rd
@@ -47,10 +47,6 @@ Possible choices are:
 }
 \examples{
 \donttest{
-if (requireNamespace("mlr3")) {
-library(mlr3)
-library(mlr3viz)
-
 task = tsk("mtcars")$select(c("am", "carb"))
 learner = lrn("regr.rpart")
 learner$train(task)
@@ -58,4 +54,3 @@ learner$train(task)
 autoplot(learner, type = "prediction", task)
 }
 }
-}
diff --git a/man/autoplot.OptimInstanceBatchSingleCrit.Rd b/man/autoplot.OptimInstanceBatchSingleCrit.Rd
index 4eb081ce..92b672d5 100644
--- a/man/autoplot.OptimInstanceBatchSingleCrit.Rd
+++ b/man/autoplot.OptimInstanceBatchSingleCrit.Rd
@@ -70,44 +70,43 @@ The y values are interpolated with the supplied \link[mlr3:Learner]{mlr3::Learne
 }
 }
 \examples{
-if (requireNamespace("mlr3") && requireNamespace("bbotk") && requireNamespace("patchwork")) {
-  library(bbotk)
-  library(paradox)
-
-  fun = function(xs) {
-    c(y = -(xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
-  }
-  domain = ps(
-    x1 = p_dbl(-10, 10),
-    x2 = p_dbl(-5, 5)
-  )
-  codomain = ps(
-    y = p_dbl(tags = "maximize")
-  )
-  obfun = ObjectiveRFun$new(
-    fun = fun,
-    domain = domain,
-    codomain = codomain
-  )
-
-  instance = oi(objective = obfun, terminator = trm("evals", n_evals = 20))
-
-  optimizer = opt("random_search", batch_size = 2)
-  optimizer$optimize(instance)
-
-  # plot y versus batch number
-  print(autoplot(instance, type = "performance"))
-
-  # plot x1 values versus performance
-  print(autoplot(instance, type = "marginal", cols_x = "x1"))
-
-  # plot parallel coordinates plot
-  print(autoplot(instance, type = "parallel"))
-
-  # plot pairs
-  print(autoplot(instance, type = "pairs"))
-
-  # plot incumbent
-  print(autoplot(instance, type = "incumbent"))
+if (mlr3misc::require_namespaces(c("paradox", "bbotk", "patchwork"), quietly = TRUE)) {
+library(bbotk)
+
+fun = function(xs) {
+  c(y = -(xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
+}
+domain = ps(
+  x1 = p_dbl(-10, 10),
+  x2 = p_dbl(-5, 5)
+)
+codomain = ps(
+  y = p_dbl(tags = "maximize")
+)
+obfun = ObjectiveRFun$new(
+  fun = fun,
+  domain = domain,
+  codomain = codomain
+)
+
+instance = oi(objective = obfun, terminator = trm("evals", n_evals = 20))
+
+optimizer = opt("random_search", batch_size = 2)
+optimizer$optimize(instance)
+
+# plot y versus batch number
+print(autoplot(instance, type = "performance"))
+
+# plot x1 values versus performance
+print(autoplot(instance, type = "marginal", cols_x = "x1"))
+
+# plot parallel coordinates plot
+print(autoplot(instance, type = "parallel"))
+
+# plot pairs
+print(autoplot(instance, type = "pairs"))
+
+# plot incumbent
+print(autoplot(instance, type = "incumbent"))
 }
 }
diff --git a/man/autoplot.PredictionClassif.Rd b/man/autoplot.PredictionClassif.Rd
index 9bf5dc2f..99b53ccf 100644
--- a/man/autoplot.PredictionClassif.Rd
+++ b/man/autoplot.PredictionClassif.Rd
@@ -44,18 +44,15 @@ Requires package \CRANpkg{precrec}.
 }
 \examples{
 \donttest{
-if (requireNamespace("mlr3")) {
-  library(mlr3)
-  library(mlr3viz)
+if (mlr3misc::require_namespaces("precrec", quietly = TRUE)) {
+task = tsk("spam")
+learner = lrn("classif.rpart", predict_type = "prob")
+object = learner$train(task)$predict(task)
 
