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1Package: valse
2Title: Variable Selection With Mixture Of Models
3Date: 2016-12-01
4Version: 0.1-0
5Description: 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).
14Author: 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]
17Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr>
18Depends:
19 R (>= 3.0.0)
20Imports:
21 MASS,
22 parallel
23Suggests:
24 capushe,
25 roxygen2,
26 testhat
27URL: http://git.auder.net/?p=valse.git
28License: MIT + file LICENSE
29RoxygenNote: 5.0.1
30Collate:
31 'plot.R'
32 'main.R'
33 'selectVariables.R'
34 'constructionModelesLassoRank.R'
35 'constructionModelesLassoMLE.R'
36 'computeGridLambda.R'
37 'initSmallEM.R'
38 'EMGrank.R'
39 'EMGLLF.R'
40 'EMGrank_R.R'
41 'EMGLLF_R.R'
42 'generateXY.R'
43 'A_NAMESPACE.R'