Package: SSGL 1.0

Ray Bai

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) <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>.

Authors:Ray Bai

SSGL_1.0.tar.gz
SSGL_1.0.tar.gz(r-4.5-noble)SSGL_1.0.tar.gz(r-4.4-noble)
SSGL_1.0.tgz(r-4.4-emscripten)SSGL_1.0.tgz(r-4.3-emscripten)
SSGL.pdf |SSGL.html
SSGL/json (API)

# Install 'SSGL' in R:
install.packages('SSGL', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 1 scripts 153 downloads 6 exports 5 dependencies

Last updated 1 years agofrom:e9dfa19109. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-linuxOKNov 20 2024

Exports:cv_gamma_grpregcv_nb_grpregcv_SSGLgamma_grpregnb_grpregSSGL

Dependencies:grpreglatticeMASSMatrixpracma