CRAN/E | sMTL

sMTL

Sparse Multi-Task Learning

Installation

About

Implements L0-constrained Multi-Task Learning and domain generalization algorithms. The algorithms are coded in Julia allowing for fast implementations of the coordinate descent and local combinatorial search algorithms. For more details, see a preprint of the paper: Loewinger et al., (2022) .

github.com/gloewing/sMTL
rpubs.com/gloewinger/996629
Bug report File report

Key Metrics

Version 0.1.0
R ≥ 3.5.0
Published 2023-02-06 454 days ago
Needs compilation? no
License MIT
License File
CRAN checks sMTL results

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Maintainer

Maintainer

Gabriel Loewinger

gloewinger@gmail.com

Authors

Gabriel Loewinger

aut / cre

Kayhan Behdin

aut

Giovanni Parmigiani

aut

Rahul Mazumder

aut

National Science Foundation Grant DMS1810829

fnd

National Science Foundation Grant DMS2113707

fnd

National Science Foundation Grant NSF-IIS1718258

fnd

Office of Naval Research Grant ONR N000142112841

fnd

National Institute on Drug Abuse

(NIH)

Grant F31DA052153

fnd

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Depends

R ≥ 3.5.0

Imports

glmnet
JuliaCall
JuliaConnectoR
caret
dplyr

Suggests

knitr
rmarkdown