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[valse.git] / pkg / DESCRIPTION
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1Package: valse
2Title: Variable Selection With Mixture Of Models
0ba1b11c 3Date: 2020-01-11
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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).
0ba1b11c 14Author: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> [aut,cre],
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15 Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
16 Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut]
0ba1b11c 17Maintainer: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr>
3453829e 18Depends:
0ba1b11c 19 R (>= 3.5.0)
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20Imports:
21 MASS,
22 parallel
23Suggests:
24 capushe,
25 methods,
26 roxygen2,
27 testthat
28URL: http://git.auder.net/?p=valse.git
29License: MIT + file LICENSE
30RoxygenNote: 6.0.1
31Collate:
32 'plot_valse.R'
33 'main.R'
34 'selectVariables.R'
35 'constructionModelesLassoRank.R'
36 'constructionModelesLassoMLE.R'
37 'computeGridLambda.R'
38 'initSmallEM.R'
39 'EMGrank.R'
40 'EMGLLF.R'
41 'generateXY.R'
42 'A_NAMESPACE.R'
43 'util.R'