docs: fix link and removed ClusVis

Author: Quentin62

RMixtComp/DESCRIPTION

@@ -1,8 +1,8 @@
 Package: RMixtComp
 Type: Package
 Title: Mixture Models with Heterogeneous and (Partially) Missing Data
-Version: 4.1.4
-Date: 2023-06-17
+Version: 4.1.5
+Date: 2025-06-15
 Authors@R: c(person("Vincent", "Kubicki", role = "aut"),
              person("Christophe", "Biernacki", role = "aut"),
              person("Quentin", "Grimonprez", role = c("aut", "cre"), email = "quentingrim@yahoo.fr"),
@@ -19,7 +19,7 @@ URL: https://github.com/modal-inria/MixtComp
 BugReports: https://github.com/modal-inria/MixtComp/issues
 Imports: RMixtCompIO(>= 4.0.4), ggplot2, plotly, scales
 Depends: RMixtCompUtilities (>= 4.1.4), R (>= 3.5.0)
-Suggests: testthat, xml2, Rmixmod, knitr, ClusVis, rmarkdown
-RoxygenNote: 7.2.3
+Suggests: testthat, xml2, Rmixmod, knitr, rmarkdown
+RoxygenNote: 7.3.2
 Encoding: UTF-8
 VignetteBuilder: knitr

RMixtComp/R/MIXTCOMP_datasets.R

@@ -89,14 +89,14 @@ NULL
 #'
 #' # create model
 #' model <- list(
-#'     pclass = "Multinomial",
-#'     survived = "Multinomial",
-#'     sex = "Multinomial",
-#'     age = "Gaussian",
-#'     sibsp = "Poisson",
-#'     parch = "Poisson",
-#'     fare = "Gaussian",
-#'     embarked = "Multinomial"
+#'   pclass = "Multinomial",
+#'   survived = "Multinomial",
+#'   sex = "Multinomial",
+#'   age = "Gaussian",
+#'   sibsp = "Poisson",
+#'   parch = "Poisson",
+#'   fare = "Gaussian",
+#'   embarked = "Multinomial"
 #' )
 #'
 #' # create algo
@@ -154,20 +154,20 @@ NULL
 #'
 #' # convert functional to MixtComp format
 #' dat <- list(
-#'     tempav = apply(
-#'         CanadianWeather$tempav, 2,
-#'         function(x) createFunctional(CanadianWeather$time, x)
-#'     ),
-#'     precav = apply(
-#'         CanadianWeather$precav, 2,
-#'         function(x) createFunctional(CanadianWeather$time, x)
-#'     )
+#'   tempav = apply(
+#'     CanadianWeather$tempav, 2,
+#'     function(x) createFunctional(CanadianWeather$time, x)
+#'   ),
+#'   precav = apply(
+#'     CanadianWeather$precav, 2,
+#'     function(x) createFunctional(CanadianWeather$time, x)
+#'   )
 #' )
 #'
 #' # create model with 4 subregressions ans 2 coefficients per regression
 #' model <- list(
-#'     tempav = list(type = "Func_CS", paramStr = "nSub: 4, nCoeff: 2"),
-#'     precav = list(type = "Func_CS", paramStr = "nSub: 4, nCoeff: 2")
+#'   tempav = list(type = "Func_CS", paramStr = "nSub: 4, nCoeff: 2"),
+#'   precav = list(type = "Func_CS", paramStr = "nSub: 4, nCoeff: 2")
 #' )
 #'
 #' # create algo
@@ -229,8 +229,8 @@ NULL
 #'
 #' # run clustering
 #' resLearn <- mixtCompLearn(prostate$data, prostate$model, algo,
-#'     nClass = 2:5, criterion = "ICL",
-#'     nRun = 3, nCore = 1
+#'   nClass = 2:5, criterion = "ICL",
+#'   nRun = 3, nCore = 1
 #' )
 #'
 #' summary(resLearn)

RMixtComp/R/MIXTCOMP_methods.R

@@ -104,8 +104,8 @@ print.MixtCompLearn <- function(x, nVarMaxToPrint = 5, nClass = NULL, ...) {
 #' @param nVarMaxToPlot number of variables to display
 #' @param nClass number of classes of the model to plot
 #' @param pkg "ggplot2" or "plotly". Package used to plot
-#' @param plotData "CI" or "Boxplot". If "CI", uses \link{plotDataCI} function. If "Boxplot", uses \link{plotDataBoxplot}
-#' @param ... extra parameter for \link{plotDataCI} or \link{plotDataBoxplot}
+#' @param plotData "CI" or "Boxplot". If "CI", uses \link[RMixtCompUtilities]{plotDataCI} function. If "Boxplot", uses \link[RMixtCompUtilities]{plotDataBoxplot}
+#' @param ... extra parameter for \link[RMixtCompUtilities]{plotDataCI} or \link[RMixtCompUtilities]{plotDataBoxplot}
 #'
 #' @return \code{ggplot2} or \code{plotly} object
 #'

