Package: RMTL 0.9

Han Cao

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>.

Authors:Han Cao [cre, aut, cph], Emanuel Schwarz [aut]

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RMTL.pdf |RMTL.html
RMTL/json (API)

# Install 'RMTL' in R:
install.packages('RMTL', repos = c('https://transbiozi.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/transbiozi/rmtl/issues

On CRAN:

low-rank-representaionmulti-task-learningregularizationsparse-coding

5.60 score 19 stars 21 scripts 610 downloads 2 mentions 5 exports 11 dependencies

Last updated 6 years agofrom:8e5bf4dcd4. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 12 2024
R-4.5-winOKNov 12 2024
R-4.5-linuxOKNov 12 2024
R-4.4-winOKNov 12 2024
R-4.4-macOKNov 12 2024
R-4.3-winOKNov 12 2024
R-4.3-macOKNov 12 2024

Exports:calcErrorCreate_simulated_datacvMTLMTLplotObj

Dependencies:codetoolscorpcordoParallelforeachGPArotationiteratorslatticeMASSmnormtnlmepsych

An Tutorial for Regularized Multi-task Learning using the package RMTL

Rendered fromrmtl.Rmdusingknitr::rmarkdownon Nov 12 2024.

Last update: 2019-03-03
Started: 2019-03-03