RoBMA provides Bayesian meta-analysis, meta-regression, multilevel
meta-analysis, model averaging, and publication-bias adjustment. The main
user-facing fitters are RoBMA, BMA,
brma, brma.glmm, bselmodel,
bPET, and bPEESE.
User guide
See Bartoš et al. (2023)
,
Maier et al. (2023)
,
Bartoš et al. (2022)
, and
Bartoš et al. (2026)
for the RoBMA methodology.
Use vignette(package = "RoBMA") to list installed vignettes.
References
Bartoš F, Maier M, Quintana DS, Wagenmakers E (2022).
“Adjusting for publication bias in JASP and R — Selection models, PET-PEESE, and robust Bayesian meta-analysis.”
Advances in Methods and Practices in Psychological Science, 5(3), 1–19.
doi:10.1177/25152459221109259
.
Bartoš F, Maier M, Wagenmakers E (2026).
“Robust Bayesian multilevel meta-analysis: Adjusting for publication bias in the presence of dependent effect sizes.”
Behavior Research Methods.
doi:10.31234/osf.io/9tgp2_v2
.
Preprint available at https://doi.org/10.31234/osf.io/9tgp2_v2.
Bartoš F, Maier M, Wagenmakers E, Doucouliagos H, Stanley TD (2023).
“Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods.”
Research Synthesis Methods, 14(1), 99–116.
doi:10.1002/jrsm.1594
.
Maier M, Bartoš F, Wagenmakers E (2023).
“Robust Bayesian Meta-Analysis: Addressing publication bias with model-averaging.”
Psychological Methods, 28(1), 107–122.
doi:10.1037/met0000405
.
Author
Frantisek Bartos f.bartos96@gmail.com