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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.
Getting started
Browse package contents
Package details |
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| Author | Alexander White [aut, cre] (<https://orcid.org/0000-0002-9117-1475>), Sha Cao [aut] (<https://orcid.org/0000-0002-8645-848X>), Yi Zhao [ctb] (<https://orcid.org/0000-0003-4766-5934>), Chi Zhang [ctb] (<https://orcid.org/0000-0001-9553-0925>) |
| Maintainer | Alexander White <whitealj@iu.edu> |
| License | MIT + file LICENSE |
| Version | 0.1.0 |
| URL | https://alexanderjwhite.github.io/mixedLSR/ |
| Package repository | View on CRAN |
| Installation |
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