pema: Penalized Meta-Analysis

Conduct penalized meta-analysis, see Van Lissa & Van Erp (2021). <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: 0.1.1
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 (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0)
Suggests: rmarkdown, knitr, tidySEM, mice, testthat (≥ 3.0.0)
Published: 2022-04-25
Author: Caspar J van Lissa ORCID iD [aut, cre], Sara J van Erp [aut]
Maintainer: Caspar J van Lissa <c.j.vanlissa at uu.nl>
License: GPL (≥ 3)
URL: https://github.com/cjvanlissa/pema
NeedsCompilation: yes
SystemRequirements: GNU make
Citation: pema citation info
Materials: README
In views: MetaAnalysis
CRAN checks: pema results

Documentation:

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

Downloads:

Package source: pema_0.1.1.tar.gz
Windows binaries: r-devel: pema_0.1.1.zip, r-release: pema_0.1.1.zip, r-oldrel: pema_0.1.1.zip
macOS binaries: r-release (arm64): pema_0.1.1.tgz, r-oldrel (arm64): pema_0.1.1.tgz, r-release (x86_64): pema_0.1.1.tgz, r-oldrel (x86_64): pema_0.1.1.tgz
Old sources: pema archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=pema to link to this page.