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 [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:

Reference manual:	mixedLSR.pdf
Vignettes:	Introduction to mixedLSR

Downloads:

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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