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.zip(r-4.7-x86_64)baygel_0.3.0.zip(r-4.7-arm64)baygel_0.3.0.zip(r-4.6-x86_64)baygel_0.3.0.zip(r-4.6-arm64)
baygel_0.3.0.tar.gz(r-4.7-arm64)baygel_0.3.0.tar.gz(r-4.7-x86_64)baygel_0.3.0.tar.gz(r-4.6-arm64)baygel_0.3.0.tar.gz(r-4.6-x86_64)
baygel_0.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
baygel/json (API)

# Install 'baygel' in R:
install.packages('baygel', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

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

On CRAN:

Conda:

openblascppopenmp

1.00 score 2 scripts 336 downloads 7 exports 3 dependencies

Last updated from:50a1991e2d. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK116
linux-devel-x86_64OK135
source / vignettesOK254
linux-release-arm64OK111
linux-release-x86_64OK163
windows-devel-arm64OK262
windows-devel-x86_64OK154
windows-release-arm64OK225
windows-release-x86_64OK208
wasm-releaseOK129

Exports:blockBAGENIblockBAGENIIblockBAGLblockBAGRblockBGENblockBGLblockBGR

Dependencies:RcppRcppArmadilloRcppProgress