Package: baygel 0.3.0

Jarod Smith

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:Jarod Smith [aut, cre], Mohammad Arashi [aut], Andriette Bekker [aut]

baygel_0.3.0.tar.gz
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://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/jarod-smithy/baygel/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

openblascppopenmp

1.00 score 2 scripts 227 downloads 7 exports 3 dependencies

Last updated 1 years agofrom:50a1991e2d. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 05 2024
R-4.5-linux-x86_64OKDec 05 2024

Exports:blockBAGENIblockBAGENIIblockBAGLblockBAGRblockBGENblockBGLblockBGR

Dependencies:RcppRcppArmadilloRcppProgress