pema: Penalized Meta-Analysis

Conduct penalized meta-analysis, see Van Lissa & Van Erp (2021). <doi:10.31234/>. 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: 0.1.2
Depends: R (≥ 3.4.0)
Imports: methods, rstan (≥ 2.18.1), Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rstantools (≥ 2.1.1), sn, shiny, ggplot2
LinkingTo: BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥, RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0)
Suggests: rmarkdown, knitr, mice, testthat (≥ 3.0.0)
Published: 2022-07-17
Author: Caspar J van Lissa ORCID iD [aut, cre], Sara J van Erp [aut]
Maintainer: Caspar J van Lissa <c.j.vanlissa at>
License: GPL (≥ 3)
NeedsCompilation: yes
SystemRequirements: GNU make
Citation: pema citation info
Materials: README
In views: MetaAnalysis
CRAN checks: pema results


Reference manual: pema.pdf
Vignettes: Conducting a Bayesian Regularized Meta-analysis


Package source: pema_0.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): pema_0.1.2.tgz, r-oldrel (arm64): pema_0.1.2.tgz, r-release (x86_64): pema_0.1.2.tgz, r-oldrel (x86_64): pema_0.1.2.tgz
Old sources: pema archive


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