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 ORCID iD [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>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: sMTL results


Reference manual: sMTL.pdf


Package source: sMTL_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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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