Package: pema 0.1.5

Caspar J van Lissa

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
pema_0.1.5.tar.gz(r-4.7-arm64)pema_0.1.5.tar.gz(r-4.7-x86_64)pema_0.1.5.tar.gz(r-4.6-arm64)pema_0.1.5.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
card.svg |card.png
pema/json (API)

# Install 'pema' in R:
install.packages('pema', repos = c('https://cran.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

3.32 score 21 scripts 729 downloads 9 exports 78 dependencies

Last updated from:43c1826ff1. Checks:5 OK, 1 FAIL. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK806
linux-devel-x86_64OK797
source / vignettesOK1163
linux-release-arm64OK808
linux-release-x86_64OK761
wasm-releaseFAIL183

Exports:as.stanbrmacheck_workshop_dataI2maxapplot_sensitivitysample_priorshiny_priorsimulate_smd

Dependencies:abindbackportsbase64encBHbslibcachemcallrcheckmateclicommonmarkcpp11descdigestdistributionalfarverfastmapfontawesomefsgenericsggplot2gluegridExtragtablehtmltoolshttpuvinlineisobandjquerylibjsonlitelabelinglaterlatticelifecycleloomagrittrMASSMatrixMatrixModelsmatrixStatsmemoisemimemnormtnumDerivotelpillarpkgbuildpkgconfigposteriorprocessxpromisespsquantregQuickJSRR6rappdirsRColorBrewerRcppRcppEigenRcppParallelrlangrstanrstantoolsS7sassscalesshinysnsourcetoolsSparseMStanHeaderssurvivaltensorAtibbleutf8vctrsviridisLitewithrxtable

Conducting a Bayesian Regularized Meta-analysis

Rendered fromusing-brma.Rmdusingknitr::rmarkdownon Jun 03 2026.

Last update: 2025-03-30
Started: 2022-04-25

Tutorial: Machine Learning-Informed Meta-Analysis

Rendered frommeta-analysis_tutorial.Rmdusingknitr::rmarkdownon Jun 03 2026.

Last update: 2025-10-06
Started: 2025-03-30