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metaBMA  

Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("metaBMA", "0.6.9")



Attach the package and use:
library("metaBMA")
Maintained by
Daniel W. Heck
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-07-26
Latest Update: 2023-09-13
Description:
Computes the posterior model probabilities for standard meta-analysis models (null model vs. alternative model assuming either fixed- or random-effects, respectively). These posterior probabilities are used to estimate the overall mean effect size as the weighted average of the mean effect size estimates of the random- and fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, & Wagenmakers (2017, ). The user can define a wide range of non-informative or informative priors for the mean effect size and the heterogeneity coefficient. Moreover, using pre-compiled Stan models, meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS) priors can be fitted and tested. This allows to compute Bayes factors and perform Bayesian model averaging across random- and fixed-effects meta-analysis with and without moderators. For a primer on Bayesian model-averaged meta-analysis, see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2021, ).
How to cite:
Daniel W. Heck (2017). metaBMA: Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis. R package version 0.6.9, https://cran.r-project.org/web/packages/metaBMA
Previous versions and publish date:
0.3.8 (2017-07-26 19:43), 0.3.9 (2017-08-04 13:56), 0.6.1 (2019-07-10 09:50), 0.6.2 (2019-09-16 17:40), 0.6.3 (2020-06-02 10:40), 0.6.5 (2020-10-29 09:50), 0.6.6 (2021-01-08 12:20), 0.6.7 (2021-03-17 07:50)
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