gglasso: Group Lasso Penalized Learning Using a Unified BMD Algorithm

A unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. The package is an implementation of Yang, Y. and Zou, H. (2015) DOI: <doi:10.1007/s11222-014-9498-5>.

Version: 1.5
Imports: methods
Suggests: testthat, knitr, rmarkdown
Published: 2020-03-18
Author: Yi Yang [aut, cre] (http://www.math.mcgill.ca/yyang/), Hui Zou [aut] (http://users.stat.umn.edu/~zouxx019/), Sahir Bhatnagar [aut] (http://sahirbhatnagar.com/)
Maintainer: Yi Yang <yi.yang6 at mcgill.ca>
BugReports: https://github.com/emeryyi/gglasso/issues
License: GPL-2
URL: https://github.com/emeryyi/gglasso
NeedsCompilation: yes
Materials: README ChangeLog
CRAN checks: gglasso results

Downloads:

Reference manual: gglasso.pdf
Vignettes: Introduction to gglasso
Package source: gglasso_1.5.tar.gz
Windows binaries: r-devel: gglasso_1.4.zip, r-devel-gcc8: gglasso_1.5.zip, r-release: gglasso_1.5.zip, r-oldrel: gglasso_1.5.zip
OS X binaries: r-release: gglasso_1.5.tgz, r-oldrel: gglasso_1.5.tgz
Old sources: gglasso archive

Reverse dependencies:

Reverse imports: FIT, MLGL, PhylogeneticEM, sail

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