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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