The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

sglssnal: Sparse-Group Lasso via Semismooth Newton Augmented Lagrangian

Implements the sparse-group lasso method of Zhang et al. (2020) <doi:10.1007/s10107-018-1329-6>. Unlike many widely available methods based on first-order descent, this method uses second-order information to solve the dual optimization problem via a semismooth Newton method.

Version: 0.1.0
Depends: R (≥ 4.4.0)
Imports: Matrix, Rcpp, RSpectra, methods, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, spelling, testthat (≥ 3.0.0)
Published: 2026-09-11
DOI: 10.32614/CRAN.package.sglssnal (may not be active yet)
Author: Robin Liu [aut, cre], Yangjing Zhang [ctb] (Author of the original MATLAB SSNAL implementation this package ports)
Maintainer: Robin Liu <robin28liu at gmail.com>
BugReports: https://github.com/roobnloo/sglssnal/issues
License: MIT + file LICENSE
URL: https://github.com/roobnloo/sglssnal
NeedsCompilation: yes
Language: en-US
Citation: sglssnal citation info
Materials: NEWS
CRAN checks: sglssnal results

Documentation:

Reference manual: sglssnal.html , sglssnal.pdf
Vignettes: Introduction to sglssnal (source, R code)

Downloads:

Package source: sglssnal_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: sglssnal_0.1.0.zip
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): sglssnal_0.1.0.tgz, r-release (x86_64): sglssnal_0.1.0.tgz, r-oldrel (x86_64): sglssnal_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=sglssnal to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.