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>
License: MIT + file LICENSE
NeedsCompilation: yes
CRAN checks: valse results


Reference manual: valse.pdf


Package source: valse_0.1-0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


Please use the canonical form to link to this page.