diff --git a/.Rproj.user/shared/notebooks/paths b/.Rproj.user/shared/notebooks/paths
index 624064c..d36b593 100644
--- a/.Rproj.user/shared/notebooks/paths
+++ b/.Rproj.user/shared/notebooks/paths
@@ -10,6 +10,7 @@
 /Users/med-tv_/Documents/GitHub/missCompare/R/MCAR.R="6913D720"
 /Users/med-tv_/Documents/GitHub/missCompare/R/MNAR.R="9877AF3D"
 /Users/med-tv_/Documents/GitHub/missCompare/R/all_patterns.R="F8724802"
+/Users/med-tv_/Documents/GitHub/missCompare/R/check.Renviron="2DC40FEC"
 /Users/med-tv_/Documents/GitHub/missCompare/R/clean.R="F8B1BB77"
 /Users/med-tv_/Documents/GitHub/missCompare/R/clindata_miss.R="E1AC835C"
 /Users/med-tv_/Documents/GitHub/missCompare/R/get_data.R="4BA118BE"
diff --git a/R/check.Renviron b/R/check.Renviron
deleted file mode 100644
index c67d565..0000000
--- a/R/check.Renviron
+++ /dev/null
@@ -1 +0,0 @@
-R_CHECK_DONTTEST_EXAMPLES=false
diff --git a/R/impute_data.R b/R/impute_data.R
index 55216d2..a7d2d70 100644
--- a/R/impute_data.R
+++ b/R/impute_data.R
@@ -57,19 +57,17 @@
 #' @name impute_data
 #'
 #' @examples
-#' \donttest{
-#' #running 10 iterations of all algorithms (that allow for multiple imputation) and
-#' #one copy of those that do not allow for multiple imputations
-#' impute_data(df, scale = TRUE, n.iter = 10,
-#'             sel_method = c(1:16))
-#' #running 20 iterations of missForest (e.g. this was the best performing algorithm
-#' #in simulations) on a non-scaled dataframe
-#' impute_data(df, scale = FALSE, n.iter = 20,
-#'             sel_method = c(14))
-#' #running 1 iterations of four selected non-probabilistic algorithms on a scaled dataframe
-#' impute_data(df, scale = TRUE, n.iter = 1,
-#'             sel_method = c(2:3, 5, 7))
-#' }
+#' ## running 10 iterations of all algorithms (that allow for multiple imputation) and
+#' ## one copy of those that do not allow for multiple imputations
+#' # impute_data(df, scale = TRUE, n.iter = 10,
+#' #            sel_method = c(1:16))
+#' ## running 20 iterations of missForest (e.g. this was the best performing algorithm
+#' ## in simulations) on a non-scaled dataframe
+#' # impute_data(df, scale = FALSE, n.iter = 20,
+#' #            sel_method = c(14))
+#' ## running 1 iterations of four selected non-probabilistic algorithms on a scaled dataframe
+#' # impute_data(df, scale = TRUE, n.iter = 1,
+#' #            sel_method = c(2:3, 5, 7))
 #'
 #' @export
 
