The goal of sparsemixmat is to provide a framework for matrix-variate data sparse model-based clustering. The approach assumes that the matrix mixture parameters are sparse and have different degree of sparsity across clusters, allowing to induce parsimony in a flexible manner. Estimation of the model relies on the maximization of a penalized likelihood, with specifically tailored group and graphical lasso penalties.
This repository is associated with the paper Cappozzo, Casa, Fop (2023+) Sparse model-based clustering of three-way data via lasso-type penalties. https://arxiv.org/abs/2307.10673
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("AndreaCappozzo/sparsemixmat")