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

Run a Gibbs sampler for a multivariate Bayesian sparse group selection model with Dirac, continuous and hierarchical spike prior for detecting pleiotropy on the traits. This package is designed for summary statistics containing estimated regression coefficients and its estimated covariance matrix. The methodology is available from: Baghfalaki, T., Sugier, P. E., Truong, T., Pettitt, A. N., Mengersen, K., & Liquet, B. (2021) <doi:10.1002/sim.8855>.

Version:	4.1.0
Depends:	R (≥ 3.5.0)
Imports:	MASS, mvtnorm, invgamma, gdata, truncnorm, postpack, wiqid, Rcpp (≥ 1.0.9)
LinkingTo:	Rcpp, RcppArmadillo
Published:	2023-12-04
Author:	Taban Baghfalaki
Maintainer:	Taban Baghfalaki <t.baghfalaki at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)]
NeedsCompilation:	yes
Materials:	NEWS
CRAN checks:	GCPBayes results

Documentation:

Reference manual:

GCPBayes.pdf

Downloads:

Package source:	GCPBayes_4.1.0.tar.gz
Windows binaries:	r-devel: GCPBayes_4.1.0.zip, r-release: GCPBayes_4.1.0.zip, r-oldrel: GCPBayes_4.0.0.zip
macOS binaries:	r-release (arm64): GCPBayes_4.1.0.tgz, r-oldrel (arm64): GCPBayes_4.1.0.tgz, r-release (x86_64): GCPBayes_4.1.0.tgz, r-oldrel (x86_64): GCPBayes_4.0.0.tgz
Old sources:	GCPBayes archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=GCPBayes to link to this page.