mixedLSR: Mixed, Low-Rank, and Sparse Multivariate Regression on High-Dimensional Data

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 ORCID iD [aut, cre], Sha Cao ORCID iD [aut], Yi Zhao ORCID iD [ctb], Chi Zhang ORCID iD [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


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-release (arm64): mixedLSR_0.1.0.tgz, r-oldrel (arm64): 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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