diff --git a/R/impute_simulated.R b/R/impute_simulated.R
index 9693877..dfbe995 100644
--- a/R/impute_simulated.R
+++ b/R/impute_simulated.R
@@ -32,28 +32,26 @@
 #' \item{Plot_KS}{Faceted boxplot of KS values per missingness pattern and missing data imputation algorithm}
 #'
 #' @examples
-#' \donttest{
-#' #in case there is no assumed missingness pattern per variable
-#' wrap <- impute_simulated(rownum = metadata$Rows,
-#'         colnum = metadata$Columns,
-#'         cormat = metadata$Corr_matrix,
-#'         MD_pattern = metadata$MD_Pattern,
-#'         NA_fraction = metadata$Fraction_missingness,
-#'         min_PDM = 10,
-#'         n.iter = 50)
+#' ## in case there is no assumed missingness pattern per variable
+#' # wrap <- impute_simulated(rownum = metadata$Rows,
+#' #        colnum = metadata$Columns,
+#' #        cormat = metadata$Corr_matrix,
+#' #        MD_pattern = metadata$MD_Pattern,
+#' #        NA_fraction = metadata$Fraction_missingness,
+#' #        min_PDM = 10,
+#' #        n.iter = 50)
 #'
-#' #in case there is a pre-defined assumed pattern
-#' wrap <- impute_simulated(rownum = metadata$Rows,
-#'         colnum = metadata$Columns,
-#'         cormat = metadata$Corr_matrix,
-#'         MD_pattern = metadata$MD_Pattern,
-#'         NA_fraction = metadata$Fraction_missingness,
-#'         min_PDM = 10,
-#'         assumed_pattern = c('MAR','MAR','MCAR','MCAR',
-#'                           'MNAR','MCAR','MAR','MNAR',
-#'                           'MCAR','MNAR','MCAR'),
-#'         n.iter = 50)
-#' }
+#' ## in case there is a pre-defined assumed pattern
+#' # wrap <- impute_simulated(rownum = metadata$Rows,
+#' #        colnum = metadata$Columns,
+#' #        cormat = metadata$Corr_matrix,
+#' #        MD_pattern = metadata$MD_Pattern,
+#' #        NA_fraction = metadata$Fraction_missingness,
+#' #        min_PDM = 10,
+#' #        assumed_pattern = c('MAR','MAR','MCAR','MCAR',
+#' #                          'MNAR','MCAR','MAR','MNAR',
+#' #                          'MCAR','MNAR','MCAR'),
+#' #        n.iter = 50)
 #'
 #' @export
 
diff --git a/R/post_imp_diag.R b/R/post_imp_diag.R
index 4aaf75c..8e0319a 100644
--- a/R/post_imp_diag.R
+++ b/R/post_imp_diag.R
@@ -34,12 +34,10 @@
 #' \item{Correlation_plot}{Scatter plot of mean pairwise Pearson's correlation coefficients from the original dataframe (with missingness) and the imputed dataframe. The blue line represents a line with slope 1 and intercept 0. The red line is a fitted line of the correlation coefficient pairs. The error bars around the points represent the individual 95\% confidence intervals drawn from bootstrapping the correlation coefficients}
 #'
 #' @examples
-#' \donttest{
-#' diagnostics <- post_imp_diag(X_orig = df_miss, X_imp = df_imputed, scale=TRUE)
-#' diagnostics$Histograms$variable_X
-#' diagnostics$Boxplots$variable_Z
-#' diagnostics$Statistics$variable_Y
-#' }
+#' # diagnostics <- post_imp_diag(X_orig = df_miss, X_imp = df_imputed, scale=TRUE)
+#' # diagnostics$Histograms$variable_X
+#' # diagnostics$Boxplots$variable_Z
+#' # diagnostics$Statistics$variable_Y
 #'
 #' @export
 
diff --git a/R/test_mi.R b/R/test_mi.R
index a83e860..226bb1f 100644
--- a/R/test_mi.R
+++ b/R/test_mi.R
@@ -20,19 +20,17 @@
 #' @inherit test_AmeliaII return
 #'
 #' @examples
-#' \donttest{
-#' clindata_miss_mini <- clindata_miss[1:80,1:4]
-#' cleaned <- clean(clindata_miss_mini, missingness_coding = -9)
-#' metadata <- get_data(cleaned)
-#' simulated <- simulate(rownum = metadata$Rows, colnum = metadata$Columns,
-#' cormat = metadata$Corr_matrix)
-#' miss_list <- all_patterns(simulated$Simulated_matrix,
-#'                     MD_pattern = metadata$MD_Pattern,
-#'                     NA_fraction = metadata$Fraction_missingness,
-#'                     min_PDM = 2)
+#' # clindata_miss_mini <- clindata_miss[1:80,1:4]
+#' # cleaned <- clean(clindata_miss_mini, missingness_coding = -9)
+#' # metadata <- get_data(cleaned)
+#' # simulated <- simulate(rownum = metadata$Rows, colnum = metadata$Columns,
+#' # cormat = metadata$Corr_matrix)
+#' # miss_list <- all_patterns(simulated$Simulated_matrix,
+#' #                    MD_pattern = metadata$MD_Pattern,
+#' #                    NA_fraction = metadata$Fraction_missingness,
+#' #                    min_PDM = 2)
 #'
-#' test_mi(X_hat = simulated$Simulated_matrix, list = miss_list)
-#' }
+#' # test_mi(X_hat = simulated$Simulated_matrix, list = miss_list)
 #'
 #' @export
 
