diff --git a/DESCRIPTION b/DESCRIPTION
index 620a0594..e56ce8d9 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: EGAnet
 Title: Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics
 Version: 1.2.3
-Date: 2022-26-08
+Date: 2022-09-04
 Authors@R: c(person("Hudson", "Golino", email = "hfg9s@virginia.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-1601-1447")),
 	     person("Alexander", "Christensen", email = "alexpaulchristensen@gmail.com", role = "aut", comment = c(ORCID = "0000-0002-9798-7037")),
 	     person("Robert", "Moulder", email = "rgm4fd@virginia.edu", role = "ctb", comment = c(ORCID = "0000-0001-7504-9560")),
diff --git a/NEWS b/NEWS
index 6a648d01..91141985 100644
--- a/NEWS
+++ b/NEWS
@@ -4,6 +4,8 @@ o FIX: many bug fixes related to latest update; functions have largely returned
 
 o UPDATE: Mac and Linux parallelizations have been optimized
 
+o UPDATE: documented examples are more efficient for CRAN checks
+
 
 Changes in 1.2.1
 
diff --git a/R/.DS_Store b/R/.DS_Store
deleted file mode 100644
index 05f274d2..00000000
Binary files a/R/.DS_Store and /dev/null differ
diff --git a/R/CFA.R b/R/CFA.R
index 3e678c3a..4219c94c 100644
--- a/R/CFA.R
+++ b/R/CFA.R
@@ -37,7 +37,8 @@
 #' # Load data
 #' wmt <- wmt2[,7:24]
 #'
-#' \donttest{# Estimate EGA
+#' \dontrun{
+#' # Estimate EGA
 #' ega.wmt <- EGA(
 #'   data = wmt,
 #'   plot.EGA = FALSE # No plot for CRAN checks
diff --git a/R/EBICglasso.qgraph.R b/R/EBICglasso.qgraph.R
index 7be93d03..9a9987a5 100644
--- a/R/EBICglasso.qgraph.R
+++ b/R/EBICglasso.qgraph.R
@@ -70,7 +70,8 @@
 #' # Obtain data
 #' wmt <- wmt2[,7:24]
 #' 
-#' \donttest{# Compute graph with tuning = 0 (BIC)
+#' \dontrun{
+#' # Compute graph with tuning = 0 (BIC)
 #' BICgraph <- EBICglasso.qgraph(
 #'   data = wmt, gamma = 0
 #' )
diff --git a/R/EGA.R b/R/EGA.R
index 0db17b90..934c6905 100644
--- a/R/EGA.R
+++ b/R/EGA.R
@@ -207,7 +207,8 @@
 #' # Obtain data
 #' wmt <- wmt2[,7:24]
 #' 
-#' \donttest{# Estimate EGA
+#' \dontrun{
+#' # Estimate EGA
 #' ega.wmt <- EGA(
 #'   data = wmt,
 #'   plot.EGA = FALSE # No plot for CRAN checks
@@ -218,30 +219,26 @@
 #' 
 #' # Produce Methods section
 #' methods.section(ega.wmt)
-#'
+#' 
 #' # Estimate EGAtmfg
 #' ega.wmt.tmfg <- EGA(
-#'   data = wmt, model = "TMFG",
-#'   plot.EGA = FALSE # No plot for CRAN checks
+#'   data = wmt, model = "TMFG"
 #' )
 #'
 #' # Estimate EGA with Louvain algorithm
 #' ega.wmt.louvain <- EGA(
-#'   data = wmt, algorithm = "louvain",
-#'   plot.EGA = FALSE # No plot for CRAN checks
+#'   data = wmt, algorithm = "louvain"
 #' )
 #' 
 #' # Estimate EGA with Leiden algorithm
 #' ega.wmt.leiden <- EGA(
-#'   data = wmt, algorithm = "leiden",
-#'   plot.EGA = FALSE # No plot for CRAN checks
+#'   data = wmt, algorithm = "leiden"
 #' )
 #'
 #' # Estimate EGA with Spinglass algorithm
 #' ega.wmt.spinglass <- EGA(
 #'   data = wmt,
-#'   algorithm = igraph::cluster_spinglass, # any {igraph} algorithm
-#'   plot.EGA = FALSE # No plot for CRAN checks
+#'   algorithm = igraph::cluster_spinglass
 #' )}
 #'
 #' @seealso \code{\link{bootEGA}} to investigate the stability of EGA's estimation via bootstrap
diff --git a/R/EGA.estimate.R b/R/EGA.estimate.R
index 7018ed42..4eac3496 100644
--- a/R/EGA.estimate.R
+++ b/R/EGA.estimate.R
@@ -135,7 +135,8 @@
 #' # Obtain data
 #' wmt <- wmt2[,7:24]
 #' 
-#' \donttest{# Estimate EGA
+#' \dontrun{
+#' # Estimate EGA
 #' ega.wmt <- EGA.estimate(data = wmt)
 #'
 #' # Estimate EGAtmfg
diff --git a/R/EGA.fit.R b/R/EGA.fit.R
index d3ec5169..2c2f87c0 100644
--- a/R/EGA.fit.R
+++ b/R/EGA.fit.R
@@ -119,7 +119,8 @@
 #' # Load data
 #' wmt <- wmt2[,7:24]
 #'
-#' \donttest{# Estimate EGA
+#' \dontrun{
+#' # Estimate EGA
 #' ega.wmt <- EGA(
 #'   data = wmt, 
 #'   plot.EGA = FALSE # No plot for CRAN checks
diff --git a/R/LCT.R b/R/LCT.R
index acb39b9b..29c61d02 100644
--- a/R/LCT.R
+++ b/R/LCT.R
@@ -57,14 +57,12 @@
 #'
 #' @examples
 #' \donttest{# Compute LCT
-#' ## Network model
-#' LCT(data = wmt2[,7:24])
-#' 
 #' ## Factor model
-#' LCT(data = psychTools::bfi[,1:25])
+#' LCT(data = psychTools::bfi[,1:25])}
 #' 
+#' \dontrun{
 #' # Dynamic LCT
-#' LCT(sim.dynEGA[sim.dynEGA$ID == 1,1:20], dynamic = TRUE)}
+#' LCT(sim.dynEGA[sim.dynEGA$ID == 1,1:24], dynamic = TRUE)}
 #' 
 #' 
 #' @references
diff --git a/R/UVA.R b/R/UVA.R
index c8bc4f40..38609633 100644
--- a/R/UVA.R
+++ b/R/UVA.R
@@ -259,7 +259,8 @@
 #' key.ind <- match(colnames(items), as.character(psychTools::spi.dictionary$item_id))
 #' key <- as.character(psychTools::spi.dictionary$item[key.ind])
 #' 
-#' \donttest{# Automated selection of local dependence (default)
+#' \dontrun{
+#' # Automated selection of local dependence (default)
 #' uva.results <- UVA(data = items, key = key)
 #' 
 #' # Produce Methods section
@@ -267,8 +268,7 @@
 #' 
 #' # Manual selection of local dependence
 #' if(interactive()){
-#' uva.results <- UVA(data = items, key = key, auto = FALSE)
-#' }
+#' uva.results <- UVA(data = items, key = key, auto = FALSE)}
 #'
 #' @references
 #' # Simulation using \code{UVA} \cr
diff --git a/R/boot.ergoInfo.R b/R/boot.ergoInfo.R
index b73822e5..286ac9b5 100644
--- a/R/boot.ergoInfo.R
+++ b/R/boot.ergoInfo.R
@@ -10,7 +10,7 @@
 #' random noise is added to the edges of the population structure to simulate sampling variability. This noise
 #' follows a random uniform distribution ranging from -0.10 to 0.10. In addition, a proportion of edges are
 #' rewired to allow for slight variations on the population structure. The proportion of nodes that are rewired
-#' is sampled from a random uniform distribution between 0.20 to 0.25. This process is carried out for each
+#' is sampled from a random uniform distribution between 0.20 to 0.40. This process is carried out for each
 #' participant resulting in \emph{n} variations of the population structure. Afterward, EII is computed. This
 #' process is carried out for \emph{i} iterations (e.g., 100).
 #' 
@@ -47,15 +47,19 @@
 #' For Windows, \code{FALSE} is about 2x faster
 #'
 #' @examples
-#' \donttest{# Dynamic EGA individual and population structures
+#' # Obtain simulated data
+#' sim.data <- sim.dynEGA
+#' 
+#' \dontrun{
+#' # Dynamic EGA individual and population structures
 #' dyn1 <- dynEGA.ind.pop(
-#'   data = sim.dynEGA[,-c(22)], n.embed = 5, tau = 1,
-#'   delta = 1, id = 21, use.derivatives = 1,
+#'   data = sim.dynEGA[,-26], n.embed = 5, tau = 1,
+#'   delta = 1, id = 25, use.derivatives = 1,
 #'   model = "glasso", ncores = 2, corr = "pearson"
 #' )
 #'
 #' # Empirical Ergodicity Information Index
-#' eii1 <- ergoInfo(dynEGA.object = dyn1, use = "edge.list")
+#' eii1 <- ergoInfo(dynEGA.object = dyn1, use = "weighted")
 #'
 #' # Bootstrap Test for Ergodicity Information Index
 #' testing.ergoinfo <- boot.ergoInfo(
@@ -88,7 +92,7 @@
 #'
 #' @export
 # Bootstrap Test for the Ergodicity Information Index
-# Updated 28.08.2022
+# Updated 04.09.2022
 boot.ergoInfo <- function(
     dynEGA.object,
     EII, iter = 100,
@@ -180,8 +184,8 @@ boot.ergoInfo <- function(
       function(i){
         dynEGA.ind[[i]]$network <- rewire(
           dynEGA.pop$dynEGA$network,
-          min = 0.20, max = 0.25,
-          noise = NULL
+          min = 0.20, max = 0.40,
+          noise = 0.10
         )
         return(dynEGA.ind[[i]])
       }
diff --git a/R/bootEGA.R b/R/bootEGA.R
index fc2cfd2a..f1501270 100644
--- a/R/bootEGA.R
+++ b/R/bootEGA.R
@@ -281,11 +281,11 @@
 #' @examples
 #' # Load data
 #' wmt <- wmt2[,7:24]
-#'
-#' \donttest{# Standard EGA example
+#' 
+#' \dontrun{
+#' # Standard EGA example
 #' boot.wmt <- bootEGA(
-#'   data = wmt, iter = 100, # recommended 500
-#'   plot.typicalStructure = FALSE, # No plot for CRAN checks
+#'   data = wmt, iter = 500,
 #'   type = "parametric", ncores = 2
 #' )
 #' 
@@ -294,41 +294,36 @@
 #' 
 #' # Louvain example
 #' boot.wmt.louvain <- bootEGA(
-#'   data = wmt, iter = 100, # recommended 500
+#'   data = wmt, iter = 500,
 #'   algorithm = "louvain",
-#'   plot.typicalStructure = FALSE, # No plot for CRAN checks
 #'   type = "parametric", ncores = 2
 #' )
 #' 
 #' # Spinglass example
 #' boot.wmt.spinglass <- bootEGA(
-#'   data = wmt, iter = 100, # recommended 500
+#'   data = wmt, iter = 500,
 #'   algorithm = igraph::cluster_spinglass, # use any function from {igraph}
-#'   plot.typicalStructure = FALSE, # No plot for CRAN checks
 #'   type = "parametric", ncores = 2
 #' )
 #'
 #' # EGA fit example
 #' boot.wmt.fit <- bootEGA(
-#'   data = wmt, iter = 100, # recommended 500
+#'   data = wmt, iter = 500,
 #'   EGA.type = "EGA.fit",
-#'   plot.typicalStructure = FALSE, # No plot for CRAN checks
 #'   type = "parametric", ncores = 2
