Package: RMTL
Title: Regularized Multi-Task Learning
Type: Package
Version: 1.0.0
Authors@R: c(person("Han", "Cao", email="hank9cao@gmail.com", role=c("cre", "aut", "cph")), 
  person("Emanuel", "Schwarz", email="emanuel.schwarz@zi-mannheim.de", role="aut"))
Description: 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>.
Depends: R (>= 3.5.0)
URL: https://github.com/transbioZI/RMTL
BugReports: https://github.com/transbioZI/RMTL/issues
Imports: MASS (>= 7.3-50), psych (>= 1.8.4), corpcor (>= 1.6.9),
        doParallel (>= 1.0.14), foreach (>= 1.4.4)
Date: 2026-02-20
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.3
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-02-20 12:47:35 UTC; hcao1
Author: Han Cao [cre, aut, cph],
  Emanuel Schwarz [aut]
Maintainer: Han Cao <hank9cao@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-22 00:20:02 UTC
Built: R 4.6.0; ; 2026-04-27 19:51:23 UTC; unix
