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 |
| Maintainer: | Jarod Smith <jarodsmith706 at gmail.com> |
| License: | GPL (≥ 3) |
| URL: | https://github.com/Jarod-Smithy/baygel |
| NeedsCompilation: | yes |
| CRAN checks: | baygel results |
| Reference manual: | baygel.html , baygel.pdf |
| 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 |
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