 #' )
 #' 
 #' # Hierarchical EGA example
 #' boot.wmt.hier <- bootEGA(
-#'   data = wmt, iter = 100, # recommended 500
+#'   data = wmt, iter = 500,
 #'   EGA.type = "hierEGA",
-#'   plot.typicalStructure = FALSE, # No plot for CRAN checks
 #'   type = "parametric", ncores = 2
 #' )
 #' 
 #' # Random-intercept EGA example
 #' boot.wmt.ri <- bootEGA(
-#'   data = wmt, iter = 100, # recommended 500
+#'   data = wmt, iter = 500,
 #'   EGA.type = "riEGA",
-#'   plot.typicalStructure = FALSE, # No plot for CRAN checks
 #'   type = "parametric", ncores = 2
 #' )}
 #'
diff --git a/R/compare.EGA.plots.R b/R/compare.EGA.plots.R
index 6c21e11e..62c70722 100644
--- a/R/compare.EGA.plots.R
+++ b/R/compare.EGA.plots.R
@@ -73,7 +73,8 @@
 #' sample1 <- items[sample(1:nrow(items), 1000),]
 #' sample2 <- items[sample(1:nrow(items), 1000),]
 #' 
-#' \donttest{# Estimate EGAs
+#' \dontrun{
+#' # Estimate EGAs
 #' ega1 <- EGA(sample1)
 #' ega2 <- EGA(sample2)
 #' 
diff --git a/R/datasets.R b/R/datasets.R
deleted file mode 100644
index 5000ee78..00000000
--- a/R/datasets.R
+++ /dev/null
@@ -1,222 +0,0 @@
-#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-## Datasets for EGAnet // Updated 24.02.2022
-#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-#Bootstrap EGA WMT-2 Data----
-#' Bootstrap EGA WMT-2 Data
-#'
-#' \code{\link[EGAnet]{bootEGA}} Results of \code{\link[EGAnet]{wmt2}}Data
-#'
-#' \code{\link[EGAnet]{bootEGA}} results using the \code{"glasso"} model and 500 iterations
-#' of the Wiener Matrizen-Test 2 (WMT-2)
-#'
-#' @name boot.wmt
-#'
-#' @docType data
-#'
-#' @usage data(boot.wmt)
-#'
-#' @format A list with 8 objects (see \code{\link[EGAnet]{bootEGA}})
-#'
-#' @keywords datasets
-#'
-#' @examples
-#' data("boot.wmt")
-#'
-NULL
-
-#Depression Data----
-#' Depression Data
-#'
-#' A response matrix (n = 574) of the Beck Depression Inventory, Beck Anxiety Inventory and
-#' the Athens Insomnia Scale.
-#'
-#' @name depression
-#'
-#' @docType data
-#'
-#' @usage data(depression)
-#'
-#' @format A 574x78 response matrix
-#'
-#' @keywords datasets
-#'
-#' @examples
-#' data("depression")
-#'
-NULL
-
-#' Loadings Comparison Test Deep Learning Neural Network Weights
-#'
-#' A list of weights from four different neural network models:
-#' random vs. non-random model (\code{r_nr_weights}),
-#' low correlation factor vs. network model (\code{lf_n_weights}),
-#' high correlation with variables less than or equal to factors vs. network model (\code{hlf_n_weights}), and
-#' high correlation with variables greater than factors vs. network model (\code{hgf_n_weights})
-#'
-#' @name dnn.weights
-#'
-#' @docType data
-#'
-#' @usage data(dnn.weights)
-#'
-#' @format A list of with a length of 4
-#'
-#' @keywords datasets
-#'
-#' @examples
-#' data("dnn.weights")
-#'
-NULL
-
-#EGA WMT-2 Data----
-#' EGA WMT-2 Data
-#'
-#' \code{\link[EGAnet]{EGA}} Network of \code{\link[EGAnet]{wmt2}}Data
-#'
-#' An \code{\link[EGAnet]{EGA}} using the \code{"glasso"} model of the
-#' Wiener Matrizen-Test 2 (WMT-2)
-#'
-#' @name ega.wmt
-#'
-#' @docType data
-#'
-#' @usage data(ega.wmt)
-#'
-#' @format A 17 x 17 adjacency matrix
-#'
-#' @keywords datasets
-#'
-#' @examples
-#' data("ega.wmt")
-#'
-NULL
-
-#Intelligence Data----
-#' Intelligence Data
-#'
-#' A response matrix (n = 1152) of the International Cognitive Ability Resource (ICAR)
-#' intelligence battery developed by Condon and Revelle (2016).
-#'
-#' @name intelligenceBattery
-#'
-#' @docType data
-#'
-#' @usage data(intelligenceBattery)
-#'
-#' @format A 1185x125 response matrix
-#'
-#' @keywords datasets
-#'
-#' @examples
-#' data("intelligenceBattery")
-#'
-NULL
-
-#Optimism Data----
-#' Optimism Data
-#'
-#' A response matrix (n = 282) containing responses to 10 items of the Revised Life
-#' Orientation Test (LOT-R), developed by Scheier, Carver, & Bridges (1994).
-#'
-#' @name optimism
-#'
-#' @docType data
-#'
-#' @usage data(optimism)
-#'
-#' @format A 282x10 response matrix
-#'
-#' @keywords datasets
-#'
-#' @references
-#' Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994).
-#' Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): a reevaluation of the Life Orientation Test.
-#' \emph{Journal of Personality and Social Psychology}, \emph{67}, 1063-1078.
-#'
-#' @examples
-#' data("optimism")
-#'
-NULL
-
-#Prime Numbers ----
-#' Prime Numbers through 100,000
-#'
-#' Numeric vector of primes generated from the primes package. Used in
-#' the function \code{[EGAnet]{ergoInfo}}. Not for general use
-#'
-#' @name prime.num
-#'
-#' @docType data
-#'
-#' @usage data(prime.num)
-#'
-#' @format A 1185x24 response matrix
-#'
-#' @keywords datasets
-#'
-#' @examples
-#' data("prime.num")
-#'
-NULL
-
-#sim.dynEGA Data ----
-#' sim.dynEGA Data
-#'
-#' A simulated (multivariate time series) data with 20 variables, 200 individual observations,
-#' 50 time points per individual and 2 groups of individuals.
-#'
-#' @name sim.dynEGA
-#'
-#' @docType data
-#'
-#' @usage data(sim.dynEGA)
-#'
-#' @format A 10000x22 multivariate time series
-#'
-#' @keywords datasets
-#'
-#' @examples
-#' data("sim.dynEGA")
-#'
-NULL
-
-#Toy Example Data----
-#' Toy Example Data
-#'
-#' A simulated dataset with 2 factors, three items per factor and  n = 500.
-#'
-#' @name toy.example
-#'
-#' @docType data
-#'
-#' @usage data(toy.example)
-#'
-#' @format A 500x6 response matrix
-#'
-#' @keywords datasets
-#'
-#' @examples
-#' data("toy.example")
-#'
-NULL
-
-#WMT-2 Data----
-#' WMT-2 Data
-#'
-#' A response matrix (n = 1185) of the Wiener Matrizen-Test 2 (WMT-2).
-#'
-#' @name wmt2
-#'
-#' @docType data
-#'
-#' @usage data(wmt2)
-#'
-#' @format A 1185x24 response matrix
-#'
-#' @keywords datasets
-#'
-#' @examples
-#' data("wmt2")
-#'
-NULL
diff --git a/R/dimensionStability.R b/R/dimensionStability.R
index efea4d57..bbf44b11 100644
--- a/R/dimensionStability.R
+++ b/R/dimensionStability.R
@@ -31,10 +31,10 @@
 #' # Load data
 #' wmt <- wmt2[,7:24]
 #'
-#' \donttest{# Estimate bootstrap EGA
+#' \dontrun{
+#' # Estimate bootstrap EGA
 #' boot.wmt <- bootEGA(
-#'   data = wmt, iter = 100, # recommended 500
-#'   plot.typicalStructure = FALSE, # No plot for CRAN checks
+#'   data = wmt, iter = 500,
 #'   type = "parametric", ncores = 2
 #' )}
 #'
@@ -42,12 +42,12 @@
 #' res <- dimensionStability(boot.wmt)
 #' res$dimension.stability
 #' 
-#' \donttest{# Produce Methods section
+#' \dontrun{
+#' # Produce Methods section
 #' methods.section(
 #'   boot.wmt,
 #'   stats = "dimensionStability"
-#' )
-#' }
+#' )}
 #' 
 #'
 #' @references
diff --git a/R/dynEGA.R b/R/dynEGA.R
index 5c6a141a..cf06a4a4 100644
--- a/R/dynEGA.R
+++ b/R/dynEGA.R
@@ -164,10 +164,11 @@
 #' # Obtain data
 #' sim.dynEGA <- sim.dynEGA # bypasses CRAN checks
 #'
-#' \donttest{# Population structure
+#' \dontrun{
+#' # Population structure
 #' dyn.random <- dynEGA(
 #'   data = sim.dynEGA, n.embed = 5, tau = 1,
-#'   delta = 1, id = 21, group = 22, use.derivatives = 1,
+#'   delta = 1, id = 25, group = 26, use.derivatives = 1,
 #'   level = "population", ncores = 2, corr = "pearson"
 #' )
 #'
@@ -177,17 +178,17 @@
 #' # Group structure
 #' dyn.group <- dynEGA(
 #'   data = sim.dynEGA, n.embed = 5, tau = 1,
-#'   delta = 1, id = 21, group = 22, use.derivatives = 1,
+#'   delta = 1, id = 25, group = 26, use.derivatives = 1,
 #'   level = "group", ncores = 2, corr = "pearson"
 #' )
 #'
 #' # Plot group structure
 #' plot(dyn.group, ncol = 2, nrow = 1)
-#'
+#' 
 #' # Intraindividual structure
 #' dyn.individual <- dynEGA(
 #'   data = sim.dynEGA, n.embed = 5, tau = 1,
-#'   delta = 1, id = 21, group = 22, use.derivatives = 1,
+#'   delta = 1, id = 25, group = 26, use.derivatives = 1,
 #'   level = "individual", ncores = 2, corr = "pearson"
 #' )
 #'
diff --git a/R/dynEGA.ind.pop.R b/R/dynEGA.ind.pop.R
index 6eb7adcc..2c48daaa 100644
--- a/R/dynEGA.ind.pop.R
+++ b/R/dynEGA.ind.pop.R
@@ -147,7 +147,7 @@
 #' \donttest{# Dynamic EGA individual and population structure
 #' dyn.ega1 <- dynEGA.ind.pop(
 #'   data = sim.dynEGA, n.embed = 5, tau = 1,
-#'   delta = 1, id = 21, use.derivatives = 1,
+#'   delta = 1, id = 25, use.derivatives = 1,
 #'   ncores = 2, corr = "pearson"
 #' )}
 #'
diff --git a/R/entropyFit.R b/R/entropyFit.R
index 7bc4ebaa..c7d39f10 100644
--- a/R/entropyFit.R
+++ b/R/entropyFit.R
@@ -25,11 +25,9 @@
 #' # Load data
 #' wmt <- wmt2[,7:24]
 #'
-#' \donttest{# Estimate EGA model
-#' ega.wmt <- EGA(
-#'   data = wmt,
-#'   plot.EGA = FALSE # No plot for CRAN checks
-#' )}
+#' \dontrun{
+#' # Estimate EGA model
+#' ega.wmt <- EGA(data = wmt)}
 #'
 #' # Compute entropy indices
 #' entropyFit(data = wmt, structure = ega.wmt$wc)
