Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values R package VarSelLCM version 2.1.3.2 CRAN Task View: Cluster (https://CRAN.R-project.org/view=Cluster) CRAN Task View: MissingData (https://CRAN.R-project.org/view=MissingData) Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here <doi:10.1007/s11222-016-9670-1>). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results. Software Depends: R (>= 3.3) Imports: methods, Rcpp (>= 0.11.1), parallel, mgcv, ggplot2, shiny LinkingTo: Rcpp, RcppArmadillo (>= 15.0.2-1) Suggests: knitr, rmarkdown, dplyr, htmltools, scales, plyr Mohammed Sedki <mohammed.sedki@u-psud.fr> Comprehensive R Archive Network (CRAN) Matthieu Marbac [aut], Mohammed Sedki [aut, cre] GPL (>= 2) 2025-09-19 application/tgz https://CRAN.R-project.org/package=VarSelLCM doi:10.32614/CRAN.package.VarSelLCM