Conduct penalized meta-analysis, see Van Lissa, Van Erp, & Clapper (2023) <doi:10.31234/osf.io/6phs5>. In meta-analysis, there are often between-study differences. These can be coded as moderator variables, and controlled for using meta-regression. However, if the number of moderators is large relative to the number of studies, such an analysis may be overfit. Penalized meta-regression is useful in these cases, because it shrinks the regression slopes of irrelevant moderators towards zero.
Version: 0.1.4 Depends: R (≥ 3.4.0) Imports: methods, rstan (≥ 2.26.0), Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rstantools (≥ 2.1.1), sn, shiny, ggplot2, cli LinkingTo: BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.26.0), StanHeaders (≥ 2.26.0) Suggests: rmarkdown, knitr, mice, testthat (≥ 3.0.0), webexercises, bain, metaforest, metafor Published: 2025-03-30 DOI: 10.32614/CRAN.package.pema Author: Caspar J van Lissa [aut, cre], Sara J van Erp [aut] Maintainer: Caspar J van Lissa <c.j.vanlissa at tilburguniversity.edu> License: GPL (≥ 3) URL: https://github.com/cjvanlissa/pema, https://cjvanlissa.github.io/pema/ NeedsCompilation: yes SystemRequirements: GNU make Citation: pema citation info Materials: README In views: MetaAnalysis CRAN checks: pema results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=pema to link to this page.
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