diff --git a/R/ergoInfo.R b/R/ergoInfo.R
index 1ce3cdaf..cf32358c 100644
--- a/R/ergoInfo.R
+++ b/R/ergoInfo.R
@@ -43,17 +43,18 @@
 #' # Obtain data
 #' sim.dynEGA <- sim.dynEGA # bypasses CRAN checks
 #'
-#' \donttest{# Dynamic EGA individual and population structure
+#' \dontrun{
+#' # Dynamic EGA individual and population structure
 #' dyn.ega1 <- dynEGA.ind.pop(
-#'   data = sim.dynEGA, n.embed = 5, tau = 1,
-#'   delta = 1, id = 21, use.derivatives = 1,
+#'   data = sim.dynEGA[,-26], n.embed = 5, tau = 1,
+#'   delta = 1, id = 25, use.derivatives = 1,
 #'   ncores = 2, corr = "pearson"
 #' )
 #'
 #' # Compute empirical ergodicity information index
 #' eii <- ergoInfo(
 #'   dynEGA.object = dyn.ega1,
-#'   use = "edge.list"
+#'   use = "weighted"
 #' )}
 #'
 #' @export
diff --git a/R/glla.R b/R/glla.R
index 479c8bb0..4f30f63a 100644
--- a/R/glla.R
+++ b/R/glla.R
@@ -28,12 +28,10 @@
 #' derivatives to be estimated via generalized local linear approximation.
 #'
 #' @examples
-#'
 #' # A time series with 8 time points
 #' tseries <- 49:56
 #' deriv.tseries <- glla(tseries, n.embed = 4, tau = 1, delta = 1, order = 2)
 #'
-#'
 #' @references
 #'
 #' Boker, S. M., Deboeck, P. R., Edler, C., & Keel, P. K. (2010)
diff --git a/R/hierEGA.R b/R/hierEGA.R
index 589e8f0a..dc08ae62 100644
--- a/R/hierEGA.R
+++ b/R/hierEGA.R
@@ -277,11 +277,11 @@
 #' # Obtain example data
 #' data <- optimism
 #' 
-#' \donttest{# hierEGA example
+#' \dontrun{
+#' # hierEGA example
 #' opt.hier<- hierEGA(
 #'   data = optimism,
-#'   algorithm = "louvain",
-#'   plot.EGA = FALSE # no plots for CRAN check
+#'   algorithm = "louvain"
 #' )}
 #'
 #' @export
diff --git a/R/infoCluster.R b/R/infoCluster.R
index 1f409316..c03f1c95 100644
--- a/R/infoCluster.R
+++ b/R/infoCluster.R
@@ -17,18 +17,16 @@
 #'# Obtain data
 #' sim.dynEGA <- sim.dynEGA # bypasses CRAN checks
 #' 
-#' \donttest{# Dynamic EGA individual and population structure
+#' \dontrun{
+#' # Dynamic EGA individual and population structure
 #' dyn.ega1 <- dynEGA.ind.pop(
 #'   data = sim.dynEGA, n.embed = 5, tau = 1,
-#'   delta = 1, id = 21, use.derivatives = 1, 
+#'   delta = 1, id = 25, use.derivatives = 1, 
 #'   ncores = 2, corr = "pearson"
 #' )
 #' 
 #' # Perform information-theoretic clustering
-#' clust1 <- infoCluster(
-#'   dynEGA.object = dyn.ega1,
-#'   plot.cluster = FALSE # No plot for CRAN checks
-#' )}
+#' clust1 <- infoCluster(dynEGA.object = dyn.ega1)}
 #'
 #' @return Returns a list containing:
 #' 
diff --git a/R/invariance.R b/R/invariance.R
index de807d25..8ec48038 100644
--- a/R/invariance.R
+++ b/R/invariance.R
@@ -212,7 +212,7 @@
 #' # Groups
 #' groups <- rep(1:2, each = nrow(wmt) / 2)
 #' 
-#' \donttest{
+#' \dontrun{
 #' # Measurement invariance
 #' results <- invariance(wmt, groups, ncores = 2)}
 #' 
diff --git a/R/itemStability.R b/R/itemStability.R
index acfaccca..3f907f8f 100644
--- a/R/itemStability.R
+++ b/R/itemStability.R
@@ -61,10 +61,12 @@
 #' \item{mean.loadings}{Matrix of the average standardized network loading
 #' (computed using \code{\link[EGAnet]{net.loads}}) for each item in each dimension}
 #'
-#' @examples # Load data
+#' @examples
+#' # Load data
 #' wmt <- wmt2[,7:24]
 #'
-#' \donttest{# Standard EGA example
+#' \dontrun{
+#' # Standard EGA example
 #' boot.wmt <- bootEGA(
 #'   data = wmt, iter = 100, # recommended 500
 #'   plot.typicalStructure = FALSE, # No plot for CRAN checks
@@ -72,10 +74,7 @@
 #' )
 #' 
 #' # Standard item stability 
-#' wmt.is <- itemStability(
-#'   boot.wmt,
-#'   IS.plot = FALSE # NO plot for CRAN checks
-#' )
+#' wmt.is <- itemStability(boot.wmt)
 #' 
 #' # Produce Methods section
 #' methods.section(
@@ -85,45 +84,33 @@
 #'
 #' # EGA fit example
 #' boot.wmt.fit <- bootEGA(
-#'   data = wmt, iter = 100, # recommended 500
+#'   data = wmt, iter = 500,
 #'   EGA.type = "EGA.fit",
-#'   plot.typicalStructure = FALSE, # No plot for CRAN checks
 #'   type = "parametric", ncores = 2
 #' )
 #' 
 #' # EGA fit item stability 
-#' wmt.is.fit <- itemStability(
-#'   boot.wmt.fit,
-#'   IS.plot = FALSE # NO plot for CRAN checks
-#' )
+#' wmt.is.fit <- itemStability(boot.wmt.fit)
 #' 
 #' # Hierarchical EGA example
 #' boot.wmt.hier <- bootEGA(
-#'   data = wmt, iter = 100, # recommended 500
+#'   data = wmt, iter = 500,
 #'   EGA.type = "hierEGA",
-#'   plot.typicalStructure = FALSE, # No plot for CRAN checks
 #'   type = "parametric", ncores = 2
 #' )
 #' 
 #' # Hierarchical EGA item stability 
-#' wmt.is.hier <- itemStability(
-#'   boot.wmt.hier,
-#'   IS.plot = FALSE # NO plot for CRAN checks
-#' )
+#' wmt.is.hier <- itemStability(boot.wmt.hier)
 #' 
 #' # Random-intercept EGA example
 #' boot.wmt.ri <- bootEGA(
-#'   data = wmt, iter = 100, # recommended 500
+#'   data = wmt, iter = 500,
 #'   EGA.type = "riEGA",
-#'   plot.typicalStructure = FALSE, # No plot for CRAN checks
 #'   type = "parametric", ncores = 2
 #' )
 #' 
 #' # Random-intercept EGA item stability 
-#' wmt.is.ri <- itemStability(
-#'   boot.wmt.ri,
-#'   IS.plot = FALSE # NO plot for CRAN checks
-#' )}
+#' wmt.is.ri <- itemStability(boot.wmt.ri)}
 #'
 #' @references
 #' Christensen, A. P., & Golino, H. (2021).
diff --git a/R/jsd.ergoInfo.R b/R/jsd.ergoInfo.R
index 835d0042..7f7fa3d8 100644
--- a/R/jsd.ergoInfo.R
+++ b/R/jsd.ergoInfo.R
@@ -40,10 +40,11 @@
 #' Values can range between 0 and 1
 #'
 #' @examples
-#' \donttest{# Dynamic EGA individual and population structures
+#' \dontrun{
+#' # Dynamic EGA individual and population structures
 #' dyn1 <- dynEGA.ind.pop(
-#'   data = sim.dynEGA[,-c(22)], n.embed = 5, tau = 1,
-#'   delta = 1, id = 21, use.derivatives = 1,
+#'   data = sim.dynEGA[,-26], n.embed = 5, tau = 1,
+#'   delta = 1, id = 25, use.derivatives = 1,
 #'   model = "glasso", ncores = 2, corr = "pearson"
 #' )
 #'
diff --git a/R/network.descriptives.R b/R/network.descriptives.R
index 126789dc..b475c0d1 100644
--- a/R/network.descriptives.R
+++ b/R/network.descriptives.R
@@ -50,11 +50,8 @@
 #' # Load data
 #' wmt <- wmt2[,7:24]
 #'
-#' \donttest{# EGA example
-#' ega.wmt <- EGA(
-#'   data = wmt,
-#'   plot.EGA = FALSE # No plot for CRAN
-#' )}
+#' \dontrun{# EGA example
+#' ega.wmt <- EGA(data = wmt)}
 #' 
 #' # Compute descriptives
 #' network.descriptives(ega.wmt)
diff --git a/R/riEGA.R b/R/riEGA.R
index 3aac92d6..c6bae56e 100644
--- a/R/riEGA.R
+++ b/R/riEGA.R
@@ -227,9 +227,8 @@
 #' # Obtain example data
 #' data <- optimism
 #' 
-#' \donttest{# riEGA example
-#' opt.res <- riEGA(data = optimism)
-#' }
+#' \dontrun{# riEGA example
+#' opt.res <- riEGA(data = optimism)}
 #'
 #' @references 
 #' # Selection of CFA Estimator \cr
diff --git a/R/sim.dynEGA.R b/R/sim.dynEGA.R
index 6c0ce0ab..e9c01797 100644
--- a/R/sim.dynEGA.R
+++ b/R/sim.dynEGA.R
@@ -1,7 +1,20 @@
 #' sim.dynEGA Data
 #'
-#' A simulated (multivariate time series) data with 20 variables, 200 individual observations,
-#' 50 time points per individual and 2 groups of individuals.
+#' A simulated (multivariate time series) data with 24 variables,
+#' 100 individual observations, 50 time points per individual and 
+#' 2 groups of individuals.
+#' 
+#' Data were generated using the \code{\link[EGAnet]{simDFM}} function
+#' with the following arguments:
+#' 
+#' \code{simDFM(
+#'   variab = 12, timep = 50, nfact = 2,
+#'   error = 0.175, dfm = "DAFS",
+#'   loadings = 0.70, autoreg = 0.50,
+#'   crossreg = 0.10, var.shock = 0.09,
+#'   cov.shock = 0.30, variation = TRUE
+#' )}
+#' 
 #'
 #' @name sim.dynEGA
 #'
@@ -9,7 +22,7 @@
 #'
 #' @usage data(sim.dynEGA)
 #'
-#' @format A 10000x22 multivariate time series
+#' @format A 5000 x 26 multivariate time series
 #'
 #' @keywords datasets
 #'
@@ -17,4 +30,4 @@
 #' data("sim.dynEGA")
 #'
 NULL
-#----
+# Updated 04.09.2022
diff --git a/R/simDFM.R b/R/simDFM.R
index 2c1802be..1666fb66 100644
--- a/R/simDFM.R
+++ b/R/simDFM.R
@@ -39,6 +39,11 @@
 #'
 #' @param burnin Number of n first samples to discard when computing the factor scores. Defaults to 1000.
 #'
+#' @param variation Boolean.
+#' Whether parameters should be varied.
+#' Defaults to \code{FALSE}.
+#' Set to \code{TRUE} to add slight variation to all parameters
+#'
 #' @examples
 #'
 #'
@@ -65,34 +70,86 @@
 #'
 #' @export
 # Simulate dynamic factor model
-# Updated 09.05.2022
-simDFM <- function(variab, timep, nfact, error, dfm = c("DAFS","RandomWalk"),
-                   loadings, autoreg, crossreg, var.shock, cov.shock, burnin = 1000){
+# Updated 04.09.2022
+simDFM <- function(
+    variab, timep, nfact, error, dfm = c("DAFS","RandomWalk"),
+    loadings, autoreg, crossreg, var.shock, cov.shock, burnin = 1000,
+    variation = FALSE
+){
 
