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: |
0.0.18 |
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:
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