Refactor documentation and update content validity functions

.Rbuildignore

@@ -6,4 +6,6 @@ README.html
 ^\.github$
 ^\.git$
 ^validate_package\.R$
-^validate_report\.json$
+^validate_report\.json$
+^AGENTS\.md$
+^build_package\.R$

.gitignore

@@ -54,3 +54,4 @@ rsconnect/
 validate_report.json
 validate_package.R
 build_package.R
+AGENTS.md

DESCRIPTION

@@ -31,6 +31,9 @@ LazyData: true
 Imports: 
     boot,
     ggplot2,
+    ggrepel,
+    MASS,
+    rlang,
     stats,
     utils
 Depends: 

NAMESPACE

@@ -26,6 +26,8 @@ export(SVALmult)
 export(SVALplot)
 export(SVALsingle)
 export(Vaiken)
+export(Vaikenpub)
+export(minimumCV)
 importFrom(boot,
   boot,
   boot.ci
@@ -42,6 +44,7 @@ importFrom(ggplot2,
   theme,
   theme_minimal
 )
+importFrom(rlang,.data)
 importFrom(stats,
   complete.cases,
   na.omit,

R/CID.R

@@ -30,9 +30,9 @@
 #'
 #'Merino-Soto, C. (2023). Coeficientes V de Aiken: diferencias en los juicios de validez de contenido. MHSalud, 20(1), 23-32. https://doi.org/10.15359/mhs.20-1.3
 #'
-#'Singer, J. (2010). Construction of confidence limits about effect measures: A general approach, by G. Y. Zou and A. Donner, Statistics in Medicine 2008; 27:1693-1702. Statistics in Medicine, 29(16), 1757–1759. https://doi.org/10.1002/sim.3887
+#'Singer, J. (2010). Construction of confidence limits about effect measures: A general approach, by G. Y. Zou and A. Donner, Statistics in Medicine 2008; 27:1693-1702. Statistics in Medicine, 29(16), 1757-1759. https://doi.org/10.1002/sim.3887
 #'
-#'Zou, G.Y. and Donner, A. (2008) Construction of confidence limits about effect measures: a general approach. Stat. Med. 27, 1693–1702. https://doi.org//10.1002/sim.3095
+#'Zou, G.Y. and Donner, A. (2008) Construction of confidence limits about effect measures: a general approach. Stat. Med. 27, 1693-1702. https://doi.org//10.1002/sim.3095
 #'
 #'@author
 #'Cesar Merino-Soto (\email{sikayax@yahoo.cam.ar})
@@ -107,7 +107,7 @@ CID <- function(group1, group2, coef.col = "coef", lwr.col = "lwr.ci", upr.col =
     stop("Ambos argumentos 'group1' y 'group2' deben ser data.frames.")
   }
 
-  # Detección de valores perdidos
+  # Deteccion de valores perdidos
   if (!na.rm) {
     if (any(is.na(group1)) || any(is.na(group2))) {
       stop("Valores perdidos detectados. Usa na.omit() primero o establece na.rm=TRUE.")

