Package: VarSelLCM
Type: Package
Title: Variable Selection for Model-Based Clustering of Continuous,
        Count, Categorical or Mixed-Type Data Set with Missing Values
Version: 2.0.1
Date: 2017-10-16
Author: Matthieu Marbac and Mohammed Sedki
Maintainer: Mohammed Sedki <mohammed.sedki@u-psud.fr>
Description: 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.
License: GPL (>= 2)
Imports: methods, Rcpp (>= 0.11.1), parallel, mgcv
URL: http://varsellcm.r-forge.r-project.org/
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' 'Plot.R' 'Imputation.R' 'withoutmixture.R'
Packaged: 2017-10-16 09:57:10 UTC; sedki
RoxygenNote: 6.0.1
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2017-10-16 10:16:53 UTC
Built: R 3.3.2; x86_64-apple-darwin13.4.0; 2017-10-17 16:33:10 UTC; unix
Archs: VarSelLCM.so.dSYM
