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metaBMA
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]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.metaBMA . https://github.com/danheck/metaBMA . https://danheck.github.io/metaBMA/ . metaBMA citation info . metaBMA results . metaBMA.pdf . metaBMA: Meta-Analysis with Bayesian Model Averaging . metaBMA_0.6.9.tar.gz . metaBMA_0.6.9.zip . metaBMA_0.6.9.zip . metaBMA_0.6.9.zip . metaBMA_0.6.9.tgz . metaBMA_0.6.9.tgz . metaBMA_0.6.9.tgz . metaBMA_0.6.9.tgz . metaBMA_0.6.9.tgz . metaBMA_0.6.9.tgz . metaBMA archive . https://CRAN.R-project.org/package=metaBMA .
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. Accessed 05 Apr. 2025.
Previous versions and publish date:
Other packages that cited metaBMA R package
View metaBMA citation profile
Other R packages that metaBMA depends,
imports, suggests or enhances
Complete documentation for metaBMA
Functions, R codes and Examples using
the metaBMA R package
Some associated functions: bma . facial_feedback . inclusion . metaBMA-package . meta_bma . meta_default . meta_fixed . meta_ordered . meta_random . meta_sensitivity . plot.meta_pred . plot.meta_sensitivity . plot.prior . plot_default . plot_forest . plot_posterior . power_pose . predicted_bf . prior . towels . transform_es .
Some associated R codes: bma.R . bounds.R . check_input.R . check_posterior.R . data_facial_feedback.R . data_list.R . data_power_pose.R . data_towels.R . deprecated.R . inclusion.R . integrate_wrapper.R . metaBMA.R . meta_bma.R . meta_bridge_sampling.R . meta_default.R . meta_fixed.R . meta_ordered.R . meta_random.R . meta_sensitivity.R . meta_stan.R . ml_estimates.R . plot_forest.R . plot_posterior.R . plot_prediction.R . plot_prior.R . posterior.R . posterior_fixed.R . posterior_logspline.R . posterior_random.R . predicted_bf.R . print.R . prior.R . prior_functions.R . rstudy.R . stanmodels.R . summary_parameters.R . transform_es.R . zzz.R . Full metaBMA package functions and examples
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