-  task = tsk("spam")
-  learner = lrn("classif.rpart", predict_type = "prob")
-  object = learner$train(task)$predict(task)
-
-  head(fortify(object))
-  autoplot(object)
-  autoplot(object, type = "roc")
-  autoplot(object, type = "prc")
+head(fortify(object))
+autoplot(object)
+autoplot(object, type = "roc")
+autoplot(object, type = "prc")
 }
 }
 }
diff --git a/man/autoplot.PredictionClust.Rd b/man/autoplot.PredictionClust.Rd
index c493f251..782fd5a8 100644
--- a/man/autoplot.PredictionClust.Rd
+++ b/man/autoplot.PredictionClust.Rd
@@ -45,17 +45,15 @@ Inspired by and uses \link[ggfortify:autoplot.kmeans]{ggfortify::autoplot.kmeans
 }
 }
 \examples{
-if (requireNamespace("mlr3")) {
-  library(mlr3)
-  library(mlr3cluster)
-  library(mlr3viz)
+if (mlr3misc::require_namespaces(c("clue", "mlr3cluster"), quietly = TRUE)) {
+library(mlr3cluster)
 
-  task = tsk("usarrests")
-  learner = lrn("clust.kmeans", centers = 3)
-  object = learner$train(task)$predict(task)
+task = tsk("usarrests")
+learner = lrn("clust.kmeans", centers = 3)
+object = learner$train(task)$predict(task)
 
-  head(fortify(object))
-  autoplot(object, task)
+head(fortify(object))
+autoplot(object, task)
 }
 }
 \references{
diff --git a/man/autoplot.PredictionRegr.Rd b/man/autoplot.PredictionRegr.Rd
index ad67a7ad..1302609b 100644
--- a/man/autoplot.PredictionRegr.Rd
+++ b/man/autoplot.PredictionRegr.Rd
@@ -51,24 +51,20 @@ possible with \code{predict_type = "se"}. \code{geom_abline()} with \code{slope
 }
 }
 \examples{
-if (requireNamespace("mlr3")) {
-  library(mlr3)
-  library(mlr3viz)
+if (mlr3misc::require_namespaces("mlr3learners", quietly = TRUE)) {
+library(mlr3learners)
 
-  task = tsk("mtcars")
-  learner = lrn("regr.rpart")
-  object = learner$train(task)$predict(task)
+task = tsk("mtcars")
+learner = lrn("regr.rpart")
+object = learner$train(task)$predict(task)
 
-  head(fortify(object))
-  autoplot(object)
-  autoplot(object, type = "histogram", binwidth = 1)
-  autoplot(object, type = "residual")
+head(fortify(object))
+autoplot(object)
+autoplot(object, type = "histogram", binwidth = 1)
+autoplot(object, type = "residual")
 
- if (requireNamespace("mlr3learners")) {
-  library(mlr3learners)
-  learner = lrn("regr.ranger", predict_type = "se")
-  object = learner$train(task)$predict(task)
-  autoplot(object, type = "confidence")
- }
+learner = lrn("regr.ranger", predict_type = "se")
+object = learner$train(task)$predict(task)
+autoplot(object, type = "confidence")
 }
 }
diff --git a/man/autoplot.ResampleResult.Rd b/man/autoplot.ResampleResult.Rd
index 99104fe7..bf15e23e 100644
--- a/man/autoplot.ResampleResult.Rd
+++ b/man/autoplot.ResampleResult.Rd
@@ -62,37 +62,34 @@ For tasks with two features the predict type will be ignored.
 }
 \examples{
 \donttest{
-if (requireNamespace("mlr3")) {
-  library(mlr3)
-  library(mlr3viz)
+if (mlr3misc::require_namespaces("precrec", quietly = TRUE)) {
+task = tsk("sonar")
+learner = lrn("classif.rpart", predict_type = "prob")
+resampling = rsmp("cv", folds = 3)
+object = resample(task, learner, resampling)
 
-  task = tsk("sonar")
-  learner = lrn("classif.rpart", predict_type = "prob")
-  resampling = rsmp("cv", folds = 3)
-  object = resample(task, learner, resampling)
+head(fortify(object))
 
-  head(fortify(object))
+# Default: boxplot
+autoplot(object)
 
