Package: VarSelLCM
Type: Package
Version: 1.0
Date: 2015-03-19
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: Variable Selection for model-based clustering by using a mixture model of Gaussian distributions 
             assuming conditional independence between variables. The algorithm carries out the model 
             selection by optimizing the MICL criterion which has a closed form for such a distribution.
Maintainer: Mohammed Sedki <mohammed.sedki@u-psud.fr>
Imports: methods, Rcpp (>= 0.11.1), parallel, mclust
LinkingTo: Rcpp, RcppArmadillo
License: GPL (>= 2)
ByteCompile: true
LazyLoad: yes
Collate: 'ICLexact.R' 'RcppExports.R' 'VSLCM_Classes.R'
        'VSLCM_initialization.R' 'VarSelLCM.R' 'Print.R' 'Summary.R'
        'onAttach.R'
Packaged: 2015-03-19 13:58:52 UTC; sedki
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2015-03-19 23:47:39
