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baygel: Bayesian Shrinkage Estimators for Precision Matrices in Gaussian Graphical Models

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) <doi:10.1214/12-BA729>; Smith et al. (2022) <doi:10.48550/arXiv.2210.16290> and Smith et al. (2023) <doi:10.48550/arXiv.2306.14199>, respectively.

Version: 0.3.0
Imports: Rcpp (≥ 1.0.8), RcppArmadillo (≥ 0.11.1.1.0)
LinkingTo: Rcpp, RcppArmadillo, RcppProgress
Suggests: MASS, pracma
Published: 2023-11-11
DOI: 10.32614/CRAN.package.baygel
Author: Jarod Smith ORCID iD [aut, cre], Mohammad Arashi ORCID iD [aut], Andriette Bekker ORCID iD [aut]
Maintainer: Jarod Smith <jarodsmith706 at gmail.com>
License: GPL (≥ 3)
URL: https://github.com/Jarod-Smithy/baygel
NeedsCompilation: yes
CRAN checks: baygel results

Documentation:

Reference manual: baygel.pdf

Downloads:

Package source: baygel_0.3.0.tar.gz
Windows binaries: r-devel: baygel_0.3.0.zip, r-release: baygel_0.3.0.zip, r-oldrel: baygel_0.3.0.zip
macOS binaries: r-release (arm64): baygel_0.3.0.tgz, r-oldrel (arm64): baygel_0.3.0.tgz, r-release (x86_64): baygel_0.3.0.tgz, r-oldrel (x86_64): baygel_0.3.0.tgz
Old sources: baygel archive

Linking:

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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.