[valse.git] / pkg / DESCRIPTION

Package: valse
Title: Variable Selection With Mixture Of Models
Date: 2016-12-01
Version: 0.1-0
Description: Two methods are implemented to cluster data with finite mixture regression models.
    Those procedures deal with high-dimensional covariates and responses through a variable
    selection procedure based on the Lasso estimator. A low-rank constraint could be added,
    computed for the Lasso-Rank procedure.
    A collection of models is constructed, varying the level of sparsity and the number of
    clusters, and a model is selected using a model selection criterion (slope heuristic,
    BIC or AIC). Details of the procedure are provided in 'Model-based clustering for
    high-dimensional data. Application to functional data' by Emilie Devijver, published in
    Advances in Data Analysis and Clustering (2016).
Author: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> [aut,cre],
    Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
    Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut]
Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr>
Depends:
    R (>= 3.0.0)
Imports:
    MASS,
    methods
Suggests:
    parallel,
    testhat,
    devtools,
    rmarkdown
URL: http://git.auder.net/?p=valse.git
License: MIT + file LICENSE
VignetteBuilder: knitr
RoxygenNote: 6.0.1
Commit	Line	Data
493a35bf	1	Package: valse
5ce95f26	2	Title: Variable Selection With Mixture Of Models
493a35bf BA	3	Date: 2016-12-01
493a35bf BA	4	Version: 0.1-0
22d21a22	5	Description: Two methods are implemented to cluster data with finite mixture regression models.
5ce95f26 BA	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).
ef67d338	14	Author: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> [aut,cre],
5ce95f26	15	Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
ef67d338	16	Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut]
ef67d338	17	Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr>
493a35bf	18	Depends:
ef67d338 BA	19	R (>= 3.0.0)
ef67d338 BA	20	Imports:
e3f2fe8a	21	MASS,
e3f2fe8a	22	methods
ef67d338 BA	23	Suggests:
ef67d338 BA	24	parallel,
5ce95f26 BA	25	testhat,
	26	devtools,
	27	rmarkdown
493a35bf	28	URL: http://git.auder.net/?p=valse.git
ef67d338 BA	29	License: MIT + file LICENSE
ef67d338 BA	30	VignetteBuilder: knitr
3f3ed99c	31	RoxygenNote: 6.0.1