R/CIDsingle.R

@@ -33,13 +33,13 @@
 #'
 #'Aiken, L. R. (1985). Three coefficients for analyzing the reliability and validity of ratings. Educational and Psychological Measurement, 45, 131-142. https://doi.org/10.1177/0013164485451012
 #'
-#'Merino-Soto, C. (2018) Confidence interval for difference between coefficients of content validity (Aiken's V): a SPSS syntax. Anales de  Psicología, 34(3), 587-590. https://doi.org/10.6018/analesps.34.3.283481.
+#'Merino-Soto, C. (2018) Confidence interval for difference between coefficients of content validity (Aiken's V): a SPSS syntax. Anales de  Psicologia, 34(3), 587-590. https://doi.org/10.6018/analesps.34.3.283481.
 #'
 #'Merino-Soto, C. (2023). Coeficientes V de Aiken: diferencias en los juicios de validez de contenido. MHSalud, 20(1), 23-32. https://doi.org/10.15359/mhs.20-1.3
 #'
-#'Singer, J. (2010). Construction of confidence limits about effect measures: A general approach, by G. Y. Zou and A. Donner, Statistics in Medicine 2008; 27:1693-1702.Statistics in Medicine, 29(16), 1757–1759. https://doi.org/10.1002/sim.3887
+#'Singer, J. (2010). Construction of confidence limits about effect measures: A general approach, by G. Y. Zou and A. Donner, Statistics in Medicine 2008; 27:1693-1702.Statistics in Medicine, 29(16), 1757-1759. https://doi.org/10.1002/sim.3887
 #'
-#'Zou, G.Y. and Donner, A. (2008) Construction of confidence limits about effect measures: a general approach. Stat. Med. 27, 1693–1702. https://doi.org/10.1002/sim.3095
+#'Zou, G.Y. and Donner, A. (2008) Construction of confidence limits about effect measures: a general approach. Stat. Med. 27, 1693-1702. https://doi.org/10.1002/sim.3095
 #'
 #'@seealso
 #'\code{\link[ratesci:moverci]{ratesci::moverci}} for MOVER method of ratios

R/CVC.R

@@ -68,7 +68,7 @@ CVC <- function(data, max, conf.level, na.rm = FALSE) {
     stop("El data.frame debe contener solo valores numericos.")
   }
 
-  # Detección de valores perdidos
+  # Deteccion de valores perdidos
   if (!na.rm) {
     if (any(is.na(data))) {
       stop("Valores perdidos detectados. Usa na.omit() primero o establece na.rm=TRUE.")

R/CVI.R

@@ -89,7 +89,7 @@ CVI <- function(data, cut, conf.level, na.rm = FALSE) {
     stop("Todas las columnas deben contener datos numericos.")
   }
 
-  # Detección de valores perdidos
+  # Deteccion de valores perdidos
   if (!na.rm) {
     if (any(is.na(data))) {
       stop("Valores perdidos detectados. Usa na.omit() primero o establece na.rm=TRUE.")

R/CVIpub.R

@@ -7,7 +7,7 @@
 #' When \code{correct = TRUE}, the function computes the adjusted coefficient \code{CVI.R}
 #' and applies the Wilson method to its absolute value. For computational stability,
 #' CVI values of exactly 1.0 or 0.0 are replaced by 0.9999 and 0.0001 respectively.
-#' If an extreme negative value of CVI.R is detected (e.g., from CVI ≈ 0), the value is
+#' If an extreme negative value of CVI.R is detected (e.g., from CVI ? 0), the value is
 #' truncated to 0 and the confidence interval is not estimated.
 #'
 #' @param cvi Numeric vector of CVI values (between 0 and 1).
@@ -58,7 +58,7 @@ CVIpub <- function(cvi, n, conf.level = 0.95, item.names = NULL, correct = FALSE
     if (correct) {
       A <- round(cvi.safe[i] * n[i])
 
-      # Cálculo estable usando log-factorial
+      # Calculo estable usando log-factorial
       log_pc <- lchoose(n[i], A) + n[i] * log(0.5)
       Pc <- exp(log_pc)
 
@@ -69,7 +69,7 @@ CVIpub <- function(cvi, n, conf.level = 0.95, item.names = NULL, correct = FALSE
         cvir <- 0
         lwr[i] <- upr[i] <- NA
         if (!truncation_reported) {
-          message("Extreme negative CVI.R value detected (CVI ≈ 0). Truncated to 0; confidence interval not estimated.")
+          message("Extreme negative CVI.R value detected (CVI ? 0). Truncated to 0; confidence interval not estimated.")
           truncation_reported <- TRUE
         }
       } else {

R/CVR.R

@@ -15,7 +15,7 @@
 #'Note: The function has not yet been prepared to resolve missing values, so the user must remove or impute any missing values.
 #'
 #'@references
-#'Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology, 28, 563–575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
+#'Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology, 28, 563-575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
 #'
 #'Martuza, V.R. (1977). Applying norm-referenced and criterion-referenced measurement in education. Boston: Allyn & Bacon
 #'
@@ -37,7 +37,7 @@
 #'@export
 CVR <- function(data, na.rm = FALSE) {
 
-  # Detección de valores perdidos
+  # Deteccion de valores perdidos
 if (!na.rm) {
   if (any(is.na(data))) {
     stop("Valores perdidos detectados. Usa na.omit() primero o establece na.rm=TRUE.")

