gcdnet: The (Adaptive) LASSO and Elastic Net Penalized Least Squares, Logistic Regression, Hybrid Huberized Support Vector Machines, Squared Hinge Loss Support Vector Machines and Expectile Regression using a Fast Generalized Coordinate Descent Algorithm

Implements a generalized coordinate descent (GCD) algorithm for computing the solution paths of the hybrid Huberized support vector machine (HHSVM) and its generalizations. Supported models include the (adaptive) LASSO and elastic net penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression.

Version: 1.0.6
Imports: grDevices, graphics, stats, methods, Matrix
Suggests: testthat
Published: 2022-08-14
DOI: 10.32614/CRAN.package.gcdnet
Author: Yi Yang, Yuwen Gu, Hui Zou
Maintainer: Yi Yang <yi.yang6 at mcgill.ca>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/emeryyi/gcdnet
NeedsCompilation: yes
Materials: ChangeLog
CRAN checks: gcdnet results


Reference manual: gcdnet.pdf


Package source: gcdnet_1.0.6.tar.gz
Windows binaries: r-devel: gcdnet_1.0.6.zip, r-release: gcdnet_1.0.6.zip, r-oldrel: gcdnet_1.0.6.zip
macOS binaries: r-release (arm64): gcdnet_1.0.6.tgz, r-oldrel (arm64): gcdnet_1.0.6.tgz, r-release (x86_64): gcdnet_1.0.6.tgz, r-oldrel (x86_64): gcdnet_1.0.6.tgz
Old sources: gcdnet archive

Reverse dependencies:

Reverse imports: hdmed, higlasso


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