remaCor: Random Effects Meta-Analysis for Correlated Test Statistics
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: | 0.0.20 |
| 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: | 2025-08-20 |
| 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 |
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