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.

Version:	2.1.3.1
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 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:

Reference manual:	VarSelLCM.pdf
Vignettes:	Vignette VarSelLCM

Downloads:

Package source:	VarSelLCM_2.1.3.1.tar.gz
Windows binaries:	r-devel: VarSelLCM_2.1.3.1.zip, r-release: VarSelLCM_2.1.3.1.zip, r-oldrel: VarSelLCM_2.1.3.1.zip
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, iClusterVB
Reverse suggests:	FCPS

Linking:

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