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metaBMA

Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis

https://github.com/danheck/metabma

Science Score: 23.0%

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    DOI references
    Found 4 DOI reference(s) in README
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    2 of 5 committers (40.0%) from academic institutions
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    Low similarity (14.8%) to scientific vocabulary

Keywords

bayes bayes-factor bayesian-inference evidence-synthesis meta-analysis model-averaging r stan
Last synced: 6 months ago · JSON representation

Repository

Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis

Basic Info
Statistics
  • Stars: 30
  • Watchers: 3
  • Forks: 2
  • Open Issues: 2
  • Releases: 2
Topics
bayes bayes-factor bayesian-inference evidence-synthesis meta-analysis model-averaging r stan
Created about 9 years ago · Last pushed 7 months ago
Metadata Files
Readme

README.md

CRAN_Status_Badge Workflow Licence monthly downloads total downloads

metaBMA

Fixed-effects meta-analyses assume that the effect size $d$ is identical in all studies. In contrast, random-effects meta-analyses assume that effects vary according to a normal distribution with mean $d$ and standard deviation $\tau$. When assuming prior distributions for $d$ and $\tau$, both models can be compared using Bayes factors. Alternatively, posterior model probabilities can be used to compare the evidence for or against an effect (i.e., whether $d = 0$) and the evidence for or against random effects (i.e., whether $\tau = 0$). By using Bayesian model averaging (BMA), both types of tests can be performed by marginalizing over the other question. Most importantly, this allows to test whether an effect exists while accounting for uncertainty whether study heterogeneity exists or not.

Installing metaBMA

To install the latest stable release of metaBMA from CRAN, run:

r install.packages("metaBMA")

The latest developer version of metaBMA can be installed from GitHub via:

```r

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

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 $d$ (which accounts for uncertainty regarding study heterogeneity).

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/

References

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

The R package’s functionality has also been implemented in the software JASP:

Berkhout, S. W., Haaf, J. M., Gronau, Q. F., Heck, D. W., & Wagenmakers, E. (2024). A tutorial on Bayesian model-averaged meta-analysis in JASP. Behavior Research Methods, 56, 1260–1282. https://doi.org/10.3758/s13428-023-02093-6

Owner

  • Name: Daniel Heck
  • Login: danheck
  • Kind: user
  • Location: Germany
  • Company: Philipps-Universität Marburg

GitHub Events

Total
  • Watch event: 1
  • Push event: 3
Last Year
  • Watch event: 1
  • Push event: 3

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 226
  • Total Committers: 5
  • Avg Commits per committer: 45.2
  • Development Distribution Score (DDS): 0.053
Past Year
  • Commits: 1
  • Committers: 1
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Daniel Heck d****k@w****e 214
Indrajeet Patil i****8@g****m 5
Daniel Heck d****k@u****e 4
Andrew Johnson a****n@a****m 2
Daniel Heck d****l@p****E 1
Committer Domains (Top 20 + Academic)
pc04798.psychologie.uni-marburg.de: 1 arjohnsonau.com: 1 uni-marburg.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 10
  • Total pull requests: 15
  • Average time to close issues: 4 months
  • Average time to close pull requests: about 10 hours
  • Total issue authors: 6
  • Total pull request authors: 3
  • Average comments per issue: 4.2
  • Average comments per pull request: 0.93
  • Merged pull requests: 15
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • IndrajeetPatil (4)
  • DanielEWeeks (2)
  • pxtm (1)
  • CorradoLanera (1)
  • tressoldi (1)
  • jcaude (1)
Pull Request Authors
  • danheck (9)
  • IndrajeetPatil (5)
  • andrjohns (1)
Top Labels
Issue Labels
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Packages

  • Total packages: 2
  • Total downloads:
    • cran 3,408 last-month
  • Total docker downloads: 1,495
  • Total dependent packages: 9
    (may contain duplicates)
  • Total dependent repositories: 19
    (may contain duplicates)
  • Total versions: 15
  • Total maintainers: 1
cran.r-project.org: metaBMA

Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis

  • Versions: 9
  • Dependent Packages: 5
  • Dependent Repositories: 19
  • Downloads: 3,408 Last month
  • Docker Downloads: 1,495
Rankings
Dependent repos count: 6.5%
Dependent packages count: 8.2%
Downloads: 8.2%
Stargazers count: 10.1%
Average: 11.7%
Docker downloads count: 16.0%
Forks count: 21.0%
Maintainers (1)
daniel.heck@uni-marburg.de
Last synced: 6 months ago
conda-forge.org: r-metabma
  • Versions: 6
  • Dependent Packages: 4
  • Dependent Repositories: 0
Rankings
Dependent packages count: 12.4%
Dependent repos count: 34.0%
Average: 37.3%
Stargazers count: 45.3%
Forks count: 57.4%
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 4.0.0 depends
  • Rcpp >= 1.0.0 depends
  • methods * depends
  • LaplacesDemon * imports
  • RcppParallel >= 5.0.1 imports
  • bridgesampling * imports
  • coda * imports
  • logspline * imports
  • mvtnorm * imports
  • rstan >= 2.18.1 imports
  • rstantools >= 2.1.1 imports
  • knitr * suggests
  • rmarkdown * suggests
  • spelling * suggests
  • testthat * suggests