   #### MISSING ARGUMENTS HANDLING ####
-  if(missing(dfm))
-  {dfm <- "DAFS"
-  }else{level <- match.arg(dfm)}
+  if(missing(dfm)){
+    dfm <- "DAFS"
+  }else{dfm <- match.arg(dfm)}
 
   # Factor Scores:
 
   if(dfm == "DAFS"){
-    # B = Matrix of Bl is a nfact x nfact matrix containing the autoregressive and cross-regressive coefficients
-    B <- matrix(crossreg, ncol = nfact, nrow = nfact)
-    diag(B) <- autoreg
-
-    # Shock = Random shock vectors following a multivariate normal distribution with mean zeros and nfact x nfact q covariance matrix D
-    D <- matrix(var.shock,nfact,nfact)
-    diag(D) <- cov.shock
-    Shock <- MASS_mvrnorm(burnin+timep,matrix(0,nfact,1),D)
-
-    Fscores <- matrix(0,burnin+timep,nfact)
-    Fscores[1,] <- Shock[1,]
-
-    for (t in 2: (burnin+timep)){
-      Fscores[t,] <- Fscores[t-1,] %*% B+ Shock[t,]
+    
+    # Add variation
+    if(isTRUE(variation)){
+      
+      # B = Matrix of Bl is a nfact x nfact matrix containing the autoregressive and cross-regressive coefficients
+      B <- matrix(
+        # Add some variation
+        crossreg + runif(nfact * nfact, -0.05, 0.05),
+        ncol = nfact,
+        nrow = nfact
+      )
+      # Make symmetric
+      B <- (t(B) + B) / 2
+      
+      # Add some varation
+      diag(B) <- autoreg + runif(nfact, -0.05, 0.05)
+      
+      # Shock = Random shock vectors following a multivariate normal distribution with mean zeros and nfact x nfact q covariance matrix D
+      D <- matrix(
+        # Add some variation
+        var.shock + runif(nfact * nfact, -0.05, 0.05),
+        nfact,
+        nfact
+      )
+      # Make symmetric
+      D <- (t(D) + D) / 2
+      
+      # Add some variation
+      diag(D) <- cov.shock + runif(nfact, -0.05, 0.05)
+      
+      # Compute shock
+      Shock <- MASS_mvrnorm(
+        burnin + timep, matrix(0, nfact, 1), D
+      )
+      
+      Fscores <- matrix(0, burnin + timep, nfact)
+      Fscores[1,] <- Shock[1,]
+      
+      for (t in 2: (burnin+timep)){
+        Fscores[t,] <- Fscores[t-1,] %*% B+ Shock[t,]
+      }
+      Fscores <- Fscores[-c(1:burnin),]
+      
+    }else{
+      
+      # B = Matrix of Bl is a nfact x nfact matrix containing the autoregressive and cross-regressive coefficients
+      B <- matrix(
+        crossreg, ncol = nfact, nrow = nfact
+      )
+      diag(B) <- autoreg
+      
+      # Shock = Random shock vectors following a multivariate normal distribution with mean zeros and nfact x nfact q covariance matrix D
+      D <- matrix(var.shock, nfact, nfact)
+      diag(D) <- cov.shock
+      Shock <- MASS_mvrnorm(burnin+timep,matrix(0,nfact,1),D)
+      
+      Fscores <- matrix(0,burnin+timep,nfact)
+      Fscores[1,] <- Shock[1,]
+      
+      for (t in 2: (burnin+timep)){
+        Fscores[t,] <- Fscores[t-1,] %*% B+ Shock[t,]
+      }
+      Fscores <- Fscores[-c(1:burnin),]
+      
     }
-    Fscores <- Fscores[-c(1:burnin),]
 
   }else{
     Fscores <- matrix(rnorm(nfact*(burnin+timep), 0, 1), nfact, burnin+timep)
@@ -102,12 +159,24 @@ simDFM <- function(variab, timep, nfact, error, dfm = c("DAFS","RandomWalk"),
   }
 
 
-  LoadMat <- as.matrix(Matrix::bdiag(lapply(rep(loadings, nfact), rep, variab)))
+  if(isTRUE(variation)){
+    loads <- lapply(rep(loadings, nfact), rep, variab)
+    loads <- lapply(loads, function(x){x + runif(length(x), -0.10, 0.10)})
+    LoadMat <- as.matrix(Matrix::bdiag(loads))
+  }else{
+    LoadMat <- as.matrix(Matrix::bdiag(lapply(rep(loadings, nfact), rep, variab)))
+  }
 
   ## Error: multivariate normal distribution with mean zeros and p x p covariance matrix Q
-  var <- error^2
-  Q <- diag(var,variab*nfact,variab*nfact)
-  e <- t(MASS_mvrnorm(timep, matrix(0,variab*nfact,1),Q))
+  if(isTRUE(variation)){
+    var <- rep(error, variab*nfact) + runif(variab*nfact, -0.025, 0.025)
+    Q <- diag(var,variab*nfact,variab*nfact)
+    e <- t(MASS_mvrnorm(timep, matrix(0,variab*nfact,1),Q))
+  }else{
+    var <- error^2
+    Q <- diag(var,variab*nfact,variab*nfact)
+    e <- t(MASS_mvrnorm(timep, matrix(0,variab*nfact,1),Q))
+  }
 
