_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-remacor 0.0.18
Propagated dependencies: r-envstats@3.0.0 r-ggplot2@3.5.1 r-mvtnorm@1.3-2 r-rcpp@1.0.13-1 r-rcpparmadillo@14.0.2-1 r-rdpack@2.6.1 r-reshape2@1.4.4
Channel: guix
Location: gnu/packages/cran.scm (gnu packages cran)
Home page: https://diseaseneurogenomics.github.io/remaCor/
Licenses: Artistic License 2.0
Synopsis: Random effects meta-analysis for correlated test statistics
Description:

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.

Total results: 1