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RMTL

1.0.0

Regularized Multi-Task Learning

1packages depend
7.6Kdownloads / year
test coverage
13/13checks pass

Overview

About
Maintained by Han CaoFirst published 2019-02-273 releasesCRAN page ↗GitHub ↗

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.

Install

Health

CRAN checks
13OK
Slowest check: 2.0 min · r-oldrel-macos-x86_64
Code health
None
Tests · ratio 0.00
not tracked
Coverage
100%
Documentation · exports
5
Dependencies · direct
Check history
  • OK2026-08-05
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-08-01
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-03-10
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMENoVignettesYes · dynamicpkgdown siteNoNEWSYes · 100% structuredCode of conductNoContributing guideNo
Examples that run
100%
Documented parameters
92%
Return-value docs
80%
References docs
0%

Downloads

7.6K
CRAN downloads in the past year
Rank #10,668 · ~21/day · ~632/mo
Daily download trend is not available in this view yet.
21830 days
91590 days
7.6K1 year
Compare downloads with other packages →
Also on118 r2u21 autocran

Repository

Repository
19Stars
12Forks
1Open issues
0Open PRs
0Releases
12Commits
3Contributors
multi-task-learningsparse-codinglow-rank-representaionregularization
12 commits · Last activity 2026-06-13

Stars over time

2024-03-21 · 192026-07-07 · 19

Repository practices

Upstream repositoryBeta

2 development-tooling and community-health practices detected across 2 families in the upstream repository

Checks run against github.com/transbiozi/rmtl on 2026-08-23.

CRAN release process (1)
cran-comments.md
Lint, format, editor (1)
VS Code
How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies
6 external dependencies (excludes base and recommended)
Depends (1)
R >= 3.5.0
LinkingTo (0)
none
Suggests (2)
Enhances (0)
none
Reverse dependencies
1direct
0indirect

Code & Tests

People & History

People (2)
Maintainer (1)
Maintainer, Author, Copyright holder
Authors (2)
Maintainer, Author, Copyright holder
Copyright holders (1)
Maintainer, Author, Copyright holder
Package Timeline

3 releases. Pick two to compare their code metrics. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • 1.0.0Latest
    2026-02-22 · current release · diff ↗
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • R
    R 4.3.0 released · 2023-04-21
  • 0.9.9
    2022-05-02 · diff ↗
  • R
    R 4.2.0 released · 2022-04-22
  • R
    R 4.1.0 released · 2021-05-18
  • R
    R 4.0.0 released · 2020-04-24
  • R
    R 3.6.0 released · 2019-04-26
  • 0.9
    2019-02-27
  • R
    R 3.5.0 released · 2018-04-23

Package metadata

First published
2019-02-27
Total releases
3 / 7 yrs
License
GPL-3 OSI
Minimum R
≥ 3.5.0
Download size
310 KB
Installed size
not tracked yet
With dependencies
not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("RMTL")
Cao, H., & Schwarz, E. (2026). RMTL: Regularized Multi-Task Learning (Version 1.0.0) [Computer software]. https://doi.org/10.32614/CRAN.package.RMTL

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for RMTL version 1.0.0 [Data set]. HJJB, LLC. Data release v2026-08-25. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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