R/CVRcut.R

@@ -23,13 +23,13 @@
 #' - \strong{Bag method}: Bayesian method proposed by Baghestani (1993), with prior selection.
 #'
 #' @references
-#' Lawshe, C. H. (1975). A quantitative approach to content validity. *Personnel Psychology*, 28(4), 563–575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
+#' Lawshe, C. H. (1975). A quantitative approach to content validity. *Personnel Psychology*, 28(4), 563-575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
 #'
-#' Ayre, C., & Scally, A. J. (2014). Critical Values for Lawshe’s Content Validity Ratio: Revisiting the Original Methods of Calculation. *Measurement and Evaluation in Counseling and Development*, 47(1), 79–86. https://doi.org/10.1177/0748175613513808
+#' Ayre, C., & Scally, A. J. (2014). Critical Values for Lawshe's Content Validity Ratio: Revisiting the Original Methods of Calculation. *Measurement and Evaluation in Counseling and Development*, 47(1), 79-86. https://doi.org/10.1177/0748175613513808
 #'
-#' Baghestani, A. R., Ahmadi, F., Tanha, A., & Meshkat, M. (2017). Bayesian Critical Values for Lawshe’s Content Validity Ratio. *Measurement and Evaluation in Counseling and Development*, 52(1), 69–73. https://doi.org/10.1080/07481756.2017.1308227
+#' Baghestani, A. R., Ahmadi, F., Tanha, A., & Meshkat, M. (2017). Bayesian Critical Values for Lawshe's Content Validity Ratio. *Measurement and Evaluation in Counseling and Development*, 52(1), 69-73. https://doi.org/10.1080/07481756.2017.1308227
 #'
-#' Wilson, F. R., Pan, W., & Schumsky, D. A. (2012). Recalculation of the Critical Values for Lawshe’s Content Validity Ratio. *Measurement and Evaluation in Counseling and Development*, 45(3), 197–210. https://doi.org/10.1177/0748175612440286
+#' Wilson, F. R., Pan, W., & Schumsky, D. A. (2012). Recalculation of the Critical Values for Lawshe's Content Validity Ratio. *Measurement and Evaluation in Counseling and Development*, 45(3), 197-210. https://doi.org/10.1177/0748175612440286
 #'
 #' @seealso [CVRcut.Wilson()], [CVRcut.Ayres()], [CVRcut.Bag()]
 #'

R/CVRcutAyres.R

@@ -1,5 +1,5 @@
 #' @title Critical value for CVR, based on Ayres & Scally (2014)
-#' @description Calculates critical values for Lawshe’s content validity ratio (CVR) using the method
+#' @description Calculates critical values for Lawshe's content validity ratio (CVR) using the method
 #' proposed by Ayres & Scally (2014), based on exact binomial probabilities.
 #' @param num_jueces Number of judges who provided their ratings (must be >= 2).
 #' @param alpha Significance level for the critical value calculation (default: 0.05).
@@ -22,9 +22,9 @@
 #' tests.
 #'
 #' @references
-#' Ayres, C., & Scally, A. J. (2014). Critical values for Lawshe's content validity ratio. Measurement and Evaluation in Counseling and Development, 47, 79–86. https://doi.org/10.1177/0748175613513808
+#' Ayres, C., & Scally, A. J. (2014). Critical values for Lawshe's content validity ratio. Measurement and Evaluation in Counseling and Development, 47, 79-86. https://doi.org/10.1177/0748175613513808
 #'
-#' Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology, 28, 563–575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
+#' Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology, 28, 563-575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
 #'
 #'@seealso
 #'\code{\link[ValCont:CVR]{ValCont::CVR}}

