2 Title: Variable Selection with Mixture of Models
5 Description: Two methods are implemented to cluster data with finite mixture
6 regression models. Those procedures deal with high-dimensional covariates and
7 responses through a variable selection procedure based on the Lasso estimator.
8 A low-rank constraint could be added, computed for the Lasso-Rank procedure.
9 A collection of models is constructed, varying the level of sparsity and the
10 number of clusters, and a model is selected using a model selection criterion
11 (slope heuristic, BIC or AIC). Details of the procedure are provided in
12 "Model-based clustering for high-dimensional data. Application to functional data"
13 by Emilie Devijver (2016) <arXiv:1409.1333v2>,
14 published in Advances in Data Analysis and Clustering.
15 Author: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> [aut,cre],
16 Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
17 Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [ctb]
18 Maintainer: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr>
30 URL: https://git.auder.net/?p=valse.git
31 License: MIT + file LICENSE
37 'constructionModelesLassoRank.R'
38 'constructionModelesLassoMLE.R'