RMixtComp/R/MIXTCOMP_mixtCompLearn.R

@@ -21,7 +21,7 @@
 #'
 #' @param data a data.frame, a matrix or a named list containing the data (see \emph{Details} and \emph{Data format} sections).
 #' @param model a named list containing models and hyperparameters (see \emph{Details} section).
-#' @param algo a list containing the parameters of the SEM-Gibbs algorithm (see \emph{Details} or \link{createAlgo}).
+#' @param algo a list containing the parameters of the SEM-Gibbs algorithm (see \emph{Details} or \link[RMixtCompUtilities]{createAlgo}).
 #' @param nClass the number of classes of the mixture model. Can be a vector for \emph{mixtCompLearn} only.
 #' @param criterion "BIC" or "ICL". Criterion used for choosing the best model.
 #' @param hierarchicalMode "auto", "yes" or "no". If "auto", it performs a hierarchical version of MixtComp
@@ -56,7 +56,7 @@
 #' Models will be imputed as follows: "Gaussian" for numeric variable, "Multinomial" for character or factor variable
 #' and "Poisson" for integer variable.
 #' A summary of available models (and associated hyperparameters and missing format) can be accessed by calling
-#' the \link{availableModels} function.
+#' the \link[RMixtCompUtilities]{availableModels} function.
 #'
 #' Eight models are available in RMixtComp: \emph{Gaussian}, \emph{Multinomial}, \emph{Poisson}, \emph{NegativeBinomial},
 #' \emph{Weibull}, \emph{Func_CS}, \emph{Func_SharedAlpha_CS}, \emph{Rank_ISR}.
@@ -72,7 +72,7 @@
 #'
 #'
 #' The \emph{algo} object is a list containing the different number of iterations for the algorithm.
-#' This list can be generated using the \link{createAlgo} function.
+#' This list can be generated using the \link[RMixtCompUtilities]{createAlgo} function.
 #' The algorithm is decomposed in a burn-in phase and a normal phase.
 #' Estimates from the burn-in phase are not shown in output.
 #' \itemize{
@@ -149,7 +149,7 @@
 #' -\eqn{\sum_{i=1}^n t_{ikj} log(t_{ikj})/(n * log(K))}}
 #'   \item{IDClassBar: entropy used to compute the discriminative power of variable:
 #' -\eqn{\sum_{i=1}^n (1-t_{ikj}) log((1-t_{ikj}))/(n * log(K))}}
-#'   \item{delta: similarities between variables (see \link{heatmapVar})}
+#'   \item{delta: similarities between variables (see \link[RMixtCompUtilities]{heatmapVar})}
 #'   \item{completedProbabilityLogBurnIn: evolution of the completed log-probability during the burn-in period
 #' (can be used to check the convergence and determine the ideal number of iteration)}
 #'   \item{completedProbabilityLogRun: evolution of the completed log-probability  after the burn-in period

