VarSelLCM: Variable Selection for Model-Based Clustering of Mixed-Type Data Set 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.

Depends: R (≥ 3.3)
Imports: methods, Rcpp (≥ 0.11.1), parallel, mgcv, ggplot2, shiny
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, dplyr, htmltools, scales, plyr
Published: 2020-10-14
DOI: 10.32614/CRAN.package.VarSelLCM
Author: Matthieu Marbac and Mohammed Sedki
Maintainer: Mohammed Sedki <mohammed.sedki at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: VarSelLCM citation info
Materials: NEWS
In views: Cluster, MissingData
CRAN checks: VarSelLCM results


Reference manual: VarSelLCM.pdf
Vignettes: Vignette VarSelLCM


Package source: VarSelLCM_2.1.3.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): VarSelLCM_2.1.3.1.tgz, r-oldrel (arm64): VarSelLCM_2.1.3.1.tgz, r-release (x86_64): VarSelLCM_2.1.3.1.tgz, r-oldrel (x86_64): VarSelLCM_2.1.3.1.tgz
Old sources: VarSelLCM archive

Reverse dependencies:

Reverse imports: ClusVis
Reverse suggests: FCPS


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