SelvarMix: Regularization for Variable Selection in Model-Based Clustering
and Discriminant Analysis
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)
is adapted to define the role of variables in the two frameworks.
| Version: |
1.1 |
| Imports: |
Rcpp (≥ 0.11.1), glasso, parallel, Rmixmod, methods |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Published: |
2015-09-20 |
| Author: |
Mohammed Sedki, Gilles Celeux, Cathy Maugis-Rabusseau |
| Maintainer: |
Mohammed Sedki <mohammed.sedki at u-psud.fr> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
yes |
| Materials: |
README |
| CRAN checks: |
SelvarMix results |
Downloads: