Package: PublicationBiasBenchmark 0.2.1

PublicationBiasBenchmark: Benchmark for Publication Bias Correction Methods

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.

Authors:František Bartoš [aut, cre], Samuel Pawel [aut], Björn S. Siepe [aut], Petr Čala [aut]

PublicationBiasBenchmark_0.2.1.tar.gz
PublicationBiasBenchmark_0.2.1.zip(r-4.7)PublicationBiasBenchmark_0.2.1.zip(r-4.6)PublicationBiasBenchmark_0.2.1.zip(r-4.5)
PublicationBiasBenchmark_0.2.1.tgz(r-4.6-any)PublicationBiasBenchmark_0.2.1.tgz(r-4.5-any)
PublicationBiasBenchmark_0.2.1.tar.gz(r-4.7-any)PublicationBiasBenchmark_0.2.1.tar.gz(r-4.6-any)
PublicationBiasBenchmark_0.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
PublicationBiasBenchmark/json (API)
NEWS

# Install 'PublicationBiasBenchmark' in R:
install.packages('PublicationBiasBenchmark', repos = c('https://fbartos.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/fbartos/publicationbiasbenchmark/issues

Pkgdown/docs site:https://fbartos.github.io

On CRAN:

Conda:

benchmarkmeta-analysispublication-biassimulation

6.48 score 2 stars 12 scripts 581 downloads 52 exports 51 dependencies

Last updated from:5aa4a4ca14. Checks:7 OK, 2 NOTE. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK200
source / vignettesOK232
linux-release-x86_64OK177
macos-release-arm64NOTE103
macos-oldrel-arm64NOTE118
windows-develOK116
windows-releaseOK114
windows-oldrelOK114
wasm-releaseOK129

Exports:biasbias_mcsecompare_measurescompare_single_measurecompute_measurescompute_single_measurecoveragecoverage_mcsecreate_empty_resultdgmdgm_conditionsdownload_dgm_datasetsdownload_dgm_measuresdownload_dgm_resultsempirical_seempirical_se_mcseempirical_varianceempirical_variance_mcseget_dgm_conditionget_method_extra_columnsget_method_settinginterval_scoreinterval_score_mcsemean_ci_widthmean_ci_width_mcsemean_generic_statisticmean_generic_statistic_mcsemeasuremeasure_mcsemethodmethod_extra_columnsmethod_settingsmsemse_mcsenegative_likelihood_rationegative_likelihood_ratio_mcsepositive_likelihood_ratiopositive_likelihood_ratio_mcsepowerpower_mcsePublicationBiasBenchmark.get_optionPublicationBiasBenchmark.optionsrelative_biasrelative_bias_mcseretrieve_dgm_datasetretrieve_dgm_measuresretrieve_dgm_resultsrmsermse_mcserun_methodsimulate_dgmvalidate_dgm_setting

Dependencies:ADGofTestaskpasscachemcliclubSandwichcrulcurldigestfastmapfsgluehttpcodehttrjsonlitelatticelifecyclelmtestmagrittrMAIVEMASSmathjaxrMatrixmemoisemetadatmetaformimenlmenumDerivopensslosfrpbapplypillarpkgconfigpuniformpurrrpwrR6rbibutilsRcppRcppArmadilloRdpackrlangsandwichstringisystibbletriebeardurltoolsutf8vctrszoo

Adding New Data-Generating Mechanisms

Rendered fromAdding_New_DGMs.Rmdusingknitr::rmarkdownon May 24 2026.

Last update: 2025-10-22
Started: 2025-10-01

Adding New Methods

Rendered fromAdding_New_Methods.Rmdusingknitr::rmarkdownon May 24 2026.

Last update: 2025-10-22
Started: 2025-10-01

Computing Method Measures

Rendered fromComputing_Method_Measures.Rmdusingknitr::rmarkdownon May 24 2026.

Last update: 2025-11-07
Started: 2025-10-15

Computing Method Results

Rendered fromComputing_Method_Results.Rmdusingknitr::rmarkdownon May 24 2026.

Last update: 2025-11-07
Started: 2025-10-15

Using Precomputed Measures

Rendered fromUsing_Precomputed_Measures.Rmdusingknitr::rmarkdownon May 24 2026.

