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.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 = 'https://cloud.r-project.org')

Bug tracker:https://github.com/umich-cphds/gigg/issues0 issues

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

On CRAN:

Conda:

openblascpp

1.70 score 184 downloads 1 exports 3 dependencies

Last updated 4 years agofrom:f84b9a3af3. Checks:1 OK, 2 NOTE. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 09 2025
R-4.5-linux-x86_64NOTEMar 09 2025
R-4.4-linux-x86_64NOTEMar 09 2025

Exports:gigg

Dependencies:BHRcppRcppArmadillo

Citation

To cite package ‘gigg’ in publications use:

Boss J, Mukherjee B (2021). gigg: Group Inverse-Gamma Gamma Shrinkage for Sparse Regression with Grouping Structure. R package version 0.2.1, https://CRAN.R-project.org/package=gigg.

Corresponding BibTeX entry: 

  @Manual{,
    title = {gigg: Group Inverse-Gamma Gamma Shrinkage for Sparse
      Regression with Grouping Structure},
    author = {Jon Boss and Bhramar Mukherjee},
    year = {2021},
    note = {R package version 0.2.1},
    url = {https://CRAN.R-project.org/package=gigg},
  }

Readme and manuals

R package gigg gigg

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

Overview

This package implements 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.

Installation

If the devtools package is not yet installed, install it first:

install.packages('devtools')

# install the package from Github:
devtools::install_github('umich-cphds/gigg')

Once installed, load the package:

library(gigg)

Examples

GIGG regression Gibbs sampler with fixed hyperparameters:

X = concentrated$X
C = concentrated$C
Y = as.vector(concentrated$Y)
grp_idx = concentrated$grps
alpha_inits = concentrated$alpha
beta_inits = concentrated$beta

gf = gigg(X, C, Y, method = "fixed", grp_idx, alpha_inits, beta_inits,
          n_burn_in = 500, n_samples = 1000, n_thin = 1, 
          verbose = TRUE, btrick = FALSE, stable_solve = TRUE)

GIGG regression Gibbs sampler with hyperparameter estimation via Marginal Maximum Likelihood Estimation:

X = concentrated$X
C = concentrated$C
Y = as.vector(concentrated$Y)
grp_idx = concentrated$grps
alpha_inits = concentrated$alpha
beta_inits = concentrated$beta

gf_mmle = gigg(X, C, Y, method = "mmle", grp_idx, alpha_inits, beta_inits,
                n_burn_in = 500, n_samples = 1000, n_thin = 1, 
                verbose = TRUE, btrick = FALSE, stable_solve = TRUE)
Current Suggested Citation

Boss, J., Datta, J., Wang, X., Park, S.K., Kang, J., & Mukherjee, B. (2021). Group Inverse-Gamma Gamma Shrinkage for Sparse Regression with Block-Correlated Predictors. arXiv preprint arXiv:2102.10670.