Benchmark for Publication Bias Correction Methods
Compare method with Multiple Measures for a DGM
Compare method with a Single Measure for a DGM
Compute Multiple Performance measures for a DGM
Compute Performance Measures
Create standardized empty method result for convergence failures
Return Pre-specified DGM Settings
Alinaghi and Reed (2018) Data-Generating Mechanism
Bom and Rachinger (2019) Data-Generating Mechanism
Carter et al. (2019) Data-Generating Mechanism
Default DGM handler
Normal Unbiased Data-Generating Mechanism
DGM Method
Stanley, Doucouliagos, and Ioannidis (2017) Data-Generating Mechanism
Download Datasets/Results/Measures of a DGM
Performance Measures and Monte Carlo Standard Errors
Method Extra Columns
Return Pre-specified Method Settings
AK Method
Default method handler
Endogenous Kink Method
Fixed Effects Meta-Analysis Method
Mean Method
pcurve (P-Curve) Method
PEESE (Precision-Effect Estimate with Standard Errors) Method
PET (Precision-Effect Test) Method
PET-PEESE (Precision-Effect Test and Precision-Effect Estimate with St...
puniform (P-Uniform) Method
Method Method
Random Effects Meta-Analysis Method
Robust Bayesian Meta-Analysis (RoBMA) Method
SM (Selection Models) Method
Trim-and-Fill Meta-Analysis Method
WAAPWLS (Weighted Average of Adequately Powered Studies) Method
Weighted and Iterated Least Squares (WILS) Method
WLS (Weighted Least Squares) Method
Options for the PublicationBiasBenchmark package
PublicationBiasBenchmark: Benchmark for Publication Bias Correction Me...
Retrieve a Pre-Simulated Condition and Repetition From a DGM
Retrieve Pre-Computed Performance measures for a DGM
Retrieve a Pre-Computed Results of a Method Applied to DGM
Generic method function for publication bias correction
Calculate sample variance of generic statistic
Calculate sample variance of squared errors
Calculate sample variance of estimates
Calculate sample variance of CI widths
Simulate From Data-Generating Mechanism
Upload Datasets of a DGM
Validate DGM Settings
Implements a unified interface for benchmarking meta-analytic publication bias correction methods through simulation studies (see Bartoš et al., 2025, <doi:10.48550/arXiv.2510.19489>). It provides 1) predefined data-generating mechanisms from the literature, 2) functions for running meta-analytic methods on simulated data, 3) pre-simulated datasets and pre-computed results for reproducible benchmarks, 4) tools for visualizing and comparing method performance.
Useful links