-  # Default: boxplot
-  autoplot(object)
+# Histogram
+autoplot(object, type = "histogram", bins = 30)
 
-  # Histogram
-  autoplot(object, type = "histogram", bins = 30)
+# ROC curve, averaged over resampling folds:
+autoplot(object, type = "roc")
 
-  # ROC curve, averaged over resampling folds:
-  autoplot(object, type = "roc")
+# ROC curve of joint prediction object:
+autoplot(object$prediction(), type = "roc")
 
-  # ROC curve of joint prediction object:
-  autoplot(object$prediction(), type = "roc")
+# Precision Recall Curve
+autoplot(object, type = "prc")
 
-  # Precision Recall Curve
-  autoplot(object, type = "prc")
-
-  # Prediction Plot
-  task = tsk("iris")$select(c("Sepal.Length", "Sepal.Width"))
-  resampling = rsmp("cv", folds = 3)
-  object = resample(task, learner, resampling, store_models = TRUE)
-  autoplot(object, type = "prediction")
+# Prediction Plot
+task = tsk("iris")$select(c("Sepal.Length", "Sepal.Width"))
+resampling = rsmp("cv", folds = 3)
+object = resample(task, learner, resampling, store_models = TRUE)
+autoplot(object, type = "prediction")
 }
 }
 }
diff --git a/man/autoplot.TaskClassif.Rd b/man/autoplot.TaskClassif.Rd
index 3444e44d..79b7a77d 100644
--- a/man/autoplot.TaskClassif.Rd
+++ b/man/autoplot.TaskClassif.Rd
@@ -33,16 +33,12 @@ Color is set to target column.
 }
 }
 \examples{
-if (requireNamespace("mlr3")) {
-  library(mlr3)
-  library(mlr3viz)
+if (mlr3misc::require_namespaces("GGally", quietly = TRUE)) {
+task = tsk("iris")
 
-  task = tsk("iris")
-
-  head(fortify(task))
-  autoplot(task)
-  autoplot(task$clone()$select(c("Sepal.Length", "Sepal.Width")),
-    type = "pairs")
-  autoplot(task, type = "duo")
+head(fortify(task))
+autoplot(task)
+autoplot(task$clone()$select(c("Sepal.Length", "Sepal.Width")), type = "pairs")
+autoplot(task, type = "duo")
 }
 }
diff --git a/man/autoplot.TaskClust.Rd b/man/autoplot.TaskClust.Rd
index 3675545f..e37c7c4b 100644
--- a/man/autoplot.TaskClust.Rd
+++ b/man/autoplot.TaskClust.Rd
@@ -29,14 +29,11 @@ Possible choices are:
 }
 }
 \examples{
-if (requireNamespace("mlr3")) {
-  library(mlr3)
-  library(mlr3cluster)
-  library(mlr3viz)
+if (mlr3misc::require_namespaces("mlr3cluster", quietly = TRUE)) {
+library(mlr3cluster)
+task = tsk("usarrests")
 
-  task = mlr_tasks$get("usarrests")
-
-  head(fortify(task))
-  autoplot(task)
+head(fortify(task))
+autoplot(task)
 }
 }
diff --git a/man/autoplot.TaskRegr.Rd b/man/autoplot.TaskRegr.Rd
index 7313ad9c..3821d459 100644
--- a/man/autoplot.TaskRegr.Rd
+++ b/man/autoplot.TaskRegr.Rd
@@ -31,15 +31,12 @@ Color is set to target column.
 }
 }
 \examples{
-if (requireNamespace("mlr3")) {
-  library(mlr3)
-  library(mlr3viz)
+if (mlr3misc::require_namespaces("GGally", quietly = TRUE)) {
+task = tsk("mtcars")
+task$select(c("am", "carb"))
 