RMixtComp/R/RMixtComp-package.R

@@ -43,27 +43,27 @@
 #' @details
 #' Main functions are \link{mixtCompLearn} for clustering, \link{mixtCompPredict} for predicting the cluster of new samples
 #' with a model learnt with \link{mixtCompLearn}.
-#' \link{createAlgo} gives you default values for required parameters.
+#' \link[RMixtCompUtilities]{createAlgo} gives you default values for required parameters.
 #'
 #' Read the help page of \link{mixtCompLearn} for available models and data format. A summary of these information can be
-#' accessed with the function \link{availableModels}.
+#' accessed with the function \link[RMixtCompUtilities]{availableModels}.
 #'
-#' All utility functions (getters, graphical) are in the \code{\link{RMixtCompUtilities-package}} package.
+#' All utility functions (getters, graphical) are in the \code{\link[RMixtCompUtilities]{RMixtCompUtilities-package}} package.
 #'
 #' In order to have an overview of the output, you can use \link{print.MixtCompLearn}, \link{summary.MixtCompLearn} and
 #' \link{plot.MixtCompLearn} functions,
 #'
-#' Getters are available to easily access some results (see. \link{mixtCompLearn} for output format): \link{getBIC},
-#' \link{getICL}, \link{getCompletedData}, \link{getParam}, \link{getProportion}, \link{getTik}, \link{getEmpiricTik},
-#' \link{getPartition}, \link{getType}, \link{getModel}, \link{getVarNames}.
+#' Getters are available to easily access some results (see. \link{mixtCompLearn} for output format): \link[RMixtCompUtilities]{getBIC},
+#' \link[RMixtCompUtilities]{getICL}, \link[RMixtCompUtilities]{getCompletedData}, \link[RMixtCompUtilities]{getParam}, \link[RMixtCompUtilities]{getProportion}, \link[RMixtCompUtilities]{getTik}, \link[RMixtCompUtilities]{getEmpiricTik},
+#' \link[RMixtCompUtilities]{getPartition}, \link[RMixtCompUtilities]{getType}, \link[RMixtCompUtilities]{getModel}, \link[RMixtCompUtilities]{getVarNames}.
 #'
 #'
-#' You can compute discriminative powers and similarities with functions: \link{computeDiscrimPowerClass},
-#' \link{computeDiscrimPowerVar}, \link{computeSimilarityClass}, \link{computeSimilarityVar}.
+#' You can compute discriminative powers and similarities with functions: \link[RMixtCompUtilities]{computeDiscrimPowerClass},
+#' \link[RMixtCompUtilities]{computeDiscrimPowerVar}, \link[RMixtCompUtilities]{computeSimilarityClass}, \link[RMixtCompUtilities]{computeSimilarityVar}.
 #'
-#' Graphics functions are \link{plot.MixtComp}, \link{plot.MixtCompLearn}, \link{heatmapClass}, \link{heatmapTikSorted},
-#' \link{heatmapVar}, \link{histMisclassif}, \link{plotConvergence}, \link{plotDataBoxplot}, \link{plotDataCI},
-#' \link{plotDiscrimClass}, \link{plotDiscrimVar}, \link{plotProportion}, \link{plotCrit}.
+#' Graphics functions are \link[RMixtCompUtilities]{plot.MixtComp}, \link{plot.MixtCompLearn}, \link[RMixtCompUtilities]{heatmapClass}, \link[RMixtCompUtilities]{heatmapTikSorted},
+#' \link[RMixtCompUtilities]{heatmapVar}, \link[RMixtCompUtilities]{histMisclassif}, \link[RMixtCompUtilities]{plotConvergence}, \link[RMixtCompUtilities]{plotDataBoxplot}, \link[RMixtCompUtilities]{plotDataCI},
+#' \link[RMixtCompUtilities]{plotDiscrimClass}, \link[RMixtCompUtilities]{plotDiscrimVar}, \link[RMixtCompUtilities]{plotProportion}, \link{plotCrit}.
 #'
 #' Datasets with running examples are provided: \link{titanic}, \link{CanadianWeather}, \link{prostate}, \link{simData}.
 #'
@@ -73,35 +73,36 @@
 #'
 #' MixtComp examples: \code{vignette("MixtComp")} or online \url{https://github.com/vandaele/mixtcomp-notebook}.
 #'
-#' Using ClusVis with RMixtComp: \code{vignette("dataFormat")}.
+#' Using ClusVis with RMixtComp: \code{vignette("ClusVis")}.
 #'
 #'
 #' @examples
 #' data(simData)
 #'
 #' # define the algorithm's parameters: you can use createAlgo function
 #' algo <- list(
-#'     nbBurnInIter = 50,
-#'     nbIter = 50,
-#'     nbGibbsBurnInIter = 50,
-#'     nbGibbsIter = 50,
-#'     nInitPerClass = 20,
-#'     nSemTry = 20,
-#'     confidenceLevel = 0.95
+#'   nbBurnInIter = 50,
+#'   nbIter = 50,
+#'   nbGibbsBurnInIter = 50,
+#'   nbGibbsIter = 50,
+#'   nInitPerClass = 20,
+#'   nSemTry = 20,
+#'   confidenceLevel = 0.95
 #' )
 #'
 #' # run RMixtComp for learning using only 3 variables
 #' resLearn <- mixtCompLearn(simData$dataLearn$matrix, simData$model$unsupervised[1:3], algo,
-#'     nClass = 1:2, nRun = 2, nCore = 1
+#'   nClass = 1:2, nRun = 2, nCore = 1
 #' )
 #'
 #' summary(resLearn)
 #' plot(resLearn)
 #'
 #' # run RMixtComp for predicting
 #' resPred <- mixtCompPredict(
-#'     simData$dataPredict$matrix, simData$model$unsupervised[1:3], algo,
-#'     resLearn, nCore = 1
+#'   simData$dataPredict$matrix, simData$model$unsupervised[1:3], algo,
+#'   resLearn,
+#'   nCore = 1
 #' )
 #'
 #' partitionPred <- getPartition(resPred)
@@ -118,8 +119,8 @@
 #'
 #' J. Jacques, C. Biernacki. (2014). Model-based clustering for multivariate partial ranking data. Journal of Statistical Planning and Inference. 149. 10.1016/j.jspi.2014.02.011.
 #'
-#' @seealso \code{\link{mixtCompLearn}} \code{\link{availableModels}} \code{\link{RMixtCompUtilities-package}},
-#' \code{\link{RMixtCompIO-package}}. Other clustering packages: \code{Rmixmod}
+#' @seealso \code{\link{mixtCompLearn}} \code{\link[RMixtCompUtilities]{availableModels}} \code{\link[RMixtCompUtilities]{RMixtCompUtilities-package}},
+#' \code{\link[RMixtCompIO]{RMixtCompIO-package}}. Other clustering packages: \code{Rmixmod}
 #'
 #' @keywords package
-NULL
+"_PACKAGE"