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.
Version: |
0.1.3 |
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, mice, testthat (≥ 3.0.0) |
Published: |
2023-03-16 |
DOI: |
10.32614/CRAN.package.pema |
Author: |
Caspar J van Lissa
[aut, cre],
Sara J van Erp [aut] |
Maintainer: |
Caspar J van Lissa <c.j.vanlissa at tilburguniversity.edu> |
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:
Downloads:
Linking:
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https://CRAN.R-project.org/package=pema
to link to this page.