Regularized Multi-Task Learning R package RMTL version 1.0.0 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>. Software 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 Han Cao <hank9cao@gmail.com> Comprehensive R Archive Network (CRAN) Han Cao [cre, aut, cph], Emanuel Schwarz [aut] GPL-3 2026-02-22 application/tgz https://CRAN.R-project.org/package=RMTL doi:10.32614/CRAN.package.RMTL