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