documentation updates to pass CRAN

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")),

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
 

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

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
 #' )

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

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

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

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

R/UVA.R

@@ -259,16 +259,16 @@
 #' 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
 #' 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
 #' # Simulation using \code{UVA} \cr

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]])
       }

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
 #' )}
 #'

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)
 #'