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baygel  

Bayesian Shrinkage Estimators for Precision Matrices in Gaussian Graphical Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("baygel", "0.3.0")



Attach the package and use:
library("baygel")
Maintained by
Jarod Smith
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-01-30
Latest Update: 2023-11-11
Description:
This R package offers block Gibbs samplers for the Bayesian (adaptive) graphical lasso, ridge, and naive elastic net priors. These samplers facilitate the simulation of the posterior distribution of precision matrices for Gaussian distributed data and were originally proposed by: Wang (2012) ; Smith et al. (2022) and Smith et al. (2023) , respectively.
How to cite:
Jarod Smith (2023). baygel: Bayesian Shrinkage Estimators for Precision Matrices in Gaussian Graphical Models. R package version 0.3.0, https://cran.r-project.org/web/packages/baygel. Accessed 06 Aug. 2026.
Previous versions and publish date:
(2026-07-09 07:20), 0.1.0 (2023-01-30 17:40), 0.2.0 (2023-07-08 13:00)
Other packages that cited baygel R package
View baygel citation profile
Other R packages that baygel depends, imports, suggests or enhances
Complete documentation for baygel
Functions, R codes and Examples using the baygel R package
Some associated functions: blockBAGENI . blockBAGENII . blockBAGL . blockBAGR . blockBGEN . blockBGL . blockBGR . blockBSGR . 
Some associated R codes: RcppExports.R .  Full baygel package functions and examples
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