Package: pema 0.1.5

pema: Penalized Meta-Analysis

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.

Authors:Caspar J van Lissa [aut, cre], Sara J van Erp [aut]

pema_0.1.5.tar.gz
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manual.pdf |manual.html✨
card.svg |card.png
pema/json (API)

# Install 'pema' in R:
install.packages('pema', repos = c('https://cjvanlissa.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cjvanlissa/pema/issues

Pkgdown/docs site:https://cjvanlissa.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • bonapersona - Data from 'The behavioral phenotype of early life adversity'
  • curry - Data from 'Happy to Help?'

On CRAN:

Conda:

cpp

5.23 score 21 scripts 845 downloads 9 exports 78 dependencies

Last updated from:af4be5d63d. Checks:12 OK, 1 FAIL. Indexed: yes.
A new build is currently in progress.

TargetResultTimeFilesSyslog
linux-devel-arm64OK774
linux-devel-x86_64OK815
source / vignettesOK1173
linux-release-arm64OK794
linux-release-x86_64OK781
macos-release-arm64OK744
macos-release-x86_64OK1504
macos-oldrel-arm64OK851
macos-oldrel-x86_64OK1383
windows-develOK1105
windows-releaseOK1017
windows-oldrelOK1130
wasm-releaseFAIL173

Exports:as.stanbrmacheck_workshop_dataI2maxapplot_sensitivitysample_priorshiny_priorsimulate_smd

Dependencies:abindbackportsbase64encBHbslibcachemcallrcheckmateclicommonmarkcpp11descdigestdistributionalfarverfastmapfontawesomefsgenericsggplot2gluegridExtragtablehtmltoolshttpuvinlineisobandjquerylibjsonlitelabelinglaterlatticelifecycleloomagrittrMASSMatrixMatrixModelsmatrixStatsmemoisemimemnormtnumDerivotelpillarpkgbuildpkgconfigposteriorprocessxpromisespsquantregQuickJSRR6rappdirsRColorBrewerRcppRcppEigenRcppParallelrlangrstanrstantoolsS7sassscalesshinysnsourcetoolsSparseMStanHeaderssurvivaltensorAtibbleutf8vctrsviridisLitewithrxtable

Conducting a Bayesian Regularized Meta-analysis

Rendered fromusing-brma.Rmdusingknitr::rmarkdownon May 05 2026.

Last update: 2025-03-29
Started: 2022-04-07

Tutorial: Machine Learning-Informed Meta-Analysis

Rendered frommeta-analysis_tutorial.Rmdusingknitr::rmarkdownon May 05 2026.

Last update: 2025-08-08
Started: 2025-02-27