Sparse Multi-Task Learning R package sMTL version 0.1.0 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) <arXiv:2212.08697>. Software Depends: R (>= 3.5.0) Imports: glmnet, JuliaCall, JuliaConnectoR, caret, dplyr Suggests: knitr, rmarkdown Gabriel Loewinger <gloewinger@gmail.com> Comprehensive R Archive Network (CRAN) Gabriel Loewinger [aut, cre] (ORCID: <https://orcid.org/0000-0002-0755-8520>), 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] MIT + file LICENSE (https://CRAN.R-project.org/package=sMTL/LICENSE) 2023-02-06 application/tgz https://CRAN.R-project.org/package=sMTL doi:10.32614/CRAN.package.sMTL