Package: pema 0.1.4

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]

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pema.pdf |pema.html✨
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

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

4.52 score 21 scripts 1.0k downloads 8 exports 81 dependencies

Last updated 10 days agofrom:779bd9daba. Checks:4 OK, 7 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 27 2025
R-4.5-win-x86_64NOTEFeb 27 2025
R-4.5-mac-x86_64OKFeb 27 2025
R-4.5-mac-aarch64OKFeb 27 2025
R-4.5-linux-x86_64OKFeb 27 2025
R-4.4-win-x86_64NOTEFeb 27 2025
R-4.4-mac-x86_64NOTEFeb 27 2025
R-4.4-mac-aarch64NOTEFeb 27 2025
R-4.3-win-x86_64NOTEFeb 27 2025
R-4.3-mac-x86_64NOTEFeb 27 2025
R-4.3-mac-aarch64NOTEFeb 27 2025

Exports:as.stanbrmaI2maxapplot_sensitivitysample_priorshiny_priorsimulate_smd

Dependencies:abindbackportsbase64encBHbslibcachemcallrcheckmateclicolorspacecommonmarkcrayondescdigestdistributionalfansifarverfastmapfontawesomefsgenericsggplot2gluegridExtragtablehtmltoolshttpuvinlineisobandjquerylibjsonlitelabelinglaterlatticelifecycleloomagrittrMASSMatrixMatrixModelsmatrixStatsmemoisemgcvmimemnormtmunsellnlmenumDerivpillarpkgbuildpkgconfigposteriorprocessxpromisespsquantregQuickJSRR6rappdirsRColorBrewerRcppRcppEigenRcppParallelrlangrstanrstantoolssassscalesshinysnsourcetoolsSparseMStanHeaderssurvivaltensorAtibbleutf8vctrsviridisLitewithrxtable

Conducting a Bayesian Regularized Meta-analysis

Rendered fromusing-brma.Rmdusingknitr::rmarkdownon Feb 27 2025.

Last update: 2022-07-16
Started: 2022-04-07