sMTL: Sparse Multi-Task Learning
Implements L0-constrained Multi-Task Learning and domain generalization algorithms. The algorithms are coded in Julia allowing for fast implementations of the coordinate descent and local combinatorial search algorithms. For more details, see a preprint of the paper: Loewinger et al., (2022) <doi:10.48550/arXiv.2212.08697>.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | glmnet,JuliaCall,JuliaConnectoR,caret,dplyr |
| Suggests: | knitr,rmarkdown |
| Published: | 2023-02-06 |
| DOI: | 10.32614/CRAN.package.sMTL |
| Author: | Gabriel Loewinger [aut, cre], Kayhan Behdin [aut], Giovanni Parmigiani [aut], Rahul Mazumder [aut], National Science Foundation Grant DMS1810829 [fnd], National Science Foundation Grant DMS2113707 [fnd], National Science Foundation Grant NSF-IIS1718258, [fnd], Office of Naval Research Grant ONR N000142112841 [fnd], National Institute on Drug Abuse (NIH) Grant F31DA052153 [fnd] |
| Maintainer: | Gabriel Loewinger <gloewinger at gmail.com> |
| BugReports: | https://github.com/gloewing/sMTL/issues |
| License: | MIT + fileLICENSE |
| URL: | https://github.com/gloewing/sMTL,https://rpubs.com/gloewinger/996629 |
| NeedsCompilation: | no |
| CRAN checks: | sMTL results |
Documentation:
| Reference manual: | sMTL.html ,sMTL.pdf |
Downloads:
| Package source: | sMTL_0.1.0.tar.gz |
| Windows binaries: | r-devel:sMTL_0.1.0.zip, r-release:sMTL_0.1.0.zip, r-oldrel:sMTL_0.1.0.zip |
| macOS binaries: | r-release (arm64):sMTL_0.1.0.tgz, r-oldrel (arm64):sMTL_0.1.0.tgz, r-release (x86_64):sMTL_0.1.0.tgz, r-oldrel (x86_64):sMTL_0.1.0.tgz |
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