Random Effects Meta-Analysis for Correlated Test Statistics
Overview
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.
- Aug 4, 2025
- in
LS.empirical()add parameter, \code{useGamma} to disable gamma approx of empirical distribution of test statistics
remaCor 0.0.19
- Feb 27, 2024
- in
RE2C()return QE, QEp, Isq, etc even when correlation is not diagonal
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-04-22
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-03-10
Documentation
- Help pages
- 7
- Examples that run
- 100%6 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 87%
- Return-value docs
- 67%
- Help pages citing references
- 43% with a bibliography file
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Package metadata
- First published
- 2022-09-07
- Total releases
- 5 / 4 yrs
- License
- Artistic-2.0 OSI
- Minimum R
- ≥ 3.6.0
- Download size
- 675 KB
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Cite this package
Run in R for the authors' entry:
citation("remaCor")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1093/bioinformatics/btx242, 10.1093/hmg/ddw049, 10.1016/j.ajhg.2011.04.014.
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From data release v2026-10-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.