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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:

Please use the canonical form https://CRAN.R-project.org/package=VarSelLCM to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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