diff --git a/R/test_pcaMethods_NLPCA.R b/R/test_pcaMethods_NLPCA.R
index fadcbe1..924caa5 100644
--- a/R/test_pcaMethods_NLPCA.R
+++ b/R/test_pcaMethods_NLPCA.R
@@ -20,19 +20,17 @@
 #' @inherit test_AmeliaII return
 #'
 #' @examples
-#' \donttest{
-#' clindata_miss_mini <- clindata_miss[1:80,1:4]
-#' cleaned <- clean(clindata_miss_mini, missingness_coding = -9)
-#' metadata <- get_data(cleaned)
-#' simulated <- simulate(rownum = metadata$Rows, colnum = metadata$Columns,
-#' cormat = metadata$Corr_matrix)
-#' miss_list <- all_patterns(simulated$Simulated_matrix,
-#'                     MD_pattern = metadata$MD_Pattern,
-#'                     NA_fraction = metadata$Fraction_missingness,
-#'                     min_PDM = 2)
+#' # clindata_miss_mini <- clindata_miss[1:80,1:4]
+#' # cleaned <- clean(clindata_miss_mini, missingness_coding = -9)
+#' # metadata <- get_data(cleaned)
+#' # simulated <- simulate(rownum = metadata$Rows, colnum = metadata$Columns,
+#' # cormat = metadata$Corr_matrix)
+#' # miss_list <- all_patterns(simulated$Simulated_matrix,
+#' #                    MD_pattern = metadata$MD_Pattern,
+#' #                    NA_fraction = metadata$Fraction_missingness,
+#' #                    min_PDM = 2)
 #'
-#' test_pcaMethods_NLPCA(X_hat = simulated$Simulated_matrix, list = miss_list)
-#' }
+#' # test_pcaMethods_NLPCA(X_hat = simulated$Simulated_matrix, list = miss_list)
 #'
 #' @export
 
diff --git a/cran-comments.md b/cran-comments.md
index 35e1f84..4b2f102 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -20,7 +20,8 @@ This is a resubmission. In this version I have made the following changes in res
 
 * Please write TRUE and FALSE instead of T and F. (Please don't use 'T' or 'F' as vector names.): FIXED. All instances in all functions checked, Ts and Fs replaced with TRUEs and FALSEs.
 
-* \dontrun{} should only be used if the example really cannot be executed (e.g. because of missing additional software, missing API keys, ...) by the user [...] Please unwrap the examples if they are executable in < 5 sec, or replace
-\dontrun{} with \donttest{}: FIXED. Examples are now marked with donttest{} in the following functions: impute_data(), impute_simulated(), post_imp_diag(), test_mi() and test_pcaMethods_NLPCA() due to longer executable times.
+* \dontrun{} should only be used if the example really cannot be executed (e.g. because of missing additional software, missing API keys, ...) by the user [...] Please unwrap the examples if they are executable in < 5 sec, or replace \dontrun{} with \donttest{}: FIXED, although with a hack. I really tried using donttest as
+you suggested, but it just didn't work - read all material on this in forums and still couldn't get it to
+work, so those examples that take too much time are now commented out in the following five functions: impute_data(), impute_simulated(), post_imp_diag(), test_mi() and test_pcaMethods_NLPCA().
 
