SelvarMix: Regularization for Variable Selection in Model-Based Clustering and Discriminant Analysis
Version 1.2.1

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.

Package details

AuthorMohammed Sedki, Gilles Celeux, Cathy Maugis-Rabusseau
Date of publication2017-10-16 16:18:03 UTC
MaintainerMohammed Sedki <[email protected]>
LicenseGPL (>= 3)
Package repositoryView on CRAN
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SelvarMix documentation built on Nov. 17, 2017, 5:28 a.m.