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valse
View on CRAN: Click
here
Download and install valse package within the R console
Install from CRAN:
install.packages("valse")
Install from Github:
library("remotes")
install_github("cran/valse")
Install by package version:
library("remotes")
install_version("valse", "0.1-0")
Attach the package and use:
library("valse")
Maintained by
Benjamin Auder
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
First Published: 2021-05-31
Latest Update: 2021-05-31
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) <doi:10.48550/arXiv.1409.1333>, published in Advances in Data Analysis and Clustering.
How to cite:
Benjamin Auder (2021). valse: Variable Selection with Mixture of Models. R package version 0.1-0, https://cran.r-project.org/web/packages/valse. Accessed 22 Apr. 2025.
Previous versions and publish date:
0.1-0 (2021-05-31 10:00)
Other packages that cited valse R package
View valse citation profile
Other R packages that valse depends,
imports, suggests or enhances
Functions, R codes and Examples using
the valse R package
Some associated functions: EMGLLF . EMGrank . computeGridLambda . constructionModelesLassoMLE . constructionModelesLassoRank . generateXY . initSmallEM . plot_valse . runValse . selectVariables . valse-package .
Some associated R codes: A_NAMESPACE.R . EMGLLF.R . EMGrank.R . computeGridLambda.R . constructionModelesLassoMLE.R . constructionModelesLassoRank.R . generateXY.R . initSmallEM.R . main.R . plot_valse.R . selectVariables.R . util.R . Full valse package functions and examples
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