Minor Changes Fix

DESCRIPTION

@@ -24,7 +24,7 @@ Authors@R: c(
   )
 Author: Cesar Merino-Soto [aut, ctb, rev] (ORCID: <https://orcid.org/0000-0001-8049-7069>), Jose Livia Segovia [aut, ctb] (ORCID: <https://orcid.org/0000-0003-2226-3349>), Diego Livia Ortiz [aut, cre, ctb, rev] (ORCID: <https://orcid.org/0000-0002-2107-3140>)
 Maintainer: Diego Livia Ortiz <diegolivia@hotmail.com>
-Description: Provides tools for content validity studies, including functions to calculate content validity coefficients from ratings by expert or experiential judges. It implements CVC(), CVI(), CVIR(), CVR(), and Vaiken() together with asymmetric confidence intervals for bounded coefficients and methods for comparing independent coefficients. The methods are based on Aiken (1980, 1985) <doi:10.1177/001316448004000419>, Wilson (1927) <doi:10.2307/2276774>, and Merino-Soto (2018) <doi:10.6018/analesps.34.3.283481>.
+Description: Content validity studies, including functions to calculate content validity coefficients from ratings by expert or experiential judges. It implements CVC(), CVI(), CVIR(), CVR(), and Vaiken() together with asymmetric confidence intervals for bounded coefficients and methods for comparing independent coefficients. The methods are based on Aiken (1980, 1985) <doi:10.1177/001316448004000419>, Wilson (1927) <doi:10.2307/2276774>, and Merino-Soto (2018) <doi:10.6018/analesps.34.3.283481>.
 License: GPL (>= 3)
 URL: https://github.com/Diegolivia/ValCont
 BugReports: https://github.com/Diegolivia/ValCont/issues

R/CIR.R

@@ -65,7 +65,7 @@
 #' Reference Online}. doi:10.1002/9781118445112.stat08085
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' # Suppose 'group1' and 'group2' contain columns 'est', 'low', and 'high'
 #' res <- CIR(group1, group2, coef.col = "est", lwr.col = "low", upr.col = "high")
 #' print(res)

R/HAiken.R

@@ -51,11 +51,19 @@
 #'   \emph{Educational and Psychological Measurement, 45}, 131-142.
 #'
 #' @examples
-#' \dontrun{
-#' # Sample data: 8 items, 18 judges, ratings 1-4
-#' datos <- data.frame(t(file1[, c("claA6", "claA18", "claA2", "claA16",
-#'                                 "claA4", "claA12", "claA9", "claA21")]))
-#' Haiken(datos, ncat = 4, conf.level = .90, overall = TRUE, B = 1000, ci.type = "logit")
+#' \donttest{
+#' set.seed(123)
+#' datos <- data.frame(
+#'   item1 = sample(1:4, 8, replace = TRUE),
+#'   item2 = sample(1:4, 8, replace = TRUE),
+#'   item3 = sample(1:4, 8, replace = TRUE),
+#'   item4 = sample(1:4, 8, replace = TRUE),
+#'   item5 = sample(1:4, 8, replace = TRUE),
+#'   item6 = sample(1:4, 8, replace = TRUE),
+#'   item7 = sample(1:4, 8, replace = TRUE),
+#'   item8 = sample(1:4, 8, replace = TRUE)
+#' )
+#' Haiken(datos, ncat = 4, conf.level = .90, overall = TRUE, B = 200, ci.type = "logit")
 #' }
 #'
 #' @export

R/HT.R

@@ -141,10 +141,10 @@
 #' ratings. \emph{Applied Measurement in Education, 17}(4), 359-370.
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' ## Example structure (toy data)
 #' set.seed(123)
-#' HTmult.data <- data.frame(
+#' dat <- data.frame(
 #'   item1.c1 = sample(1:5, 50, replace = TRUE),
 #'   item1.c2 = sample(1:5, 50, replace = TRUE),
 #'   item1.c3 = sample(1:5, 50, replace = TRUE),
@@ -153,14 +153,15 @@
 #'   item2.c3 = sample(1:5, 50, replace = TRUE)
 #' )
 #'
-#'HTmult(
+#' HTmult(
 #'   data       = dat,
 #'   items      = c("item1", "item2"),
 #'   constructs = c("c1", "c2", "c3"),
 #'   key        = c(item1 = "c2", item2 = "c3"),
 #'   anchors    = 5,
 #'   ci         = TRUE,
-#'   conf.level = 0.95)
+#'   conf.level = 0.95
+#' )
 #' }
 #'
 #' @export

