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.
Version: 0.1.0 Depends: R (≥ 4.1.0) Imports: grpreg, purrr, MASS, stats, ggplot2 Suggests: knitr, rmarkdown, mclust Published: 2022-11-04 DOI: 10.32614/CRAN.package.mixedLSR Author: Alexander White [aut, cre], Sha Cao [aut], Yi Zhao [ctb], Chi Zhang [ctb] Maintainer: Alexander White <whitealj at iu.edu> BugReports: https://github.com/alexanderjwhite/mixedLSR License: MIT + file LICENSE URL: https://alexanderjwhite.github.io/mixedLSR/ NeedsCompilation: no Materials: README NEWS CRAN checks: mixedLSR results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=mixedLSR to link to this page.
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