GCPBayes: GCPBayes Package

In GCPBayes: Bayesian Meta-Analysis of Pleiotropic Effects Using Group Structure

GCPBayes Package

Description

Execute a Gibbs sampler to analyze a multivariate Bayesian sparse group selection model, incorporating Dirac, continuous, and hierarchical spike priors to detect pleiotropic effects across multiple traits. This package is tailored for summary statistics comprising estimated regression coefficients and their corresponding covariance matrices.

Author(s)

Taban Baghfalaki [e-mail], [e-mail]

References

1. Baghfalaki, T., Sugier, P. E., Truong, T., Pettitt, A. N., Mengersen, K., & Liquet, B. (2021). Bayesian meta analysis models for cross cancer genomic investigation of pleiotropic effects using group structure. Statistics in Medicine, 40(6), 1498-1518.

2. Baghfalaki, T., Sugier, Y. Asgari, P. E., Truong, & Liquet, B. (2023). GCPBayes: An R Package for Studying Cross-Phenotype Genetic Associations with Group-Level Bayesian Meta-Analysis. RJournal, 15(1), 122-141.

See Also

Useful links:

• https://github.com/tbaghfalaki/GCPBayes

GCPBayes documentation built on Nov. 29, 2025, 1:06 a.m.