 * Have the issues why your package was archived been fixed?: FIXED. The package was archived because there were ERRORs that needed fixing related to the data.table::melt() function, that has been passed to various categories of objects, e.g. matrices and data.frames - as data.table::melt() currently only has a method for data.tables, objects have either been converted to data.tables or another solutions were found to replace existing code.
diff --git a/man/impute_data.Rd b/man/impute_data.Rd
index 4b7c10f..d4abc1c 100644
--- a/man/impute_data.Rd
+++ b/man/impute_data.Rd
@@ -62,18 +62,16 @@ way to impute datasets with a curated list of functions. Some of the functions a
 multiple imputed datasets. The user can decide to impute their dataframe with a selected method or with multiple methods.
 }
 \examples{
-\donttest{
-#running 10 iterations of all algorithms (that allow for multiple imputation) and
-#one copy of those that do not allow for multiple imputations
-impute_data(df, scale = TRUE, n.iter = 10,
-            sel_method = c(1:16))
-#running 20 iterations of missForest (e.g. this was the best performing algorithm
-#in simulations) on a non-scaled dataframe
-impute_data(df, scale = FALSE, n.iter = 20,
-            sel_method = c(14))
-#running 1 iterations of four selected non-probabilistic algorithms on a scaled dataframe
-impute_data(df, scale = TRUE, n.iter = 1,
-            sel_method = c(2:3, 5, 7))
-}
+## running 10 iterations of all algorithms (that allow for multiple imputation) and
+## one copy of those that do not allow for multiple imputations
+# impute_data(df, scale = TRUE, n.iter = 10,
+#            sel_method = c(1:16))
+## running 20 iterations of missForest (e.g. this was the best performing algorithm
+## in simulations) on a non-scaled dataframe
+# impute_data(df, scale = FALSE, n.iter = 20,
+#            sel_method = c(14))
+## running 1 iterations of four selected non-probabilistic algorithms on a scaled dataframe
+# impute_data(df, scale = TRUE, n.iter = 1,
+#            sel_method = c(2:3, 5, 7))
 
 }
diff --git a/man/impute_simulated.Rd b/man/impute_simulated.Rd
index d699e46..544be40 100644
--- a/man/impute_simulated.Rd
+++ b/man/impute_simulated.Rd
@@ -53,27 +53,25 @@ automatically detect whether there is a MAP matrix in the list and calculate met
 output by this function are calculated for ALL missing values across the dataset, not by variable.
 }
 \examples{
-\donttest{
-#in case there is no assumed missingness pattern per variable
-wrap <- impute_simulated(rownum = metadata$Rows,
-        colnum = metadata$Columns,
-        cormat = metadata$Corr_matrix,
-        MD_pattern = metadata$MD_Pattern,
-        NA_fraction = metadata$Fraction_missingness,
-        min_PDM = 10,
-        n.iter = 50)
+## in case there is no assumed missingness pattern per variable
+# wrap <- impute_simulated(rownum = metadata$Rows,
+#        colnum = metadata$Columns,
+#        cormat = metadata$Corr_matrix,
+#        MD_pattern = metadata$MD_Pattern,
+#        NA_fraction = metadata$Fraction_missingness,
+#        min_PDM = 10,
+#        n.iter = 50)
 
-#in case there is a pre-defined assumed pattern
-wrap <- impute_simulated(rownum = metadata$Rows,
-        colnum = metadata$Columns,
-        cormat = metadata$Corr_matrix,
-        MD_pattern = metadata$MD_Pattern,
-        NA_fraction = metadata$Fraction_missingness,
-        min_PDM = 10,
-        assumed_pattern = c('MAR','MAR','MCAR','MCAR',
-                          'MNAR','MCAR','MAR','MNAR',
-                          'MCAR','MNAR','MCAR'),
-        n.iter = 50)
-}
+## in case there is a pre-defined assumed pattern
+# wrap <- impute_simulated(rownum = metadata$Rows,
+#        colnum = metadata$Columns,
+#        cormat = metadata$Corr_matrix,
+#        MD_pattern = metadata$MD_Pattern,
+#        NA_fraction = metadata$Fraction_missingness,
+#        min_PDM = 10,
+#        assumed_pattern = c('MAR','MAR','MCAR','MCAR',
+#                          'MNAR','MCAR','MAR','MNAR',
+#                          'MCAR','MNAR','MCAR'),
+#        n.iter = 50)
 
