Package: gigg 0.2.1

Michael Kleinsasser

gigg: Group Inverse-Gamma Gamma Shrinkage for Sparse Regression with Grouping Structure

A Gibbs sampler corresponding to a Group Inverse-Gamma Gamma (GIGG) regression model with adjustment covariates. Hyperparameters in the GIGG prior specification can either be fixed by the user or can be estimated via Marginal Maximum Likelihood Estimation. Jonathan Boss, Jyotishka Datta, Xin Wang, Sung Kyun Park, Jian Kang, Bhramar Mukherjee (2021) <arxiv:2102.10670>.

Authors:Jon Boss [aut], Bhramar Mukherjee [aut], Michael Kleinsasser [cre]

gigg_0.2.1.tar.gz
gigg_0.2.1.zip(r-4.5)gigg_0.2.1.zip(r-4.4)gigg_0.2.1.zip(r-4.3)
gigg_0.2.1.tgz(r-4.4-x86_64)gigg_0.2.1.tgz(r-4.4-arm64)gigg_0.2.1.tgz(r-4.3-x86_64)gigg_0.2.1.tgz(r-4.3-arm64)
gigg_0.2.1.tar.gz(r-4.5-noble)gigg_0.2.1.tar.gz(r-4.4-noble)
gigg_0.2.1.tgz(r-4.4-emscripten)gigg_0.2.1.tgz(r-4.3-emscripten)
gigg.pdf |gigg.html✨
gigg/json (API)

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

Peer review:

Bug tracker:https://github.com/umich-cphds/gigg/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

1 exports 0.74 score 3 dependencies 1 scripts 201 downloads

Last updated 3 years agofrom:e2aaaf75ce. Checks:OK: 4 NOTE: 5. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 28 2024
R-4.5-win-x86_64NOTEAug 28 2024
R-4.5-linux-x86_64NOTEAug 28 2024
R-4.4-win-x86_64NOTEAug 28 2024
R-4.4-mac-x86_64NOTEAug 28 2024
R-4.4-mac-aarch64NOTEAug 28 2024
R-4.3-win-x86_64OKAug 28 2024
R-4.3-mac-x86_64OKAug 28 2024
R-4.3-mac-aarch64OKAug 28 2024

Exports:gigg

Dependencies:BHRcppRcppArmadillo