Imports: rstan (>= 2.18.1), rstantools (>= 1.5.0)
Packaged: 2018-11-14 18:09:28 UTC; burakgunhan

Packaged: 2019-01-19 21:32:47 UTC; burakgunhan
Author: Burak Kuersad Guenhan [aut, cre]

Description: Performs Bayesian meta-analysis and model-based meta-analysis using 'Stan'.
Packaged: 2019-05-11 20:19:46 UTC; burakgunhan

Guenhan, Roever, and Friede (2019) <doi:10.1002/jrsm.1370>.
Packaged: 2019-12-03 14:30:38 UTC; burakgunhan

Guenhan, Roever, and Friede (2020) <doi:10.1002/jrsm.1370>.
Packaged: 2020-04-07 19:17:11 UTC; burakgunhan

Authors@R: c(person("Burak Kuersad", "Guenhan", email="burakgunhan@gmail.com",
Packaged: 2021-07-24 20:51:01 UTC; burakgunhan
Maintainer: Burak Kuersad Guenhan <burakgunhan@gmail.com>

Description: Performs Bayesian meta-analysis, meta-regression and model-based meta-analysis
using 'Stan'. Includes binomial-normal hierarchical models and option to use
weakly informative priors for the heterogeneity parameter and the treatment effect
parameter which are described in Guenhan, Roever, and Friede (2020) <doi:10.1002/jrsm.1370>.
Imports: Rcpp (>= 0.12.17), methods, rstan (>= 2.18.1), rstantools (>=
Suggests: testthat, knitr, rmarkdown, ggplot2, shinystan, vdiffr

Title: Subtracting Summary Statistics of One or more Cohorts from
Maintainer: Ilja M. Nolte <i.m.nolte@umcg.nl>
Description: If results from a meta-GWAS are used for validation in one of the cohorts that was included in the meta-analysis, this will yield biased (i.e. too optimistic) results. The validation cohort needs to be independent from the meta-GWAS results. MetaSubtract will subtract the results of the respective cohort from the meta-GWAS results analytically without having to redo the meta-GWAS analysis using the leave-one-out methodology. It can handle different meta-analyses methods and takes into account if single or double genomic control correction was applied to the original meta-analysis. It can be used for whole GWAS, but also for a limited set of SNPs or other genetic markers.

Description: If results from a meta-GWAS are used for validation in one of the cohorts that was included in the meta-analysis, this will yield biased (i.e. too optimistic) results. The validation cohort needs to be independent from the meta-Genome-Wide-Association-Study (meta-GWAS) results. 'MetaSubtract' will subtract the results of the respective cohort from the meta-GWAS results analytically without having to redo the meta-GWAS analysis using the leave-one-out methodology. It can handle different meta-analyses methods and takes into account if single or double genomic control correction was applied to the original meta-analysis. It can also handle different meta-analysis methods. It can be used for whole GWAS, but also for a limited set of genetic markers.