dgm.no_bias

An example data-generating mechanism to simulate effect sizes without
publication bias.

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.

Franti<c5><a1>ek Barto<c5><a1>

PublicationBiasBenchmark

Benchmark for Publication Bias Correction Methods

František Bartoš

Samuel Pawel

Björn S. Siepe

dgm.no_bias function

<dl><dt>dgm_name</dt>
<dd>DGM name (automatically passed)</dd>
<dt>settings</dt>
<dd>List containing<dl>
<dt>mean_effect</dt>
<dd>Mean effect</dd></dl></dd><dt>heterogeneity</dt>
<dd>Effect heterogeneity</dd><dt>n_studies</dt>
<dd>Number of effect size estimates</dd>
</dl>

Arguments

Normal Unbiased Data-Generating Mechanism — dgm.no_bias

<dl>

<dt>dgm_name</dt>
<dd>DGM name (automatically passed)</dd>


<dt>settings</dt>
<dd>List containing<dl>
<dt>mean_effect</dt>
<dd>Mean effect</dd>

<dt>heterogeneity</dt>
<dd>Effect heterogeneity</dd>

<dt>n_studies</dt>
<dd>Number of effect size estimates</dd>


</dl></dd>

</dl>

dgm.no_bias: Normal Unbiased Data-Generating Mechanism

Description

Usage

Value

Arguments

Details

References

See Also