Performs Bayesian meta-analysis 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>.

c(person("Burak Kuersad", "Guenhan", email="burakgunhan@gmail.com",
role=c("aut", "cre"), comment = c(ORCID = "0000-0002-7454-8680")),
person("Trustees of", "Columbia University", role="cph", comment="src/init.cpp,
tools/make_cpp.R, R/stanmodels.R"))

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>.
c(person("Burak Kuersad", "Guenhan", email="burakgunhan@gmail.com",
role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7454-8680")),
person("Christian", "Roever", email="christian.roever@med.uni-goettingen.de",
role=c("aut"), comment = c(ORCID = "0000-0002-6911-698X")),
person("Trustees of", "Columbia University", role="cph", comment="src/init.cpp,
tools/make_cpp.R, R/stanmodels.R"))
testthat, knitr, rmarkdown, ggplot2, shinystan, vdiffr
Burak Kuersad Guenhan [aut, cre]
(<https://orcid.org/0000-0002-7454-8680>),
Christian Roever [aut] (<https://orcid.org/0000-0002-6911-698X>),
Trustees of Columbia University [cph] (src/init.cpp, tools/make_cpp.R,
R/stanmodels.R)

Subtracting Summary Statistics of One or more Cohorts from
Meta-GWAS Results
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.