R/CVRcutWilson.R

@@ -55,7 +55,7 @@ CVRcut.Wilson <- function(num_jueces, alpha = 0.05, tails = "one") {
     z_alpha <- stats::qnorm(1 - alpha / 2)
   }
 
-  # Calcular CVR crítico y numero minimo de jueces
+  # Calcular CVR critico y numero minimo de jueces
   cutoff <- round(z_alpha / sqrt(num_jueces), 3)
   min_jueces <- ceiling(z_alpha * sqrt(num_jueces / 2) + num_jueces / 2)
 

R/CVplot.R

@@ -31,7 +31,7 @@
 #' \strong{Note}: The function has not yet been prepared to resolve missing values, so the user must remove or impute any `NA`s before plotting.
 #'
 #' @references
-#' Hink, J. K., Wogalter, M. S., & Eustace, J. K. (1996). Display of Quantitative Information: Are Grables better than Plain Graphs or Tables? *Proceedings of the Human Factors and Ergonomics Society Annual Meeting*, 40(23), 1155–1159. https://doi.org/10.1177/154193129604002302
+#' Hink, J. K., Wogalter, M. S., & Eustace, J. K. (1996). Display of Quantitative Information: Are Grables better than Plain Graphs or Tables? *Proceedings of the Human Factors and Ergonomics Society Annual Meeting*, 40(23), 1155-1159. https://doi.org/10.1177/154193129604002302
 #'
 #' Tufte, E. R. (2001). *The Visual Display of Quantitative Information*. Cheshire, CT: Graphics Press.
 #'
@@ -65,7 +65,7 @@ CVplot <- function(data, item.col, point.coeficient, lwr.ci, up.ci,
                    rotate.x.labels = FALSE,
                    na.rm = FALSE) {
 
-  # Detección de valores perdidos
+  # Deteccion de valores perdidos
   if (!na.rm) {
     if (any(is.na(data))) {
       stop("Valores perdidos detectados. Usa na.omit() primero o establece na.rm=TRUE.")

R/D2.R

@@ -47,12 +47,12 @@
 #'
 #' @references
 #' Aiken, L. R., & Aiken, T. A. (1986). Difference Tests for Distributions of Ratings.
-#' Educational and Psychological Measurement, 46(4), 871–881. https://doi.org/10.1177/001316448604600407
+#' Educational and Psychological Measurement, 46(4), 871-881. https://doi.org/10.1177/001316448604600407
 #'
 #' Rosenthal, R. (1991). Meta-analytic procedures for social research (revised). Sage: Newbury Park, CA.
 #'
 #' Bishara, A. J., & Hittner, J. B. (2017). Confidence intervals for correlations when data are not normal.
-#' Behavior Research Methods, 49(1), 294–309. https://doi.org/10.3758/s13428-016-0702-8
+#' Behavior Research Methods, 49(1), 294-309. https://doi.org/10.3758/s13428-016-0702-8
 #'
 #' @examples
 #'

R/HAiken.R

@@ -23,7 +23,7 @@
 #'
 #'Aiken, L. R. (1985). Three coefficients for analyzing the reliability and validity of ratings. Educational and Psychological Measurement, 45, 131-142. https://doi.org/10.1177/0013164485451012
 #'
-#'Penfield, R. D., & Miller, J. M. (2004). Improving Content Validation Studies Using an Asymmetric Confidence Interval for the Mean of Expert Ratings. Applied Measurement in Education, 17(4), 359–370. https://doi.org/10.1207/s15324818ame1704_2
+#'Penfield, R. D., & Miller, J. M. (2004). Improving Content Validation Studies Using an Asymmetric Confidence Interval for the Mean of Expert Ratings. Applied Measurement in Education, 17(4), 359-370. https://doi.org/10.1207/s15324818ame1704_2
 #'
 #'Wilson, E. B. (1927). Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association, 22, 209-212. https://doi.org/10.2307/2276774
 #'
@@ -60,7 +60,7 @@
 #'@export
 Haiken <- function(data, ncat, conf.level, na.rm = FALSE) {
 
-  # Detección de valores perdidos
+  # Deteccion de valores perdidos
   if (!na.rm) {
     if (any(is.na(data))) {
       stop("Valores perdidos detectados. Usa na.omit() primero o establece na.rm=TRUE.")

