The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
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[@]metanet.ch.

SSGL: Spike-and-Slab Group Lasso for Group-Regularized Generalized Linear Models

Fits group-regularized generalized linear models (GLMs) using the spike-and-slab group lasso (SSGL) prior introduced by Bai et al. (2022) <doi:10.1080/01621459.2020.1765784> and extended to GLMs by Bai (2023) <doi:10.48550/arXiv.2007.07021>. This package supports fitting the SSGL model for the following GLMs with group sparsity: Gaussian linear regression, binary logistic regression, Poisson regression, negative binomial regression, and gamma regression. Stand-alone functions for group-regularized negative binomial regression and group-regularized gamma regression are also available, with the option of employing the group lasso penalty of Yuan and Lin (2006) <doi:10.1111/j.1467-9868.2005.00532.x>, the group minimax concave penalty (MCP) of Breheny and Huang <doi:10.1007/s11222-013-9424-2>, or the group smoothly clipped absolute deviation (SCAD) penalty of Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>.

Version: 1.0
Depends: R (≥ 3.6.0)
Imports: stats, MASS, pracma, grpreg
Published: 2023-06-27
DOI: 10.32614/CRAN.package.SSGL
Author: Ray Bai
Maintainer: Ray Bai <raybaistat at gmail.com>
License: GPL-3
NeedsCompilation: yes
CRAN checks: SSGL results

Documentation:

Reference manual: SSGL.pdf

Downloads:

Package source: SSGL_1.0.tar.gz
Windows binaries: r-devel: SSGL_1.0.zip, r-release: SSGL_1.0.zip, r-oldrel: SSGL_1.0.zip
macOS binaries: r-release (arm64): SSGL_1.0.tgz, r-oldrel (arm64): SSGL_1.0.tgz, r-release (x86_64): SSGL_1.0.tgz, r-oldrel (x86_64): SSGL_1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=SSGL 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.