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: 0.0.11
Depends: R (≥ 3.6.0), RUnit, clusterGeneration, ggplot2, grid, reshape2, methods
Imports: mvtnorm, compiler, Rdpack, stats
Suggests: knitr, metafor
Published: 2022-11-10
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: no
Citation: remaCor citation info
Materials: README NEWS
In views: MetaAnalysis
CRAN checks: remaCor results

Documentation:

Reference manual: remaCor.pdf
Vignettes: remaCor: Random effects meta-analysis for correlated test statistics

Downloads:

Package source: remaCor_0.0.11.tar.gz
Windows binaries: r-devel: remaCor_0.0.11.zip, r-release: remaCor_0.0.11.zip, r-oldrel: remaCor_0.0.11.zip
macOS binaries: r-release (arm64): remaCor_0.0.11.tgz, r-oldrel (arm64): remaCor_0.0.11.tgz, r-release (x86_64): remaCor_0.0.11.tgz, r-oldrel (x86_64): remaCor_0.0.11.tgz
Old sources: remaCor archive

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