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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:
Downloads:
Linking:
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https://CRAN.R-project.org/package=mixedLSR
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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