Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. Based on the accelerated gradient descent method, the algorithms feature a state-of-art computational complexity O(1/k^2). Sparse model structure is induced by the solving the proximal operator. The detail of the package is described in the paper of Han Cao and Emanuel Schwarz (2018) <doi:10.1093/bioinformatics/bty831>.
| Version: | 0.9.9 |
| Depends: | R (≥ 3.5.0) |
| Imports: | MASS (≥ 7.3-50), psych (≥ 1.8.4), corpcor (≥ 1.6.9), doParallel (≥ 1.0.14), foreach (≥ 1.4.4) |
| Suggests: | knitr, rmarkdown |
| Published: | 2022-05-02 |
| DOI: | 10.32614/CRAN.package.RMTL |
| Author: | Han Cao [cre, aut, cph], Emanuel Schwarz [aut] |
| Maintainer: | Han Cao <hank9cao at gmail.com> |
| BugReports: | https://github.com/transbioZI/RMTL/issues/ |
| License: | GPL-3 |
| URL: | https://github.com/transbioZI/RMTL/ |
| NeedsCompilation: | no |
| Materials: | README, NEWS |
| CRAN checks: | RMTL results |
| Reference manual: | RMTL.html , RMTL.pdf |
| Vignettes: |
An Tutorial for Regularized Multi-task Learning using the package RMTL (source, R code) |
| Package source: | RMTL_0.9.9.tar.gz |
| Windows binaries: | r-devel: RMTL_0.9.9.zip, r-release: RMTL_0.9.9.zip, r-oldrel: RMTL_0.9.9.zip |
| macOS binaries: | r-release (arm64): RMTL_0.9.9.tgz, r-oldrel (arm64): RMTL_0.9.9.tgz, r-release (x86_64): RMTL_0.9.9.tgz, r-oldrel (x86_64): RMTL_0.9.9.tgz |
| Old sources: | RMTL archive |
| Reverse suggests: | joinet |
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