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SSGL  

Spike-and-Slab Group Lasso for Group-Regularized Generalized Linear Models
View on CRAN: Click here


Download and install SSGL package within the R console
Install from CRAN:
install.packages("SSGL")

Install from Github:
library("remotes")
install_github("cran/SSGL")

Install by package version:
library("remotes")
install_version("SSGL", "1.0")



Attach the package and use:
library("SSGL")
Maintained by
Ray Bai
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-06-27
Latest Update: 2023-06-27
Description:
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>.
How to cite:
Ray Bai (2023). SSGL: Spike-and-Slab Group Lasso for Group-Regularized Generalized Linear Models. R package version 1.0, https://cran.r-project.org/web/packages/SSGL. Accessed 21 Nov. 2024.
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