VarSelLCM: Variable Selection for Model-Based Clustering of Continuous, Count, Categorical or Mixed-Type Data Set with Missing Values

Variable Selection for model-based clustering managed by the Latent Class Model. This model analyses mixed-type data (data with continuous and/ or count and/or categorical variables) with missing values (missing at random) by assuming independence between classes. The one-dimensional marginals of the components follow standard distributions for facilitating both the model interpretation and the model selection. The variable selection is led by an alternated optimization procedure for maximizing 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 by taking the expectation of the missing values conditionally on the model, its parameters and on the observed variables.

Version: 2.0.1
Imports: methods, Rcpp (≥ 0.11.1), parallel, mgcv
LinkingTo: Rcpp, RcppArmadillo
Published: 2017-10-16
Author: Matthieu Marbac and Mohammed Sedki
Maintainer: Mohammed Sedki <mohammed.sedki at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: VarSelLCM results


Reference manual: VarSelLCM.pdf
Package source: VarSelLCM_2.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: VarSelLCM_2.0.1.tgz
OS X Mavericks binaries: r-oldrel: VarSelLCM_2.0.1.tgz
Old sources: VarSelLCM archive


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