valse: Variable Selection with Mixture of Models

Two methods are implemented to cluster data with finite mixture regression models. Those procedures deal with high-dimensional covariates and responses through a variable selection procedure based on the Lasso estimator. A low-rank constraint could be added, computed for the Lasso-Rank procedure. A collection of models is constructed, varying the level of sparsity and the number of clusters, and a model is selected using a model selection criterion (slope heuristic, BIC or AIC). Details of the procedure are provided in "Model-based clustering for high-dimensional data. Application to functional data" by Emilie Devijver (2016) <doi:10.48550/arXiv.1409.1333>, published in Advances in Data Analysis and Clustering.

Version:	0.1-0
Depends:	R (≥ 3.5.0)
Imports:	MASS, parallel, cowplot, ggplot2, reshape2
Suggests:	capushe, roxygen2
Published:	2021-05-31
Author:	Benjamin Auder [aut,cre], Emilie Devijver [aut], Benjamin Goehry [ctb]
Maintainer:	Benjamin Auder <benjamin.auder at universite-paris-saclay.fr>
License:	MIT + file LICENSE
URL:	https://git.auder.net/?p=valse.git
NeedsCompilation:	yes
CRAN checks:	valse results

Documentation:

Reference manual:

valse.pdf

Downloads:

Package source:	valse_0.1-0.tar.gz
Windows binaries:	r-devel: valse_0.1-0.zip, r-release: valse_0.1-0.zip, r-oldrel: valse_0.1-0.zip
macOS binaries:	r-release (arm64): valse_0.1-0.tgz, r-oldrel (arm64): valse_0.1-0.tgz, r-release (x86_64): valse_0.1-0.tgz, r-oldrel (x86_64): valse_0.1-0.tgz

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