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valse  

Variable Selection with Mixture of Models
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]
All associated links for this package
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 21 Dec. 2024.
Previous versions and publish date:
0.1-0 (2021-05-31 10:00)
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