VarSelLCM: Variable Selection for Model-Based Clustering using the Integrated Complete-Data Likelihood of a Latent Class Model

Uses a finite mixture model for performing the cluster analysis with variable selection of continuous data by assuming independence between classes. The package deals dataset with missing values by assuming that values are missing at random. The one-dimensional marginals of the components follow Gaussian distributions for facilitating both model interpretation and model selection. The variable selection is led by the Maximum Integrated Complete-Data Likelihood criterion. The maximum likelihood inference is done by an EM algorithm for the selected model. This package also performs the imputation of missing values.

Version: 1.2
Imports: methods, Rcpp (≥ 0.11.1), parallel
LinkingTo: Rcpp, RcppArmadillo
Published: 2015-06-10
Author: Matthieu Marbac and Mohammed Sedki
Maintainer: Mohammed Sedki <mohammed.sedki at u-psud.fr>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: VarSelLCM results

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Reference manual: VarSelLCM.pdf
Package source: VarSelLCM_1.2.tar.gz
Windows binaries: r-devel: VarSelLCM_1.2.zip, r-release: VarSelLCM_1.2.zip, r-oldrel: VarSelLCM_1.2.zip
OS X El Capitan binaries: r-release: VarSelLCM_1.2.tgz
OS X Mavericks binaries: r-oldrel: VarSelLCM_1.2.tgz
Old sources: VarSelLCM archive

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