VarSelLCM: Variable Selection for Model-Based Clustering of Mixed-Type DataSet with Missing Values
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.
| Version: | 2.1.3.2 |
| 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 |
| Published: | 2025-09-19 |
| DOI: | 10.32614/CRAN.package.VarSelLCM |
| Author: | Matthieu Marbac [aut], Mohammed Sedki [aut, cre] |
| Maintainer: | Mohammed Sedki <mohammed.sedki at u-psud.fr> |
| License: | GPL-2 |GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://varsellcm.r-forge.r-project.org/ |
| NeedsCompilation: | yes |
| Citation: | VarSelLCM citation info |
| Materials: | NEWS |
| In views: | Cluster,MissingData |
| CRAN checks: | VarSelLCM results |
Documentation:
Downloads:
Reverse dependencies:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=VarSelLCMto link to this page.