Mixed, low-rank, and sparse multivariate regression (mixedLSR) provides tools for performing mixture regression when the coefficient matrix is low-rank and sparse. mixedLSR allows subgroup identification by alternating optimization with simulated annealing to encourage global optimum convergence. This method is data-adaptive, automatically performing parameter selection to identify low-rank substructures in the coefficient matrix.
You can install the development version of mixedLSR from GitHub with:
# install.packages("devtools")
devtools::install_github("alexanderjwhite/mixedLSR")