-  task = tsk("mtcars")
-  task$select(c("am", "carb"))
-
-  head(fortify(task))
-  autoplot(task)
-  autoplot(task, type = "pairs")
+head(fortify(task))
+autoplot(task)
+autoplot(task, type = "pairs")
 }
 }
diff --git a/man/autoplot.TuningInstanceBatchSingleCrit.Rd b/man/autoplot.TuningInstanceBatchSingleCrit.Rd
index 7cc948d1..a9961d1b 100644
--- a/man/autoplot.TuningInstanceBatchSingleCrit.Rd
+++ b/man/autoplot.TuningInstanceBatchSingleCrit.Rd
@@ -65,37 +65,37 @@ The y values are interpolated with the supplied \link[mlr3:Learner]{mlr3::Learne
 }
 }
 \examples{
-if (requireNamespace("mlr3tuning") && requireNamespace("patchwork")) {
-  library(mlr3tuning)
+if (mlr3misc::require_namespaces(c("mlr3tuning", "patchwork", "paradox"), quietly = TRUE)) {
+library(mlr3tuning)
 
-  learner = lrn("classif.rpart")
-  learner$param_set$values$cp = to_tune(0.001, 0.1)
-  learner$param_set$values$minsplit = to_tune(1, 10)
+learner = lrn("classif.rpart")
+learner$param_set$values$cp = to_tune(0.001, 0.1)
+learner$param_set$values$minsplit = to_tune(1, 10)
 
-  instance = ti(
-    task = tsk("iris"),
-    learner = learner,
-    resampling = rsmp("holdout"),
-    measure = msr("classif.ce"),
-    terminator = trm("evals", n_evals = 10))
+instance = ti(
+  task = tsk("iris"),
+  learner = learner,
+  resampling = rsmp("holdout"),
+  measure = msr("classif.ce"),
+  terminator = trm("evals", n_evals = 10))
 
-  tuner = tnr("random_search")
+tuner = tnr("random_search")
 
-  tuner$optimize(instance)
+tuner$optimize(instance)
 
-  # plot performance versus batch number
-  autoplot(instance, type = "performance")
+# plot performance versus batch number
+autoplot(instance, type = "performance")
 
-  # plot cp values versus performance
-  autoplot(instance, type = "marginal", cols_x = "cp")
+# plot cp values versus performance
+autoplot(instance, type = "marginal", cols_x = "cp")
 
-  # plot transformed parameter values versus batch number
-  autoplot(instance, type = "parameter", trafo = TRUE)
+# plot transformed parameter values versus batch number
+autoplot(instance, type = "parameter", trafo = TRUE)
 
-  # plot parallel coordinates plot
-  autoplot(instance, type = "parallel")
+# plot parallel coordinates plot
+autoplot(instance, type = "parallel")
 
-  # plot pairs
-  autoplot(instance, type = "pairs")
+# plot pairs
+autoplot(instance, type = "pairs")
 }
 }
diff --git a/tests/testthat/teardown.R b/tests/testthat/teardown.R
index 6ed02f88..b51d98bc 100644
--- a/tests/testthat/teardown.R
+++ b/tests/testthat/teardown.R
@@ -1,2 +1,6 @@
-lg$set_threshold(old_threshold)
+if (requireNamespace("lgr")) {
+  lg = lgr::get_logger("mlr3")
+  lg$set_threshold(old_threshold)
+}
+
 setDTthreads(old_dt_threads)
diff --git a/tests/testthat/test_BenchmarkResult.R b/tests/testthat/test_BenchmarkResult.R
index 8010b689..c13f38b8 100644
--- a/tests/testthat/test_BenchmarkResult.R
+++ b/tests/testthat/test_BenchmarkResult.R
@@ -19,6 +19,7 @@ test_that("autoplot BenchmarkResult", {
   expect_true(is_ggplot(p))
   expect_doppelganger("bmr_boxplot", p)
 
+  skip_if_not_installed("precrec")
   expect_error(autoplot(bmr, type = "roc"), "multiple")
 
   object = bmr$clone(deep = TRUE)$filter(task_ids = "sonar")
@@ -33,6 +34,7 @@ test_that("autoplot BenchmarkResult", {
 })
 
 test_that("holdout roc plot (#54)", {
+  skip_if_not_installed("precrec")
   tasks = tsks("german_credit")
 
   learners = c("classif.featureless", "classif.rpart")
diff --git a/tests/testthat/test_Filter.R b/tests/testthat/test_Filter.R
index fe5943e4..ef84e776 100644
--- a/tests/testthat/test_Filter.R
+++ b/tests/testthat/test_Filter.R
@@ -1,4 +1,4 @@
-skip_if_not_installed("mlr3")
+skip_if_not_installed("mlr3filters")
 
