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>.">

remaCor: Random Effects Meta-Analysis for Correlated Test Statistics (original) (raw)

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 ORCID iD [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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