Last update: 2025-10-22
Started: 2025-10-15

Using Precomputed Results

Rendered fromUsing_Precomputed_Results.Rmdusingknitr::rmarkdownon May 24 2026.

Last update: 2025-11-26
Started: 2025-10-15

Using Presimulated Datasets

Rendered fromUsing_Presimulated_Datasets.Rmdusingknitr::rmarkdownon May 24 2026.

Last update: 2025-11-07
Started: 2025-10-15

Readme and manuals

Help Manual

Help pageTopics
Compare method with Multiple Measures for a DGMcompare_measures
Compare method with a Single Measure for a DGMcompare_single_measure
Compute Multiple Performance measures for a DGMcompute_measures
Compute Performance Measurescompute_single_measure
Create standardized empty method result for convergence failurescreate_empty_result
DGM Methoddgm
Return Pre-specified DGM Settingsdgm_conditions get_dgm_condition
Alinaghi and Reed (2018) Data-Generating Mechanismdgm.Alinaghi2018
Bom and Rachinger (2019) Data-Generating Mechanismdgm.Bom2019
Carter et al. (2019) Data-Generating Mechanismdgm.Carter2019
Default DGM handlerdgm.default
Normal Unbiased Data-Generating Mechanismdgm.no_bias
Stanley, Doucouliagos, and Ioannidis (2017) Data-Generating Mechanismdgm.Stanley2017
Download Datasets/Results/Measures of a DGMdownload_dgm download_dgm_datasets download_dgm_measures download_dgm_results
Get performance measure functionmeasure
Get performance measure MCSE functionmeasure_mcse
Performance Measures and Monte Carlo Standard Errorsbias bias_mcse coverage coverage_mcse empirical_se empirical_se_mcse empirical_variance empirical_variance_mcse interval_score interval_score_mcse mean_ci_width mean_ci_width_mcse mean_generic_statistic mean_generic_statistic_mcse measures mse mse_mcse negative_likelihood_ratio negative_likelihood_ratio_mcse positive_likelihood_ratio positive_likelihood_ratio_mcse power power_mcse relative_bias relative_bias_mcse rmse rmse_mcse
Method Methodmethod
Method Extra Columnsget_method_extra_columns method_extra_columns method_extra_columns.default
Return Pre-specified Method Settingsget_method_setting method_settings
AK Methodmethod.AK
Default method handlermethod.default
Endogenous Kink Methodmethod.EK
Fixed Effects Meta-Analysis Methodmethod.FMA
MAIVE: Meta-Analysis Instrumental Variable Estimatormethod.MAIVE
Mean Methodmethod.mean
pcurve (P-Curve) Methodmethod.pcurve
PEESE (Precision-Effect Estimate with Standard Errors) Methodmethod.PEESE
PET (Precision-Effect Test) Methodmethod.PET
PET-PEESE (Precision-Effect Test and Precision-Effect Estimate with Standard Errors) Methodmethod.PETPEESE
puniform (P-Uniform) Methodmethod.puniform
Random Effects Meta-Analysis Methodmethod.RMA
Robust Bayesian Meta-Analysis (RoBMA) Methodmethod.RoBMA
SM (Selection Models) Methodmethod.SM
Trim-and-Fill Meta-Analysis Methodmethod.trimfill
WAAPWLS (Weighted Average of Adequately Powered Studies) Methodmethod.WAAPWLS
Weighted and Iterated Least Squares (WILS) Methodmethod.WILS
WLS (Weighted Least Squares) Methodmethod.WLS
Options for the PublicationBiasBenchmark packagePublicationBiasBenchmark.get_option PublicationBiasBenchmark.options PublicationBiasBenchmark_options
Retrieve a Pre-Simulated Condition and Repetition From a DGMretrieve_dgm_dataset
Retrieve Pre-Computed Performance measures for a DGMretrieve_dgm_measures
Retrieve a Pre-Computed Results of a Method Applied to DGMretrieve_dgm_results
Generic method function for publication bias correctionrun_method
Simulate From Data-Generating Mechanismsimulate_dgm
Validate DGM Settingsvalidate_dgm_setting