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 |
| 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 + file LICENSE |
| URL: | https://github.com/gloewing/sMTL, https://rpubs.com/gloewinger/996629 |
| NeedsCompilation: | no |
| CRAN checks: | sMTL results |
Documentation:
| Reference manual: | 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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