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
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

Documentation:

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

Downloads:

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

Linking:

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