Conduct penalized meta-analysis (“pema”) 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 overfitted. Penalized meta-regression is useful in these cases, because it shrinks the regression slopes of irrelevant moderators towards zero.
Use CRAN to
install the latest release of pema:
install.packages("pema")Alternatively, use R-universe to install the
development version of pema by running the following
code:
options(repos = c(
cjvanlissa = 'https://cjvanlissa.r-universe.dev',
CRAN = 'https://cloud.r-project.org'))
install.packages('pema')The pema validation paper is currently in preprint
stage. The code and results of the validation study are publicly
available. You can cite pema using the following citation
(please use the same citation for either the package, or the paper);
consider updating that citation once the preprint is published:
Van Lissa, C. J., & van Erp, S. (2021, December 9). Select relevant moderators using Bayesian regularized meta-regression. Retrieved from psyarxiv.com/6phs5
This repository contains the source code for the R-package called
pema.
We are always eager to receive user feedback and contributions to help us improve both the workflow and the software. Major contributions warrant coauthorship to the package. Please contact the lead author at c.j.vanlissa@uu.nl, or:
By participating in this project, you agree to abide by the Contributor Code of Conduct
v2.0. Contributions to the package must adhere to the tidyverse style guide. When
contributing code, please add tests for that contribution to the
tests/testthat folder, and ensure that these tests pass in
the GitHub
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