Installation
About
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)
git.auder.net/?p=valse.git |
Key Metrics
Downloads
Yesterday | 6 0% |
Last 7 days | 38 -7% |
Last 30 days | 128 -2% |
Last 90 days | 317 -43% |
Last 365 days | 1.617 -13% |