mixedLSR: Mixed, Low-Rank, and Sparse Multivariate Regression onHigh-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 + fileLICENSE |
| URL: | https://alexanderjwhite.github.io/mixedLSR/ |
| NeedsCompilation: | no |
| Materials: | README,NEWS |
| CRAN checks: | mixedLSR results |
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