add a few TODOs in constructionModelesEMGLLF.R
[valse.git] / DESCRIPTION
1 Package: valse
2 Title: VAriabLe SElection with mixture of models
3 Date: 2016-12-01
4 Version: 0.1-0
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 selection
7 procedure based on the Lasso estimator. A low-rank constraint could be added, computed for the Lasso-Rank procedure.
8 A collection of models is constructed, varying the level of sparsity and the number of clusters, and a model is selected
9 using a model selection criterion (slope heuristic, BIC or AIC).
10 Details of the procedure are provided in 'Model-based clustering for high-dimensional data. Application to functional data'
11 by Emilie Devijver, published in Advances in Data Analysis and Clustering (2016)
12 Author: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> [aut,cre],
13 Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut]
14 Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut]
15 Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr>
16 Depends:
17 R (>= 3.0.0)
18 Imports:
19 MASS
20 Suggests:
21 parallel,
22 testthat,
23 knitr
24 URL: http://git.auder.net/?p=valse.git
25 License: MIT + file LICENSE
26 VignetteBuilder: knitr
27 RoxygenNote: 5.0.1