Update README and package DESCRIPTION
[valse.git] / pkg / DESCRIPTION
1 Package: valse
2 Title: Variable Selection with Mixture of Models
3 Date: 2020-03-11
4 Version: 0.1-0
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 'Model-
12 based clustering for high-dimensional data. Application to functional data' by
13 Emilie Devijver, published in Advances in Data Analysis and Clustering (2016).
14 Author: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> [aut,cre],
15 Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
16 Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [ctb]
17 Maintainer: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr>
18 Depends:
19 R (>= 3.5.0)
20 Imports:
21 MASS,
22 parallel,
23 ggplot2,
24 cowplot,
25 reshape2
26 Suggests:
27 capushe,
28 roxygen2
29 URL: http://git.auder.net/?p=valse.git
30 License: MIT + file LICENSE
31 RoxygenNote: 7.0.2
32 Collate:
33 'plot_valse.R'
34 'main.R'
35 'selectVariables.R'
36 'constructionModelesLassoRank.R'
37 'constructionModelesLassoMLE.R'
38 'computeGridLambda.R'
39 'initSmallEM.R'
40 'EMGrank.R'
41 'EMGLLF.R'
42 'generateXY.R'
43 'A_NAMESPACE.R'
44 'util.R'