Package: baygel 0.3.0
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.
Authors:
baygel_0.3.0.tar.gz
baygel_0.3.0.zip(r-4.5)baygel_0.3.0.zip(r-4.4)baygel_0.3.0.zip(r-4.3)
baygel_0.3.0.tgz(r-4.5-x86_64)baygel_0.3.0.tgz(r-4.5-arm64)baygel_0.3.0.tgz(r-4.4-x86_64)baygel_0.3.0.tgz(r-4.4-arm64)baygel_0.3.0.tgz(r-4.3-x86_64)baygel_0.3.0.tgz(r-4.3-arm64)
baygel_0.3.0.tar.gz(r-4.5-noble)baygel_0.3.0.tar.gz(r-4.4-noble)
baygel_0.3.0.tgz(r-4.4-emscripten)baygel_0.3.0.tgz(r-4.3-emscripten)
baygel.pdf |baygel.html✨
baygel/json (API)
| # Install 'baygel' in R: |
| install.packages('baygel', repos = c('https://jarod-smithy.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jarod-smithy/baygel/issues
Last updated 1 years agofrom:2484b8b487. Checks:11 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 01 2025 |
| R-4.5-win-x86_64 | OK | Mar 01 2025 |
| R-4.5-mac-x86_64 | OK | Mar 01 2025 |
| R-4.5-mac-aarch64 | OK | Mar 01 2025 |
| R-4.5-linux-x86_64 | OK | Mar 01 2025 |
| R-4.4-win-x86_64 | OK | Mar 01 2025 |
| R-4.4-mac-x86_64 | OK | Mar 01 2025 |
| R-4.4-mac-aarch64 | OK | Mar 01 2025 |
| R-4.3-win-x86_64 | OK | Mar 01 2025 |
| R-4.3-mac-x86_64 | OK | Mar 01 2025 |
| R-4.3-mac-aarch64 | OK | Mar 01 2025 |
Exports:blockBAEblockBAGENIblockBAGENIIblockBAGLblockBAGRblockBGENblockBGLblockBGR
Dependencies:pracmaRcppRcppArmadilloRcppProgressstatmod