 
   ## Simulated obs data
diff --git a/R/tefi.R b/R/tefi.R
index 9d5f0092..3aef801d 100644
--- a/R/tefi.R
+++ b/R/tefi.R
@@ -22,9 +22,7 @@
 #'
 #' \dontrun{
 #' # Estimate EGA model
-#' ega.wmt <- EGA(data = wmt, model = "glasso")
-#'
-#' }
+#' ega.wmt <- EGA(data = wmt, model = "glasso")}
 #'
 #' # Compute entropy indices
 #' tefi(data = ega.wmt$correlation, structure = ega.wmt$wc)
diff --git a/R/totalCorMat.R b/R/totalCorMat.R
index 02a2afcd..4150d6fd 100644
--- a/R/totalCorMat.R
+++ b/R/totalCorMat.R
@@ -11,10 +11,8 @@
 #'
 #' @examples
 #' \dontrun{
-#' \donttest{
 #' # Compute total correlation
-#' totalCorMat(wmt2[,7:24])
-#'}}
+#' totalCorMat(wmt2[,7:24])}
 #'
 #' @references
 #' Watanabe, S. (1960).
diff --git a/R/toy.example.R b/R/toy.example.R
new file mode 100644
index 00000000..6a0ae86e
--- /dev/null
+++ b/R/toy.example.R
@@ -0,0 +1,19 @@
+#Toy Example Data----
+#' Toy Example Data
+#'
+#' A simulated dataset with 2 factors, three items per factor and  n = 500.
+#'
+#' @name toy.example
+#'
+#' @docType data
+#'
+#' @usage data(toy.example)
+#'
+#' @format A 500x6 response matrix
+#'
+#' @keywords datasets
+#'
+#' @examples
+#' data("toy.example")
+#'
+NULL
\ No newline at end of file
diff --git a/R/vn.entropy.R b/R/vn.entropy.R
index ecf5c423..59996281 100644
--- a/R/vn.entropy.R
+++ b/R/vn.entropy.R
@@ -21,18 +21,14 @@
 #' idx <- na.omit(match(gsub("-", "", unlist(psychTools::spi.keys[1:5])), colnames(psychTools::spi)))
 #' items <- psychTools::spi[,idx]
 #' 
-#' \donttest{# Estimate EGA
-#' ega.spi <- EGA(
-#'   data = items, model = "glasso",
-#'   plot.EGA = FALSE # No plot for CRAN checks
-#' )
+#' \dontrun{# Estimate EGA
+#' ega.spi <- EGA(data = items, model = "glasso")
 #' 
 #' # Compute entropy indices
 #' vn.entropy(
 #'   data = ega.spi$correlation,
 #'   structure = ega.spi$wc
-#' )
-#' }
+#' )}
 #'
 #' @references 
 #' Golino, H., Moulder, R. G., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Nesselroade, J., Sadana, R., Thiyagarajan, J. A., & Boker, S. M. (2020).
diff --git a/data/sim.dynEGA.RData b/data/sim.dynEGA.RData
index eb43bc09..b9f761b9 100644
Binary files a/data/sim.dynEGA.RData and b/data/sim.dynEGA.RData differ
diff --git a/man/CFA.Rd b/man/CFA.Rd
index 78fcc8b0..6ed47763 100644
--- a/man/CFA.Rd
+++ b/man/CFA.Rd
@@ -44,7 +44,8 @@ Verifies the fit of the structure suggested by \code{\link[EGAnet]{EGA}} using c
 # Load data
 wmt <- wmt2[,7:24]
 
-\donttest{# Estimate EGA
+\dontrun{
+# Estimate EGA
 ega.wmt <- EGA(
   data = wmt,
   plot.EGA = FALSE # No plot for CRAN checks
diff --git a/man/EBICglasso.qgraph.Rd b/man/EBICglasso.qgraph.Rd
index ec61bcd9..d9fc2108 100644
--- a/man/EBICglasso.qgraph.Rd
+++ b/man/EBICglasso.qgraph.Rd
@@ -69,7 +69,8 @@ is computed using \code{\link[qgraph]{wi2net}} and returned.
 # Obtain data
 wmt <- wmt2[,7:24]
 
-\donttest{# Compute graph with tuning = 0 (BIC)
+\dontrun{
+# Compute graph with tuning = 0 (BIC)
 BICgraph <- EBICglasso.qgraph(
   data = wmt, gamma = 0
 )
diff --git a/man/EGA.Rd b/man/EGA.Rd
index e5877abe..fc0d3ff9 100644
--- a/man/EGA.Rd
+++ b/man/EGA.Rd
@@ -229,7 +229,8 @@ can be used to estimate the number of communities (see examples).
 # Obtain data
 wmt <- wmt2[,7:24]
 
-\donttest{# Estimate EGA
+\dontrun{
+# Estimate EGA
 ega.wmt <- EGA(
   data = wmt,
   plot.EGA = FALSE # No plot for CRAN checks
@@ -243,27 +244,23 @@ methods.section(ega.wmt)
 
 # Estimate EGAtmfg
 ega.wmt.tmfg <- EGA(
-  data = wmt, model = "TMFG",
-  plot.EGA = FALSE # No plot for CRAN checks
+  data = wmt, model = "TMFG"
 )
 
 # Estimate EGA with Louvain algorithm
 ega.wmt.louvain <- EGA(
-  data = wmt, algorithm = "louvain",
-  plot.EGA = FALSE # No plot for CRAN checks
+  data = wmt, algorithm = "louvain"
 )
 
 # Estimate EGA with Leiden algorithm
 ega.wmt.leiden <- EGA(
-  data = wmt, algorithm = "leiden",
-  plot.EGA = FALSE # No plot for CRAN checks
+  data = wmt, algorithm = "leiden"
 )
 
 # Estimate EGA with Spinglass algorithm
 ega.wmt.spinglass <- EGA(
   data = wmt,
-  algorithm = igraph::cluster_spinglass, # any {igraph} algorithm
-  plot.EGA = FALSE # No plot for CRAN checks
+  algorithm = igraph::cluster_spinglass
 )}
 
 }
diff --git a/man/EGA.estimate.Rd b/man/EGA.estimate.Rd
index 9d580995..0731d32f 100644
--- a/man/EGA.estimate.Rd
+++ b/man/EGA.estimate.Rd
@@ -154,7 +154,8 @@ can be used to estimate the number of communities (see examples).
 # Obtain data
 wmt <- wmt2[,7:24]
 
-\donttest{# Estimate EGA
+\dontrun{
+# Estimate EGA
 ega.wmt <- EGA.estimate(data = wmt)
 
 # Estimate EGAtmfg
diff --git a/man/EGA.fit.Rd b/man/EGA.fit.Rd
index 5cb1717c..9416de41 100644
--- a/man/EGA.fit.Rd
+++ b/man/EGA.fit.Rd
@@ -136,7 +136,8 @@ algorithm is varied and unique community solutions are compared using
 # Load data
 wmt <- wmt2[,7:24]
 
-\donttest{# Estimate EGA
+\dontrun{
+# Estimate EGA
 ega.wmt <- EGA(
   data = wmt, 
   plot.EGA = FALSE # No plot for CRAN checks
diff --git a/man/LCT.Rd b/man/LCT.Rd
index eb667292..030bf63d 100644
--- a/man/LCT.Rd
+++ b/man/LCT.Rd
@@ -69,14 +69,12 @@ with 240,000 samples per model with varying parameters.
 }
 \examples{
 \donttest{# Compute LCT
-## Network model
-LCT(data = wmt2[,7:24])
-
 ## Factor model
-LCT(data = psychTools::bfi[,1:25])
+LCT(data = psychTools::bfi[,1:25])}
 
+\dontrun{
 # Dynamic LCT
-LCT(sim.dynEGA[sim.dynEGA$ID == 1,1:20], dynamic = TRUE)}
+LCT(sim.dynEGA[sim.dynEGA$ID == 1,1:24], dynamic = TRUE)}
 
 
 }
diff --git a/man/UVA.Rd b/man/UVA.Rd
index 133067fa..70b9dbf2 100644
--- a/man/UVA.Rd
+++ b/man/UVA.Rd
@@ -285,7 +285,8 @@ items <- psychTools::spi[,idx]
 key.ind <- match(colnames(items), as.character(psychTools::spi.dictionary$item_id))
 key <- as.character(psychTools::spi.dictionary$item[key.ind])
 
-\donttest{# Automated selection of local dependence (default)
+\dontrun{
+# Automated selection of local dependence (default)
 uva.results <- UVA(data = items, key = key)
 
 # Produce Methods section
@@ -293,8 +294,7 @@ methods.section(uva.results)}
 
 # Manual selection of local dependence
 if(interactive()){
-uva.results <- UVA(data = items, key = key, auto = FALSE)
-}
+uva.results <- UVA(data = items, key = key, auto = FALSE)}
 
 }
 \references{
diff --git a/man/boot.ergoInfo.Rd b/man/boot.ergoInfo.Rd
index 463b5740..4a0a1ce0 100644
--- a/man/boot.ergoInfo.Rd
+++ b/man/boot.ergoInfo.Rd
@@ -60,7 +60,7 @@ all participants in the data have the population network structure. Sampling is
 random noise is added to the edges of the population structure to simulate sampling variability. This noise
 follows a random uniform distribution ranging from -0.10 to 0.10. In addition, a proportion of edges are
 rewired to allow for slight variations on the population structure. The proportion of nodes that are rewired
-is sampled from a random uniform distribution between 0.20 to 0.25. This process is carried out for each
+is sampled from a random uniform distribution between 0.20 to 0.40. This process is carried out for each
 participant resulting in \emph{n} variations of the population structure. Afterward, EII is computed. This
 process is carried out for \emph{i} iterations (e.g., 100).
 
