Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent. Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known. Fixed effect meta-analysis uses the method of Lin and Sullivan (2009) <doi:10.1016/j.ajhg.2009.11.001>, and random effects meta-analysis uses the method of Han, et al. <doi:10.1093/hmg/ddw049>.
Version: 0.0.18 Depends: R (≥ 3.6.0), ggplot2, methods Imports: mvtnorm, grid, reshape2, compiler, Rcpp, EnvStats, Rdpack, stats LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, RUnit, clusterGeneration, metafor Published: 2024-02-08 DOI: 10.32614/CRAN.package.remaCor Author: Gabriel Hoffman [aut, cre] Maintainer: Gabriel Hoffman <gabriel.hoffman at mssm.edu> BugReports: https://github.com/DiseaseNeurogenomics/remaCor/issues License: Artistic-2.0 URL: https://diseaseneurogenomics.github.io/remaCor/ NeedsCompilation: yes Citation: remaCor citation info Materials: README NEWS In views: MetaAnalysis CRAN checks: remaCor results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=remaCor to link to this page.
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