RMTL: Regularized Multi-Task Learning

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.pdf
Vignettes: An Tutorial for Regularized Multi-task Learning using the package RMTL


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 dependencies:

Reverse enhances: joinet


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