@@ -72,15 +72,19 @@ then the empirical data cannot be described by the population structure -- signi
 collapsing across to the population structure.
 }
 \examples{
-\donttest{# Dynamic EGA individual and population structures
+# Obtain simulated data
+sim.data <- sim.dynEGA
+
+\dontrun{
+# Dynamic EGA individual and population structures
 dyn1 <- dynEGA.ind.pop(
-  data = sim.dynEGA[,-c(22)], n.embed = 5, tau = 1,
-  delta = 1, id = 21, use.derivatives = 1,
+  data = sim.dynEGA[,-26], n.embed = 5, tau = 1,
+  delta = 1, id = 25, use.derivatives = 1,
   model = "glasso", ncores = 2, corr = "pearson"
 )
 
 # Empirical Ergodicity Information Index
-eii1 <- ergoInfo(dynEGA.object = dyn1, use = "edge.list")
+eii1 <- ergoInfo(dynEGA.object = dyn1, use = "weighted")
 
 # Bootstrap Test for Ergodicity Information Index
 testing.ergoinfo <- boot.ergoInfo(
diff --git a/man/boot.wmt.Rd b/man/boot.wmt.Rd
index 3ff5f258..81c6b359 100644
--- a/man/boot.wmt.Rd
+++ b/man/boot.wmt.Rd
@@ -1,33 +1,21 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/boot.wmt.R, R/datasets.R
+% Please edit documentation in R/boot.wmt.R
 \docType{data}
 \name{boot.wmt}
 \alias{boot.wmt}
 \title{\code{\link[EGAnet]{bootEGA}} Results of \code{\link[EGAnet]{wmt2}}Data}
 \format{
 A list with 9 objects (see \code{\link[EGAnet]{bootEGA}})
-
-A list with 8 objects (see \code{\link[EGAnet]{bootEGA}})
 }
 \usage{
-data(boot.wmt)
-
 data(boot.wmt)
 }
 \description{
 \code{\link[EGAnet]{bootEGA}} results using the \code{"glasso"} model and 500 iterations
 of the Wiener Matrizen-Test 2 (WMT-2)
-
-\code{\link[EGAnet]{bootEGA}} Results of \code{\link[EGAnet]{wmt2}}Data
-}
-\details{
-\code{\link[EGAnet]{bootEGA}} results using the \code{"glasso"} model and 500 iterations
-of the Wiener Matrizen-Test 2 (WMT-2)
 }
 \examples{
 data("boot.wmt")
 
-data("boot.wmt")
-
 }
 \keyword{datasets}
diff --git a/man/bootEGA.Rd b/man/bootEGA.Rd
index 5321db32..97c80a85 100644
--- a/man/bootEGA.Rd
+++ b/man/bootEGA.Rd
@@ -309,10 +309,10 @@ correlations over the \emph{n} bootstraps.
 # Load data
 wmt <- wmt2[,7:24]
 
-\donttest{# Standard EGA example
+\dontrun{
+# Standard EGA example
 boot.wmt <- bootEGA(
-  data = wmt, iter = 100, # recommended 500
-  plot.typicalStructure = FALSE, # No plot for CRAN checks
+  data = wmt, iter = 500,
   type = "parametric", ncores = 2
 )
 
@@ -321,41 +321,36 @@ methods.section(boot.wmt)
 
 # Louvain example
 boot.wmt.louvain <- bootEGA(
-  data = wmt, iter = 100, # recommended 500
+  data = wmt, iter = 500,
   algorithm = "louvain",
-  plot.typicalStructure = FALSE, # No plot for CRAN checks
   type = "parametric", ncores = 2
 )
 
 # Spinglass example
 boot.wmt.spinglass <- bootEGA(
-  data = wmt, iter = 100, # recommended 500
+  data = wmt, iter = 500,
   algorithm = igraph::cluster_spinglass, # use any function from {igraph}
-  plot.typicalStructure = FALSE, # No plot for CRAN checks
   type = "parametric", ncores = 2
 )
 
 # EGA fit example
 boot.wmt.fit <- bootEGA(
-  data = wmt, iter = 100, # recommended 500
+  data = wmt, iter = 500,
   EGA.type = "EGA.fit",
-  plot.typicalStructure = FALSE, # No plot for CRAN checks
   type = "parametric", ncores = 2
 )
 
 # Hierarchical EGA example
 boot.wmt.hier <- bootEGA(
-  data = wmt, iter = 100, # recommended 500
+  data = wmt, iter = 500,
   EGA.type = "hierEGA",
-  plot.typicalStructure = FALSE, # No plot for CRAN checks
   type = "parametric", ncores = 2
 )
 
 # Random-intercept EGA example
 boot.wmt.ri <- bootEGA(
-  data = wmt, iter = 100, # recommended 500
+  data = wmt, iter = 500,
   EGA.type = "riEGA",
-  plot.typicalStructure = FALSE, # No plot for CRAN checks
   type = "parametric", ncores = 2
 )}
 
diff --git a/man/compare.EGA.plots.Rd b/man/compare.EGA.plots.Rd
index c6dada9d..c13a5a68 100644
--- a/man/compare.EGA.plots.Rd
+++ b/man/compare.EGA.plots.Rd
@@ -90,7 +90,8 @@ items <- psychTools::spi[,c(11:20)]
 sample1 <- items[sample(1:nrow(items), 1000),]
 sample2 <- items[sample(1:nrow(items), 1000),]
 
-\donttest{# Estimate EGAs
+\dontrun{
+# Estimate EGAs
 ega1 <- EGA(sample1)
 ega2 <- EGA(sample2)
 
diff --git a/man/depression.Rd b/man/depression.Rd
index 0e64ba5f..bf48d3da 100644
--- a/man/depression.Rd
+++ b/man/depression.Rd
@@ -1,30 +1,21 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/datasets.R, R/depression.R
+% Please edit documentation in R/depression.R
 \docType{data}
 \name{depression}
 \alias{depression}
 \title{Depression Data}
 \format{
-A 574x78 response matrix
-
 A 574x78 response matrix
 }
 \usage{
-data(depression)
-
 data(depression)
 }
 \description{
-A response matrix (n = 574) of the Beck Depression Inventory, Beck Anxiety Inventory and
-the Athens Insomnia Scale.
-
 A response matrix (n = 574) of the Beck Depression Inventory, Beck Anxiety Inventory and
 the Athens Insomnia Scale.
 }
 \examples{
 data("depression")
 
-data("depression")
-
 }
 \keyword{datasets}
diff --git a/man/dimensionStability.Rd b/man/dimensionStability.Rd
index 5ead9a83..087cd98d 100644
--- a/man/dimensionStability.Rd
+++ b/man/dimensionStability.Rd
@@ -39,10 +39,10 @@ times the original dimension is exactly replicated in across bootstrap samples
 # Load data
 wmt <- wmt2[,7:24]
 
-\donttest{# Estimate bootstrap EGA
+\dontrun{
+# Estimate bootstrap EGA
 boot.wmt <- bootEGA(
-  data = wmt, iter = 100, # recommended 500
-  plot.typicalStructure = FALSE, # No plot for CRAN checks
+  data = wmt, iter = 500,
   type = "parametric", ncores = 2
 )}
 
@@ -50,12 +50,12 @@ boot.wmt <- bootEGA(
 res <- dimensionStability(boot.wmt)
 res$dimension.stability
 
-\donttest{# Produce Methods section
+\dontrun{
+# Produce Methods section
 methods.section(
   boot.wmt,
   stats = "dimensionStability"
-)
-}
+)}
 
 
 }
diff --git a/man/dnn.weights.Rd b/man/dnn.weights.Rd
index 8eca4910..fe3ebdf8 100644
--- a/man/dnn.weights.Rd
+++ b/man/dnn.weights.Rd
@@ -1,26 +1,16 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/datasets.R, R/dnn.weights.R
+% Please edit documentation in R/dnn.weights.R
 \docType{data}
 \name{dnn.weights}
 \alias{dnn.weights}
 \title{Loadings Comparison Test Deep Learning Neural Network Weights}
 \format{
-A list of with a length of 4
-
 A list of with a length of 4
 }
 \usage{
-data(dnn.weights)
-
 data(dnn.weights)
 }
 \description{
-A list of weights from four different neural network models:
-random vs. non-random model (\code{r_nr_weights}),
-low correlation factor vs. network model (\code{lf_n_weights}),
-high correlation with variables less than or equal to factors vs. network model (\code{hlf_n_weights}), and
-high correlation with variables greater than factors vs. network model (\code{hgf_n_weights})
-
 A list of weights from four different neural network models:
 random vs. non-random model (\code{r_nr_weights}),
 low correlation factor vs. network model (\code{lf_n_weights}),
@@ -30,7 +20,5 @@ high correlation with variables greater than factors vs. network model (\code{hg
 \examples{
 data("dnn.weights")
 
-data("dnn.weights")
-
 }
 \keyword{datasets}
diff --git a/man/dynEGA.Rd b/man/dynEGA.Rd
index 0ebfd058..4af02299 100644
--- a/man/dynEGA.Rd
+++ b/man/dynEGA.Rd
@@ -189,10 +189,11 @@ GLLA is a filtering method for estimating derivatives from data that uses time d
 # Obtain data
 sim.dynEGA <- sim.dynEGA # bypasses CRAN checks
 
-\donttest{# Population structure
+\dontrun{
+# Population structure
 dyn.random <- dynEGA(
   data = sim.dynEGA, n.embed = 5, tau = 1,
-  delta = 1, id = 21, group = 22, use.derivatives = 1,
+  delta = 1, id = 25, group = 26, use.derivatives = 1,
   level = "population", ncores = 2, corr = "pearson"
 )
 
@@ -202,7 +203,7 @@ plot(dyn.random)
 # Group structure
 dyn.group <- dynEGA(
   data = sim.dynEGA, n.embed = 5, tau = 1,
-  delta = 1, id = 21, group = 22, use.derivatives = 1,
+  delta = 1, id = 25, group = 26, use.derivatives = 1,
   level = "group", ncores = 2, corr = "pearson"
 )
 
@@ -212,7 +213,7 @@ plot(dyn.group, ncol = 2, nrow = 1)
 # Intraindividual structure
 dyn.individual <- dynEGA(
   data = sim.dynEGA, n.embed = 5, tau = 1,
-  delta = 1, id = 21, group = 22, use.derivatives = 1,
+  delta = 1, id = 25, group = 26, use.derivatives = 1,
   level = "individual", ncores = 2, corr = "pearson"
 )
 
diff --git a/man/dynEGA.ind.pop.Rd b/man/dynEGA.ind.pop.Rd
index c3d5f7da..b84bbd6d 100644
--- a/man/dynEGA.ind.pop.Rd
+++ b/man/dynEGA.ind.pop.Rd
@@ -168,7 +168,7 @@ sim.dynEGA <- sim.dynEGA # bypasses CRAN checks
 \donttest{# Dynamic EGA individual and population structure
 dyn.ega1 <- dynEGA.ind.pop(
   data = sim.dynEGA, n.embed = 5, tau = 1,
-  delta = 1, id = 21, use.derivatives = 1,
+  delta = 1, id = 25, use.derivatives = 1,
   ncores = 2, corr = "pearson"
 )}
 
diff --git a/man/ega.wmt.Rd b/man/ega.wmt.Rd
index b5c6bd68..82b775e2 100644
--- a/man/ega.wmt.Rd
+++ b/man/ega.wmt.Rd
@@ -1,33 +1,21 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/datasets.R, R/ega.wmt.R
+% Please edit documentation in R/ega.wmt.R
 \docType{data}
 \name{ega.wmt}
 \alias{ega.wmt}
-\title{EGA WMT-2 Data}
+\title{\code{\link[EGAnet]{EGA}} Network of \code{\link[EGAnet]{wmt2}}Data}
 \format{
-A 17 x 17 adjacency matrix
-
 A 17 x 17 adjacency matrix
 }
 \usage{
-data(ega.wmt)
-
 data(ega.wmt)
 }
 \description{
-\code{\link[EGAnet]{EGA}} Network of \code{\link[EGAnet]{wmt2}}Data
-
-An \code{\link[EGAnet]{EGA}} using the \code{"glasso"} model of the
-Wiener Matrizen-Test 2 (WMT-2)
-}
-\details{
 An \code{\link[EGAnet]{EGA}} using the \code{"glasso"} model of the
 Wiener Matrizen-Test 2 (WMT-2)
 }
 \examples{
 data("ega.wmt")
 
