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pema  

Penalized Meta-Analysis
View on CRAN: Click here


Download and install pema package within the R console
Install from CRAN:
install.packages("pema")

Install from Github:
library("remotes")
install_github("cran/pema")

Install by package version:
library("remotes")
install_version("pema", "0.1.3")



Attach the package and use:
library("pema")
Maintained by
Caspar J van Lissa
[Scholar Profile | Author Map]
First Published: 2021-12-15
Latest Update: 2023-03-16
Description:
Conduct penalized meta-analysis, see Van Lissa, Van Erp, & Clapper (2023) . 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.
How to cite:
Caspar J van Lissa (2021). pema: Penalized Meta-Analysis. R package version 0.1.3, https://cran.r-project.org/web/packages/pema. Accessed 11 Apr. 2025.
Previous versions and publish date:
0.1.0 (2021-12-15 10:20), 0.1.1 (2022-04-25 09:20), 0.1.2 (2022-07-17 22:40), 0.1.3 (2023-03-16 12:40)
Other packages that cited pema R package
View pema citation profile
Other R packages that pema depends, imports, suggests or enhances
Complete documentation for pema
Functions, R codes and Examples using the pema R package
Some associated functions: I2 . as.stan . bonapersona . brma . curry . maxap . pema-package . plot_sensitivity . sample_prior . shiny_prior . simulate_smd . 
Some associated R codes: I2.R . as_stan.R . bonapersona.R . brma.R . curry.R . maxap.R . methods_predict.R . methods_print_summary.R . pema-package.R . plot_prior.R . plot_sensitivity.R . pma_wrappers.R . q.R . shiny_prior.R . simulate_smd.R . standardize.R . stanmodels.R . utils.R . zzz.R .  Full pema package functions and examples
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