[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,
    parallel
Suggests:
    capushe,
    roxygen2,
    testhat
URL: http://git.auder.net/?p=valse.git
License: MIT + file LICENSE
RoxygenNote: 5.0.1
Collate:
    'plot_valse.R'
    'main.R'
    'selectVariables.R'
    'constructionModelesLassoRank.R'
    'constructionModelesLassoMLE.R'
    'computeGridLambda.R'
    'initSmallEM.R'
    'EMGrank.R'
    'EMGLLF.R'
    'generateXY.R'
    'A_NAMESPACE.R'
Commit	Line	Data
	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
	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@math.u-psud.fr> [aut,cre],
	15	Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
	16	Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut]
	17	Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr>
	18	Depends:
	19	R (>= 3.0.0)
	20	Imports:
	21	MASS,
	22	parallel
	23	Suggests:
	24	capushe,
	25	roxygen2,
	26	testhat
	27	URL: http://git.auder.net/?p=valse.git
	28	License: MIT + file LICENSE
	29	RoxygenNote: 5.0.1
	30	Collate:
	31	'plot_valse.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	'generateXY.R'
	41	'A_NAMESPACE.R'