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 |
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 |
Reference manual: | mixedLSR.pdf |
Vignettes: |
Introduction to mixedLSR |
Package source: | mixedLSR_0.1.0.tar.gz |
Windows binaries: | r-devel: mixedLSR_0.1.0.zip, r-release: mixedLSR_0.1.0.zip, r-oldrel: mixedLSR_0.1.0.zip |
macOS binaries: | r-devel (arm64): mixedLSR_0.1.0.tgz, r-release (arm64): mixedLSR_0.1.0.tgz, r-oldrel (arm64): mixedLSR_0.1.0.tgz, r-devel (x86_64): mixedLSR_0.1.0.tgz, r-release (x86_64): mixedLSR_0.1.0.tgz, r-oldrel (x86_64): mixedLSR_0.1.0.tgz |
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