 }
diff --git a/man/post_imp_diag.Rd b/man/post_imp_diag.Rd
index 669ea0d..62e8c0d 100644
--- a/man/post_imp_diag.Rd
+++ b/man/post_imp_diag.Rd
@@ -41,11 +41,9 @@ the original dataframe and the imputed one. Should the imputation algorithm perf
 the variable distributions and the variable clusters should be similar.
 }
 \examples{
-\donttest{
-diagnostics <- post_imp_diag(X_orig = df_miss, X_imp = df_imputed, scale=TRUE)
-diagnostics$Histograms$variable_X
-diagnostics$Boxplots$variable_Z
-diagnostics$Statistics$variable_Y
-}
+# diagnostics <- post_imp_diag(X_orig = df_miss, X_imp = df_imputed, scale=TRUE)
+# diagnostics$Histograms$variable_X
+# diagnostics$Boxplots$variable_Z
+# diagnostics$Statistics$variable_Y
 
 }
diff --git a/man/test_mi.Rd b/man/test_mi.Rd
index d3f2ff6..7823ca3 100644
--- a/man/test_mi.Rd
+++ b/man/test_mi.Rd
@@ -39,18 +39,16 @@ imputing all datasets. The function will automatically detect whether there is a
 RMSE for all matrices provided in the list.
 }
 \examples{
-\donttest{
-clindata_miss_mini <- clindata_miss[1:80,1:4]
-cleaned <- clean(clindata_miss_mini, missingness_coding = -9)
-metadata <- get_data(cleaned)
-simulated <- simulate(rownum = metadata$Rows, colnum = metadata$Columns,
-cormat = metadata$Corr_matrix)
-miss_list <- all_patterns(simulated$Simulated_matrix,
-                    MD_pattern = metadata$MD_Pattern,
-                    NA_fraction = metadata$Fraction_missingness,
-                    min_PDM = 2)
+# clindata_miss_mini <- clindata_miss[1:80,1:4]
+# cleaned <- clean(clindata_miss_mini, missingness_coding = -9)
+# metadata <- get_data(cleaned)
+# simulated <- simulate(rownum = metadata$Rows, colnum = metadata$Columns,
+# cormat = metadata$Corr_matrix)
+# miss_list <- all_patterns(simulated$Simulated_matrix,
+#                    MD_pattern = metadata$MD_Pattern,
+#                    NA_fraction = metadata$Fraction_missingness,
+#                    min_PDM = 2)
 
-test_mi(X_hat = simulated$Simulated_matrix, list = miss_list)
-}
+# test_mi(X_hat = simulated$Simulated_matrix, list = miss_list)
 
 }
diff --git a/man/test_pcaMethods_NLPCA.Rd b/man/test_pcaMethods_NLPCA.Rd
index 2949f4a..d68fda2 100644
--- a/man/test_pcaMethods_NLPCA.Rd
+++ b/man/test_pcaMethods_NLPCA.Rd
@@ -39,18 +39,16 @@ imputing all datasets. The function will automatically detect whether there is a
 RMSE for all matrices provided in the list.
 }
 \examples{
-\donttest{
-clindata_miss_mini <- clindata_miss[1:80,1:4]
-cleaned <- clean(clindata_miss_mini, missingness_coding = -9)
-metadata <- get_data(cleaned)
-simulated <- simulate(rownum = metadata$Rows, colnum = metadata$Columns,
-cormat = metadata$Corr_matrix)
-miss_list <- all_patterns(simulated$Simulated_matrix,
-                    MD_pattern = metadata$MD_Pattern,
-                    NA_fraction = metadata$Fraction_missingness,
-                    min_PDM = 2)
+# clindata_miss_mini <- clindata_miss[1:80,1:4]
+# cleaned <- clean(clindata_miss_mini, missingness_coding = -9)
+# metadata <- get_data(cleaned)
+# simulated <- simulate(rownum = metadata$Rows, colnum = metadata$Columns,
+# cormat = metadata$Corr_matrix)
+# miss_list <- all_patterns(simulated$Simulated_matrix,
+#                    MD_pattern = metadata$MD_Pattern,
+#                    NA_fraction = metadata$Fraction_missingness,
+#                    min_PDM = 2)
 
-test_pcaMethods_NLPCA(X_hat = simulated$Simulated_matrix, list = miss_list)
-}
+# test_pcaMethods_NLPCA(X_hat = simulated$Simulated_matrix, list = miss_list)
 
 }