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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 |
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 |
Reference manual: | sMTL.pdf |
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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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