R/MDScontent.R

@@ -95,39 +95,19 @@
 #'
 #' @importFrom rlang .data
 #' @examples
-#' # This simulated data has the following structure: 12 judges evaluated the fit
-#' # of 6 items on 4 traits. The data therefore contain 12 rows and 24 columns,
-#' # with four columns for each item, one column per trait.
-#'
-#' # In the validity survey, the items were presented in 12 rows, and each item
-#' # was evaluated for its correspondence to the four traits. The columns follow
-#' # this pattern:
-#'
-#' # item item1.trait1 item1.trait2 item1.trait3 item1.trait4 item2.trait1 ...
-#'
-#' # The theoretical correspondence used below is:
-#'
-#' # Item 1 -> Trait 1
-#' # Item 2 -> Trait 1
-#' # Item 3 -> Trait 2
-#' # Item 4 -> Trait 2
-#' # Item 5 -> Trait 3
-#' # Item 6 -> Trait 4
-#'
-#' dat2 <- matrix(c(
-#'   5,2,2,1,  5,1,2,1,  2,5,1,2,  1,5,2,1,  2,1,5,2,  1,2,2,5,
-#'   5,1,2,2,  4,2,1,1,  1,5,2,1,  2,5,1,1,  1,2,5,2,  2,1,1,5,
-#'   4,2,1,1,  5,1,2,1,  2,4,1,2,  1,5,2,2,  2,1,5,1,  1,2,2,5,
-#'   5,1,2,1,  5,2,1,1,  1,5,2,1,  2,4,1,2,  1,2,5,2,  2,1,1,5,
-#'   4,2,1,2,  5,1,2,1,  2,5,1,1,  1,5,2,1,  2,1,4,2,  1,2,2,4,
-#'   5,1,2,1,  4,2,1,2,  1,5,2,1,  2,5,1,1,  1,2,5,1,  2,1,2,5,
-#'   5,2,1,1,  5,1,2,1,  2,4,1,2,  1,5,2,1,  2,1,5,2,  1,2,1,5,
-#'   4,1,2,1,  5,2,1,1,  1,5,2,2,  2,4,1,1,  1,2,5,1,  2,1,2,5,
-#'   5,2,1,1,  4,1,2,2,  2,5,1,1,  1,5,2,1,  2,1,5,2,  1,2,1,5,
-#'   5,1,2,2,  5,2,1,1,  1,5,2,1,  2,5,1,2,  1,2,5,1,  2,1,2,5,
-#'   4,2,1,1,  5,1,2,1,  2,4,1,2,  1,5,2,1,  2,1,4,2,  1,2,1,5,
-#'   5,1,2,1,  4,2,1,1,  1,5,2,1,  2,4,1,2,  1,2,5,1,  2,1,2,5),
-#'   nrow = 12,
+#' \donttest{
+#' # A small toy example with 6 items judged on 4 traits.
+#' set.seed(123)
+#' dat2 <- matrix(
+#'   c(
+#'     5, 2, 2, 1,  5, 1, 2, 1,  2, 5, 1, 2,  1, 5, 2, 1,
+#'     2, 1, 5, 2,  1, 2, 2, 5,  5, 1, 2, 2,  4, 2, 1, 1,
+#'     1, 5, 2, 1,  2, 5, 1, 1,  1, 2, 5, 2,  2, 1, 1, 5,
+#'     5, 1, 2, 1,  5, 2, 1, 1,  1, 5, 2, 1,  2, 4, 1, 2,
+#'     1, 2, 5, 2,  2, 1, 1, 5,  4, 2, 1, 2,  5, 1, 2, 1,
+#'     2, 5, 1, 1,  1, 5, 2, 1,  2, 1, 4, 2,  1, 2, 2, 4
+#'   ),
+#'   nrow = 6,
 #'   byrow = TRUE
 #' )
 #' key <- c(1, 1, 2, 2, 3, 4)
@@ -141,6 +121,7 @@
 #'   mds = "nonmetric",
 #'   display = "items"
 #' )
+#' }
 #'
 #' @export
 MDScontent <- function(
@@ -413,6 +394,12 @@ MDScontent <- function(
                         ifelse(mds == "nonmetric", paste0(" | STRESS: ", signif(stress, 3)), ""))
     )
 
+  if ((display == "items" || display == "both") && isTRUE(label.items)) {
+    if (!requireNamespace("ggrepel", quietly = TRUE)) {
+      stop("Package 'ggrepel' is required for label.items = TRUE. Install it with install.packages('ggrepel').")
+    }
+  }
+
   if (display == "items" || display == "both") {
     if (!is.null(hull_data)) {
       p <- p + ggplot2::geom_polygon(data = hull_data,

R/SVALplot.R

@@ -23,7 +23,7 @@
 #'\code{\link[ValCont:SVALsingle]{ValCont::SVALsingle}} for single-item analysis.
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' results <- SVALmult(data = data.gais, columns = c("gais2", "gais3"))
 #' SVALplot(results, item = "gais3", type = "svc", target = 4)
 #' SVALplot(results, item = "gais3", type = "psa", labels = c("Anger", "Fear", "Joy", "Sadness"))}