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) <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.

Package details

AuthorMohammed Sedki, Gilles Celeux, Cathy Maugis-Rabusseau
MaintainerMohammed Sedki <[email protected]>
LicenseGPL (>= 3)
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the SelvarMix package in your browser

Any scripts or data that you put into this service are public.

SelvarMix documentation built on May 2, 2019, 3:27 a.m.