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) . 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 Repository: https://transbiozi.r-universe.dev Date/Publication: 2026-02-20 12:57:27 UTC RemoteUrl: https://github.com/transbiozi/rmtl RemoteRef: HEAD RemoteSha: b6a01ad5b409babc3a614c454322a7b8cf5b53bb NeedsCompilation: no Packaged: 2026-10-11 08:48:18 UTC; root Author: Han Cao [cre, aut, cph], Emanuel Schwarz [aut] Maintainer: Han Cao