Fixed-effects meta-analyses assume that the effect size
To install the latest stable release of metaBMA
from CRAN, run:
install.packages("metaBMA")
The latest developer version of metaBMA
can be installed from GitHub via:
### install dependencies if necessary:
# install.packages(c("rstan", "rstantools", "bridgesampling",
# "LaplacesDemon", "logspline", "mvtnorm",
# "coda", "knitr", "methods"))
if (!require("devtools"))
install.packages("devtools")
devtools::install_github("danheck/metaBMA")
Note that metaBMA
requires the software Stan.
In case of issues with using Stan, information how to install the R package rstan
is available here:
https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
The most general functions in metaBMA
are meta_bma
and meta_default
, which fit random- and fixed-effects models, compute the inclusion Bayes factor for the presence of an effect and the averaged posterior distribution of the mean effect
Moreover, meta_fixed()
and meta_random()
fit standard meta-analysis models with fixed-effects and random-effects, respectively. The model-specific posteriors for the parameter d can be averaged with bma()
and inclusion Bayes factors be computed with inclusion()
.
The function prior()
facilitates the construction and visual inspection of prior distributions. Sensitivity analysis can be performed with the function meta_sensitivity()
.
For an overview, see: https://danheck.github.io/metaBMA/
If you use metaBMA
, please cite the software as follows:
Heck, D. W., Gronau, Q. F., & Wagenmakers, E.-J. (2019). metaBMA: Bayesian model averaging for random and fixed effects meta-analysis. https://CRAN.R-project.org/package=metaBMA
An (open-access) introduction to Bayesian meta-analysis with model averaging is available at:
Gronau, Q. F., Heck, D. W., Berkhout, S. W., Haaf, J. M., & Wagenmakers, E.-J. (2021). A primer on Bayesian model-averaged meta-analysis. Advances in Methods and Practices in Psychological Science, 4, 1–19. https://doi.org/10.1177/25152459211031256