-data("ega.wmt")
-
 }
 \keyword{datasets}
diff --git a/man/entropyFit.Rd b/man/entropyFit.Rd
index 395af8f0..f92323ad 100644
--- a/man/entropyFit.Rd
+++ b/man/entropyFit.Rd
@@ -34,11 +34,9 @@ Lower values suggest better fit of a structure to the data.
 # Load data
 wmt <- wmt2[,7:24]
 
-\donttest{# Estimate EGA model
-ega.wmt <- EGA(
-  data = wmt,
-  plot.EGA = FALSE # No plot for CRAN checks
-)}
+\dontrun{
+# Estimate EGA model
+ega.wmt <- EGA(data = wmt)}
 
 # Compute entropy indices
 entropyFit(data = wmt, structure = ega.wmt$wc)
diff --git a/man/ergoInfo.Rd b/man/ergoInfo.Rd
index 2ffd2e95..2874ffa4 100644
--- a/man/ergoInfo.Rd
+++ b/man/ergoInfo.Rd
@@ -50,17 +50,18 @@ Computes the Ergodicity Information Index
 # Obtain data
 sim.dynEGA <- sim.dynEGA # bypasses CRAN checks
 
-\donttest{# Dynamic EGA individual and population structure
+\dontrun{
+# Dynamic EGA individual and population structure
 dyn.ega1 <- dynEGA.ind.pop(
-  data = sim.dynEGA, n.embed = 5, tau = 1,
-  delta = 1, id = 21, use.derivatives = 1,
+  data = sim.dynEGA[,-26], n.embed = 5, tau = 1,
+  delta = 1, id = 25, use.derivatives = 1,
   ncores = 2, corr = "pearson"
 )
 
 # Compute empirical ergodicity information index
 eii <- ergoInfo(
   dynEGA.object = dyn.ega1,
-  use = "edge.list"
+  use = "weighted"
 )}
 
 }
diff --git a/man/glla.Rd b/man/glla.Rd
index 8ea93aa5..c3787db7 100644
--- a/man/glla.Rd
+++ b/man/glla.Rd
@@ -37,12 +37,10 @@ Estimates the derivatives of a time series using generalized local linear approx
 GLLA is a filtering method for estimating derivatives from data that uses time delay embedding and a variant of Savitzky-Golay filtering to accomplish the task.
 }
 \examples{
-
 # A time series with 8 time points
 tseries <- 49:56
 deriv.tseries <- glla(tseries, n.embed = 4, tau = 1, delta = 1, order = 2)
 
-
 }
 \references{
 Boker, S. M., Deboeck, P. R., Edler, C., & Keel, P. K. (2010)
diff --git a/man/hierEGA.Rd b/man/hierEGA.Rd
index 6c7e4193..ac0400ed 100644
--- a/man/hierEGA.Rd
+++ b/man/hierEGA.Rd
@@ -286,11 +286,11 @@ estimate factor or network scores, which are used to estimate the higher-order d
 # Obtain example data
 data <- optimism
 
-\donttest{# hierEGA example
+\dontrun{
+# hierEGA example
 opt.hier<- hierEGA(
   data = optimism,
-  algorithm = "louvain",
-  plot.EGA = FALSE # no plots for CRAN check
+  algorithm = "louvain"
 )}
 
 }
diff --git a/man/infoCluster.Rd b/man/infoCluster.Rd
index c8c2f280..4b7dc375 100644
--- a/man/infoCluster.Rd
+++ b/man/infoCluster.Rd
@@ -36,18 +36,16 @@ change in the clusters identified
 # Obtain data
 sim.dynEGA <- sim.dynEGA # bypasses CRAN checks
 
-\donttest{# Dynamic EGA individual and population structure
+\dontrun{
+# Dynamic EGA individual and population structure
 dyn.ega1 <- dynEGA.ind.pop(
   data = sim.dynEGA, n.embed = 5, tau = 1,
-  delta = 1, id = 21, use.derivatives = 1, 
+  delta = 1, id = 25, use.derivatives = 1, 
   ncores = 2, corr = "pearson"
 )
 
 # Perform information-theoretic clustering
-clust1 <- infoCluster(
-  dynEGA.object = dyn.ega1,
-  plot.cluster = FALSE # No plot for CRAN checks
-)}
+clust1 <- infoCluster(dynEGA.object = dyn.ega1)}
 
 }
 \author{
diff --git a/man/intelligenceBattery.Rd b/man/intelligenceBattery.Rd
index 9ad2ebd7..07637e93 100644
--- a/man/intelligenceBattery.Rd
+++ b/man/intelligenceBattery.Rd
@@ -1,30 +1,21 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/datasets.R, R/intelligenceBattery.R
+% Please edit documentation in R/intelligenceBattery.R
 \docType{data}
 \name{intelligenceBattery}
 \alias{intelligenceBattery}
 \title{Intelligence Data}
 \format{
-A 1185x125 response matrix
-
 A 1185x125 response matrix
 }
 \usage{
-data(intelligenceBattery)
-
 data(intelligenceBattery)
 }
 \description{
-A response matrix (n = 1152) of the International Cognitive Ability Resource (ICAR)
-intelligence battery developed by Condon and Revelle (2016).
-
 A response matrix (n = 1152) of the International Cognitive Ability Resource (ICAR)
 intelligence battery developed by Condon and Revelle (2016).
 }
 \examples{
 data("intelligenceBattery")
 
-data("intelligenceBattery")
-
 }
 \keyword{datasets}
diff --git a/man/invariance.Rd b/man/invariance.Rd
index 2718dc09..5b850f03 100644
--- a/man/invariance.Rd
+++ b/man/invariance.Rd
@@ -233,7 +233,7 @@ wmt <- wmt2[-1,7:24]
 # Groups
 groups <- rep(1:2, each = nrow(wmt) / 2)
 
-\donttest{
+\dontrun{
 # Measurement invariance
 results <- invariance(wmt, groups, ncores = 2)}
 
diff --git a/man/itemStability.Rd b/man/itemStability.Rd
index 95dc3dd0..7903b5fe 100644
--- a/man/itemStability.Rd
+++ b/man/itemStability.Rd
@@ -74,7 +74,8 @@ network loading for each item in each dimension (\code{item.loadings}).
 # Load data
 wmt <- wmt2[,7:24]
 
-\donttest{# Standard EGA example
+\dontrun{
+# Standard EGA example
 boot.wmt <- bootEGA(
   data = wmt, iter = 100, # recommended 500
   plot.typicalStructure = FALSE, # No plot for CRAN checks
@@ -82,10 +83,7 @@ boot.wmt <- bootEGA(
 )
 
 # Standard item stability 
-wmt.is <- itemStability(
-  boot.wmt,
-  IS.plot = FALSE # NO plot for CRAN checks
-)
+wmt.is <- itemStability(boot.wmt)
 
 # Produce Methods section
 methods.section(
@@ -95,45 +93,33 @@ methods.section(
 
 # EGA fit example
 boot.wmt.fit <- bootEGA(
-  data = wmt, iter = 100, # recommended 500
+  data = wmt, iter = 500,
   EGA.type = "EGA.fit",
-  plot.typicalStructure = FALSE, # No plot for CRAN checks
   type = "parametric", ncores = 2
 )
 
 # EGA fit item stability 
-wmt.is.fit <- itemStability(
-  boot.wmt.fit,
-  IS.plot = FALSE # NO plot for CRAN checks
-)
+wmt.is.fit <- itemStability(boot.wmt.fit)
 
 # Hierarchical EGA example
 boot.wmt.hier <- bootEGA(
-  data = wmt, iter = 100, # recommended 500
+  data = wmt, iter = 500,
   EGA.type = "hierEGA",
-  plot.typicalStructure = FALSE, # No plot for CRAN checks
   type = "parametric", ncores = 2
 )
 
 # Hierarchical EGA item stability 
-wmt.is.hier <- itemStability(
-  boot.wmt.hier,
-  IS.plot = FALSE # NO plot for CRAN checks
-)
+wmt.is.hier <- itemStability(boot.wmt.hier)
 
 # Random-intercept EGA example
 boot.wmt.ri <- bootEGA(
-  data = wmt, iter = 100, # recommended 500
+  data = wmt, iter = 500,
   EGA.type = "riEGA",
-  plot.typicalStructure = FALSE, # No plot for CRAN checks
   type = "parametric", ncores = 2
 )
 
 # Random-intercept EGA item stability 
-wmt.is.ri <- itemStability(
-  boot.wmt.ri,
-  IS.plot = FALSE # NO plot for CRAN checks
-)}
+wmt.is.ri <- itemStability(boot.wmt.ri)}
 
