SelvarMix (1.1)

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.

Maintainer: Mohammed Sedki
Author(s): Mohammed Sedki, Gilles Celeux, Cathy Maugis-Rabusseau

License: GPL (>= 3)

Uses: glasso, Rcpp, Rmixmod

Released over 4 years ago.