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SelvarMix (version 1.2.1)

Regularization for Variable Selection in Model-Based Clustering and Discriminant Analysis

Description

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.

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Version

Install

install.packages('SelvarMix')

Monthly Downloads

50

Version

1.2.1

License

GPL (>= 3)

Maintainer

Last Published

October 16th, 2017

Functions in SelvarMix (1.2.1)

wine

Wine data set
SelvarClustLasso

Regularization for variable selection in model-based clustering
SelvarMix-package

Regularization for variable selection in model-based clustering and discriminant analysis
SortvarClust

Variable ranking with LASSO in model-based clustering
SortvarLearn

Variable ranking with LASSO in discriminant analysis
scenarioCor

Simulated quantitative data according SRUW modeling
SelvarLearnLasso

Regularization for variable selection in discriminant analysis