 }
 \references{
diff --git a/man/jsd.ergoInfo.Rd b/man/jsd.ergoInfo.Rd
index 531807a2..a80380dc 100644
--- a/man/jsd.ergoInfo.Rd
+++ b/man/jsd.ergoInfo.Rd
@@ -70,10 +70,11 @@ proportion is \strong{fewer} than the maximum allowable proportion, then the sys
 is determined to be ergodic
 }
 \examples{
-\donttest{# Dynamic EGA individual and population structures
+\dontrun{
+# Dynamic EGA individual and population structures
 dyn1 <- dynEGA.ind.pop(
-  data = sim.dynEGA[,-c(22)], n.embed = 5, tau = 1,
-  delta = 1, id = 21, use.derivatives = 1,
+  data = sim.dynEGA[,-26], n.embed = 5, tau = 1,
+  delta = 1, id = 25, use.derivatives = 1,
   model = "glasso", ncores = 2, corr = "pearson"
 )
 
diff --git a/man/network.descriptives.Rd b/man/network.descriptives.Rd
index 518efe2f..eef5623a 100644
--- a/man/network.descriptives.Rd
+++ b/man/network.descriptives.Rd
@@ -57,11 +57,8 @@ Computes descriptive statistics for network models
 # Load data
 wmt <- wmt2[,7:24]
 
-\donttest{# EGA example
-ega.wmt <- EGA(
-  data = wmt,
-  plot.EGA = FALSE # No plot for CRAN
-)}
+\dontrun{# EGA example
+ega.wmt <- EGA(data = wmt)}
 
 # Compute descriptives
 network.descriptives(ega.wmt)
diff --git a/man/optimism.Rd b/man/optimism.Rd
index a8cb3a3d..2e16888d 100644
--- a/man/optimism.Rd
+++ b/man/optimism.Rd
@@ -1,37 +1,24 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/datasets.R, R/optimism.R
+% Please edit documentation in R/optimism.R
 \docType{data}
 \name{optimism}
 \alias{optimism}
 \title{Optimism Data}
 \format{
-A 282x10 response matrix
-
 A 282x10 response matrix
 }
 \usage{
-data(optimism)
-
 data(optimism)
 }
 \description{
-A response matrix (n = 282) containing responses to 10 items of the Revised Life
-Orientation Test (LOT-R), developed by Scheier, Carver, & Bridges (1994).
-
 A response matrix (n = 282) containing responses to 10 items of the Revised Life
 Orientation Test (LOT-R), developed by Scheier, Carver, & Bridges (1994).
 }
 \examples{
 data("optimism")
 
-data("optimism")
-
 }
 \references{
-Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994).
-Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): a reevaluation of the Life Orientation Test.
-\emph{Journal of Personality and Social Psychology}, \emph{67}, 1063-1078.
-
 Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994).
 Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): a reevaluation of the Life Orientation Test.
 \emph{Journal of Personality and Social Psychology}, \emph{67}, 1063-1078.
diff --git a/man/prime.num.Rd b/man/prime.num.Rd
index b55ccb88..929b39f9 100644
--- a/man/prime.num.Rd
+++ b/man/prime.num.Rd
@@ -1,30 +1,21 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/datasets.R, R/prime.num.R
+% Please edit documentation in R/prime.num.R
 \docType{data}
 \name{prime.num}
 \alias{prime.num}
 \title{Prime Numbers through 100,000}
 \format{
-A 1185x24 response matrix
-
 A 1185x24 response matrix
 }
 \usage{
-data(prime.num)
-
 data(prime.num)
 }
 \description{
-Numeric vector of primes generated from the primes package. Used in
-the function \code{[EGAnet]{ergoInfo}}. Not for general use
-
 Numeric vector of primes generated from the primes package. Used in
 the function \code{[EGAnet]{ergoInfo}}. Not for general use
 }
 \examples{
 data("prime.num")
 
-data("prime.num")
-
 }
 \keyword{datasets}
diff --git a/man/riEGA.Rd b/man/riEGA.Rd
index c31a9631..5340f764 100644
--- a/man/riEGA.Rd
+++ b/man/riEGA.Rd
@@ -243,9 +243,8 @@ reversed items in the database
 # Obtain example data
 data <- optimism
 
-\donttest{# riEGA example
-opt.res <- riEGA(data = optimism)
-}
+\dontrun{# riEGA example
+opt.res <- riEGA(data = optimism)}
 
 }
 \references{
diff --git a/man/sim.dynEGA.Rd b/man/sim.dynEGA.Rd
index 43d8f9ad..2f608269 100644
--- a/man/sim.dynEGA.Rd
+++ b/man/sim.dynEGA.Rd
@@ -1,30 +1,34 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/datasets.R, R/sim.dynEGA.R
+% Please edit documentation in R/sim.dynEGA.R
 \docType{data}
 \name{sim.dynEGA}
 \alias{sim.dynEGA}
 \title{sim.dynEGA Data}
 \format{
-A 10000x22 multivariate time series
-
-A 10000x22 multivariate time series
+A 5000 x 26 multivariate time series
 }
 \usage{
-data(sim.dynEGA)
-
 data(sim.dynEGA)
 }
 \description{
-A simulated (multivariate time series) data with 20 variables, 200 individual observations,
-50 time points per individual and 2 groups of individuals.
+A simulated (multivariate time series) data with 24 variables,
+100 individual observations, 50 time points per individual and 
+2 groups of individuals.
+}
+\details{
+Data were generated using the \code{\link[EGAnet]{simDFM}} function
+with the following arguments:
 
-A simulated (multivariate time series) data with 20 variables, 200 individual observations,
-50 time points per individual and 2 groups of individuals.
+\code{simDFM(
+  variab = 12, timep = 50, nfact = 2,
+  error = 0.175, dfm = "DAFS",
+  loadings = 0.70, autoreg = 0.50,
+  crossreg = 0.10, var.shock = 0.09,
+  cov.shock = 0.30, variation = TRUE
+)}
 }
 \examples{
 data("sim.dynEGA")
 
-data("sim.dynEGA")
-
 }
 \keyword{datasets}
diff --git a/man/simDFM.Rd b/man/simDFM.Rd
index 820421f0..e586047d 100644
--- a/man/simDFM.Rd
+++ b/man/simDFM.Rd
@@ -15,7 +15,8 @@ simDFM(
   crossreg,
   var.shock,
   cov.shock,
-  burnin = 1000
+  burnin = 1000,
+  variation = FALSE
 )
 }
 \arguments{
@@ -53,6 +54,11 @@ This is the default method}
 \item{cov.shock}{Magnitude of the random shock covariance}
 
 \item{burnin}{Number of n first samples to discard when computing the factor scores. Defaults to 1000.}
+
+\item{variation}{Boolean.
+Whether parameters should be varied.
+Defaults to \code{FALSE}.
+Set to \code{TRUE} to add slight variation to all parameters}
 }
 \description{
 Function to simulate data following a dynamic factor model (DFM). Two DFMs are currently available:
diff --git a/man/tefi.Rd b/man/tefi.Rd
index cd393107..435b9f84 100644
--- a/man/tefi.Rd
+++ b/man/tefi.Rd
@@ -31,9 +31,7 @@ wmt <- wmt2[,7:24]
 
 \dontrun{
 # Estimate EGA model
-ega.wmt <- EGA(data = wmt, model = "glasso")
-
-}
+ega.wmt <- EGA(data = wmt, model = "glasso")}
 
 # Compute entropy indices
 tefi(data = ega.wmt$correlation, structure = ega.wmt$wc)
diff --git a/man/totalCorMat.Rd b/man/totalCorMat.Rd
index 666265cd..54cd2794 100644
--- a/man/totalCorMat.Rd
+++ b/man/totalCorMat.Rd
@@ -18,10 +18,8 @@ Computes the pairwise total correlation for a dataset
 }
 \examples{
 \dontrun{
-\donttest{
 # Compute total correlation
-totalCorMat(wmt2[,7:24])
-}}
+totalCorMat(wmt2[,7:24])}
 
 }
 \references{
diff --git a/man/toy.example.Rd b/man/toy.example.Rd
index 090621d6..184b9499 100644
--- a/man/toy.example.Rd
+++ b/man/toy.example.Rd
@@ -1,20 +1,20 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/datasets.R
-\docType{data}
-\name{toy.example}
-\alias{toy.example}
-\title{Toy Example Data}
-\format{
-A 500x6 response matrix
-}
-\usage{
-data(toy.example)
-}
-\description{
-A simulated dataset with 2 factors, three items per factor and  n = 500.
-}
-\examples{
-data("toy.example")
-
-}
-\keyword{datasets}
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/toy.example.R
+\docType{data}
+\name{toy.example}
+\alias{toy.example}
+\title{Toy Example Data}
+\format{
+A 500x6 response matrix
+}
+\usage{
+data(toy.example)
+}
+\description{
+A simulated dataset with 2 factors, three items per factor and  n = 500.
+}
+\examples{
+data("toy.example")
+
+}
+\keyword{datasets}
diff --git a/man/vn.entropy.Rd b/man/vn.entropy.Rd
index 7d0cae3e..b55995eb 100644
--- a/man/vn.entropy.Rd
+++ b/man/vn.entropy.Rd
@@ -30,18 +30,14 @@ Lower values suggest better fit of a structure to the data.
 idx <- na.omit(match(gsub("-", "", unlist(psychTools::spi.keys[1:5])), colnames(psychTools::spi)))
 items <- psychTools::spi[,idx]
 
-\donttest{# Estimate EGA
-ega.spi <- EGA(
-  data = items, model = "glasso",
-  plot.EGA = FALSE # No plot for CRAN checks
-)
+\dontrun{# Estimate EGA
+ega.spi <- EGA(data = items, model = "glasso")
 
 # Compute entropy indices
 vn.entropy(
   data = ega.spi$correlation,
   structure = ega.spi$wc
-)
-}
+)}
 
 }
 \references{
diff --git a/man/wmt2.Rd b/man/wmt2.Rd
index cf9fc373..7284a1c3 100644
--- a/man/wmt2.Rd
+++ b/man/wmt2.Rd
@@ -1,28 +1,20 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/datasets.R, R/wmt2.R
+% Please edit documentation in R/wmt2.R
 \docType{data}
 \name{wmt2}
 \alias{wmt2}
 \title{WMT-2 Data}
 \format{
-A 1185x24 response matrix
-
 A 1185x24 response matrix
 }
 \usage{
-data(wmt2)
-
 data(wmt2)
 }
 \description{
-A response matrix (n = 1185) of the Wiener Matrizen-Test 2 (WMT-2).
-
 A response matrix (n = 1185) of the Wiener Matrizen-Test 2 (WMT-2).
 }
 \examples{
 data("wmt2")
 
-data("wmt2")
-
 }
 \keyword{datasets}