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) <arXiv:1409.1333v2>, 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

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

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