 test_that("autoplot.Filter", {
   task = mlr3::tsk("iris")
diff --git a/tests/testthat/test_LearnerClassif.R b/tests/testthat/test_LearnerClassif.R
index 8c61f34b..87eb63a6 100644
--- a/tests/testthat/test_LearnerClassif.R
+++ b/tests/testthat/test_LearnerClassif.R
@@ -1,3 +1,5 @@
+skip_if_not_installed("mlr3")
+
 test_that("autoplot.PredictionClassif decision boundary probability", {
   set.seed(42)
   task = mlr3::tsk("pima")$select(c("age", "pedigree"))
diff --git a/tests/testthat/test_LearnerRegr.R b/tests/testthat/test_LearnerRegr.R
index 455d7e1a..73c6b8cc 100644
--- a/tests/testthat/test_LearnerRegr.R
+++ b/tests/testthat/test_LearnerRegr.R
@@ -1,3 +1,5 @@
+skip_if_not_installed("mlr3")
+
 test_that("autoplot.PredictionRegr decision boundary 1D", {
   set.seed(42)
   task = mlr3::tsk("mtcars")$select("am")
diff --git a/tests/testthat/test_PredictionClassif.R b/tests/testthat/test_PredictionClassif.R
index 45b77c73..5d1d972c 100644
--- a/tests/testthat/test_PredictionClassif.R
+++ b/tests/testthat/test_PredictionClassif.R
@@ -10,6 +10,7 @@ test_that("autoplot.PredictionClassif", {
   expect_true(is_ggplot(p))
   expect_doppelganger("predictionclassif_stacked", p)
 
+  skip_if_not_installed("precrec")
   p = autoplot(prediction, type = "roc")
   expect_true(is_ggplot(p))
   expect_doppelganger("predictionclassif_roc", p)
diff --git a/tests/testthat/test_ResampleResult.R b/tests/testthat/test_ResampleResult.R
index fee375e3..696adf53 100644
--- a/tests/testthat/test_ResampleResult.R
+++ b/tests/testthat/test_ResampleResult.R
@@ -24,6 +24,7 @@ test_that("autoplot ResampleResult", {
   expect_true(is_ggplot(p))
   expect_doppelganger("resampleresult_histogram", p)
 
+  skip_if_not_installed("precrec")
   p = autoplot(rr, type = "roc")
   expect_true(is_ggplot(p))
   expect_doppelganger("resampleresult_roc", p)
@@ -84,8 +85,8 @@ regression!")
 
 
 test_that("roc is not inverted", {
-  autoplot(rr, type = "roc")
   skip_if_not_installed("precrec")
+  autoplot(rr, type = "roc")
   tab = as.data.table(precrec::auc(precrec::evalmod(as_precrec(rr))))
   expect_number(mean(tab[curvetypes == "ROC", aucs]), lower = 0.5)
 })
diff --git a/tests/testthat/test_TaskClassif.R b/tests/testthat/test_TaskClassif.R
index 902e942f..7b4297a8 100644
--- a/tests/testthat/test_TaskClassif.R
+++ b/tests/testthat/test_TaskClassif.R
@@ -7,6 +7,7 @@ test_that("autoplot.TaskClassif", {
   expect_true(is_ggplot(p))
   expect_doppelganger("taskclassif_target", p)
 
+  skip_if_not_installed("GGally")
   p = autoplot(task, type = "pairs")
   expect_s3_class(p, "ggmatrix")
   expect_doppelganger("taskclassif_pairs", p)
diff --git a/tests/testthat/test_TaskRegr.R b/tests/testthat/test_TaskRegr.R
index 9670f9a5..9ffadbf4 100644
--- a/tests/testthat/test_TaskRegr.R
+++ b/tests/testthat/test_TaskRegr.R
@@ -7,6 +7,7 @@ test_that("autoplot.TaskRegr", {
   expect_true(is_ggplot(p))
   expect_doppelganger("taskregr_target", p)
 
+  skip_if_not_installed("GGally")
   p = autoplot(task, type = "pairs")
   expect_s3_class(p, "ggmatrix")
   expect_doppelganger("taskregr_pairs", p)