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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.0 |
rolling linux/jammy R-4.5 | RMTL_1.0.0.tar.gz |
399.1 KiB |
1.0.0 |
rolling linux/noble R-4.5 | RMTL_1.0.0.tar.gz |
399.0 KiB |
1.0.0 |
rolling source/ R- | RMTL_1.0.0.tar.gz |
309.2 KiB |
1.0.0 |
latest linux/jammy R-4.5 | RMTL_1.0.0.tar.gz |
399.1 KiB |
1.0.0 |
latest linux/noble R-4.5 | RMTL_1.0.0.tar.gz |
399.0 KiB |
1.0.0 |
latest source/ R- | RMTL_1.0.0.tar.gz |
309.2 KiB |
1.0.0 |
2026-04-26 source/ R- | RMTL_1.0.0.tar.gz |
309.2 KiB |
1.0.0 |
2026-04-23 source/ R- | RMTL_1.0.0.tar.gz |
309.2 KiB |
1.0.0 |
2026-04-09 windows/windows R-4.5 | RMTL_1.0.0.zip |
400.2 KiB |
0.9.9 |
2025-04-20 source/ R- | RMTL_0.9.9.tar.gz |
292.8 KiB |