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-4.7-x86_64)gigg_0.2.1.zip(r-4.6-x86_64)gigg_0.2.1.zip(r-4.5-x86_64)
gigg_0.2.1.tgz(r-4.6-x86_64)gigg_0.2.1.tgz(r-4.6-arm64)gigg_0.2.1.tgz(r-4.5-x86_64)gigg_0.2.1.tgz(r-4.5-arm64)
gigg_0.2.1.tar.gz(r-4.7-arm64)gigg_0.2.1.tar.gz(r-4.7-x86_64)gigg_0.2.1.tar.gz(r-4.6-arm64)gigg_0.2.1.tar.gz(r-4.6-x86_64)
gigg_0.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
gigg/json (API)

# Install 'gigg' in R:

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

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

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

concentrated - Example data set
distributed - Example data set

On CRAN:

openblas cpp

2.70 score 2 scripts 307 downloads 1 exports 3 dependencies

Last updated from:e2aaaf75ce. Checks:11 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	NOTE	138
linux-devel-x86_64	NOTE	137
source / vignettes	OK	184
linux-release-arm64	NOTE	140
linux-release-x86_64	NOTE	123
macos-release-arm64	NOTE	113
macos-release-x86_64	NOTE	265
macos-oldrel-arm64	NOTE	81
macos-oldrel-x86_64	NOTE	289
windows-devel	NOTE	115
windows-release	NOTE	128
windows-oldrel	NOTE	105
wasm-release	OK	143