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.


Reference manual

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1.2.1 by Mohammed Sedki, a year ago

Browse source code at

Authors: Mohammed Sedki , Gilles Celeux , Cathy Maugis-Rabusseau

Documentation:   PDF Manual  

GPL (>= 3) license

Imports Rcpp, methods

Depends on glasso, Rmixmod, parallel, base

Linking to Rcpp, RcppArmadillo

See at CRAN