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.">

baygel: Bayesian Shrinkage Estimators for Precision Matrices in Gaussian Graphical Models (original) (raw)

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
License: GPL (≥ 3)
URL: https://github.com/Jarod-Smithy/baygel
NeedsCompilation: yes
CRAN checks: baygel results

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