Package: SelvarMix
Type: Package
Title: Regularization for Variable Selection in Model-Based Clustering
        and Discriminant Analysis
Version: 1.2.1
Date: 2017-10-16
Author: Mohammed Sedki, Gilles Celeux, Cathy Maugis-Rabusseau
Maintainer: Mohammed Sedki <mohammed.sedki@u-psud.fr>
Description: Performs a regularization approach to variable selection in the
             model-based clustering and classification frameworks.
             First, the variables are arranged in order with a lasso-like procedure. 
             Second, the method of Maugis, Celeux, and Martin-Magniette (2009, 2011)
             <doi:10.1016/j.csda.2009.04.013>, <doi:10.1016/j.jmva.2011.05.004> 
             is adapted to define the role of variables in the two frameworks. 
License: GPL (>= 3)
Depends: R (>= 3.1.0), glasso, Rmixmod, parallel, base
Imports: Rcpp (>= 0.11.1), methods
LinkingTo: Rcpp, RcppArmadillo
NeedsCompilation: yes
Packaged: 2017-10-16 16:00:57 UTC; sedki
Repository: CRAN
Date/Publication: 2017-10-16 16:18:03 UTC
Built: R 4.1.0; x86_64-w64-mingw32; 2020-08-03 09:36:56 UTC; windows
Archs: i386, x64
