Package: VarSelLCM
Type: Package
Version: 1.2
Date: 2015-06-08
Author: Matthieu Marbac and Mohammed Sedki
Title: Variable Selection for Model-Based Clustering using the
        Integrated Complete-Data Likelihood of a Latent Class Model
Description: 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.
Maintainer: Mohammed Sedki <mohammed.sedki@u-psud.fr>
License: GPL (>= 2)
Imports: methods, Rcpp (>= 0.11.1), parallel
LinkingTo: Rcpp, RcppArmadillo
ByteCompile: true
LazyLoad: yes
Collate: 'CheckInputs.R' 'DataCstr.R' 'VSLCMGrlClasses.R' 'ICLexact.R'
        'DesignOutput.R' 'Summary.R' 'Print.R' 'VarSelLCM.R'
        'RcppExports.R' 'Imputation.R' 'withoutmixture.R'
Packaged: 2015-06-10 13:11:39 UTC; sedki
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2015-06-10 19:00:07