R/HT.R

@@ -1,4 +1,4 @@
-#' Hinkin–Tracey Content Validity Indices for Multiple Items
+#' Hinkin-Tracey Content Validity Indices for Multiple Items
 #'
 #' @description
 #' Computes Hinkin and Tracey (1999) content validity indices for multiple items
@@ -20,7 +20,7 @@
 #' and then rescaled to the \code{htc}/\code{htd} metrics.
 #'
 #' @param data A data frame or matrix in wide format, where each column
-#'   corresponds to one item–construct combination. Columns are expected
+#'   corresponds to one item-construct combination. Columns are expected
 #'   to follow the pattern \code{"item.construct"}, e.g.,
 #'   \code{"item1.c1"}, \code{"item1.c2"}, \code{"item1.c3"}.
 #' @param items Character vector with the base names of the items to be
@@ -116,29 +116,29 @@
 #'     \code{item}, \code{target}, \code{nj}, and the construct means
 #'     (\code{M.<construct>} columns).}
 #'   \item{\code{Item.criteria}}{A data frame with one row per item,
-#'     containing the global Hinkin–Tracey indices:
+#'     containing the global Hinkin-Tracey indices:
 #'     \code{item}, \code{htc}, \code{htc.lci}, \code{htc.uci},
 #'     \code{htd}, \code{htd.lci}, \code{htd.uci}. If \code{ci = FALSE},
 #'     the confidence interval columns are filled with \code{NA}.}
 #'   \item{\code{Pairwise.criteria}}{A data frame in long format, with one
-#'     row per item–orbiting construct combination, containing:
+#'     row per item-orbiting construct combination, containing:
 #'     \code{item}, \code{target}, \code{orbiting}, \code{htd},
 #'     \code{htd.lci}, \code{htd.uci}. If \code{ci = FALSE}, the confidence
 #'     interval columns are filled with \code{NA}.}
 #' }
 #'
 #' @references
 #' Hinkin, T. R., & Tracey, J. B. (1999). An analysis of variance approach
-#' to content validation. \emph{Organizational Research Methods, 2}(2), 175–186.
+#' to content validation. \emph{Organizational Research Methods, 2}(2), 175-186.
 #'
 #' Colquitt, J. A., Sabey, T. B., Rodell, J. B., & Hill, E. T. (2019).
 #' Content validation guidelines: Evaluation criteria for definitional
 #' correspondence and definitional distinctiveness. \emph{Journal of Applied
-#' Psychology, 104}(10), 1243–1265.
+#' Psychology, 104}(10), 1243-1265.
 #'
 #' Penfield, R. D., & Miller, J. M. (2004). Improving content validation
 #' studies using an asymmetric confidence interval for the mean of expert
-#' ratings. \emph{Applied Measurement in Education, 17}(4), 359–370.
+#' ratings. \emph{Applied Measurement in Education, 17}(4), 359-370.
 #'
 #' @examples
 #' \dontrun{

R/LuAgree.R

@@ -142,7 +142,7 @@
 #'   \item \code{n_subjects} Number of subjects used in the analysis.
 #'   \item \code{n_judges} Number of judges.
 #'   \item \code{categories} The ordered rating categories used.
-#'   \item \code{p_empirical} Empirical category probabilities (información
+#'   \item \code{p_empirical} Empirical category probabilities (informacion
 #'         auxiliar; no se imprime por defecto).
 #'   \item \code{call} The matched function call.
 #' }