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: |
0.1.2 |
| 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: |
2022-07-17 |
| 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:
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