[valse.git] / pkg / DESCRIPTION

Package: valse
Title: Variable Selection with Mixture of Models
Date: 2021-05-16
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 (2016) <arXiv:1409.1333v2>,
    published in Advances in Data Analysis and Clustering.
Author: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> [aut,cre],
    Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
    Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [ctb]
Maintainer: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr>
Depends:
    R (>= 3.5.0)
Imports:
    MASS,
    parallel,
    cowplot,
    ggplot2,
    reshape2
Suggests:
    capushe,
    roxygen2
URL: https://git.auder.net/?p=valse.git
License: MIT + file LICENSE
RoxygenNote: 7.1.1
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'
    'util.R'
Commit	Line	Data
	1	Package: valse
	2	Title: Variable Selection with Mixture of Models
	3	Date: 2021-05-16
	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
	12	"Model-based clustering for high-dimensional data. Application to functional data"
	13	by Emilie Devijver (2016) <arXiv:1409.1333v2>,
	14	published in Advances in Data Analysis and Clustering.
	15	Author: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> [aut,cre],
	16	Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
	17	Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [ctb]
	18	Maintainer: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr>
	19	Depends:
	20	R (>= 3.5.0)
	21	Imports:
	22	MASS,
	23	parallel,
	24	cowplot,
	25	ggplot2,
	26	reshape2
	27	Suggests:
	28	capushe,
	29	roxygen2
	30	URL: https://git.auder.net/?p=valse.git
	31	License: MIT + file LICENSE
	32	RoxygenNote: 7.1.1
	33	Collate:
	34	'plot_valse.R'
	35	'main.R'
	36	'selectVariables.R'
	37	'constructionModelesLassoRank.R'
	38	'constructionModelesLassoMLE.R'
	39	'computeGridLambda.R'
	40	'initSmallEM.R'
	41	'EMGrank.R'
	42	'EMGLLF.R'
	43	'generateXY.R'
	44	'A_NAMESPACE.R'
	45	'util.R'