effect_sizes

d2logOR

d2OR

r2logOR

r2OR

z2logOR

z2OR

logOR2r

logOR2z

logOR2d

logOR2OR

OR2r

OR2z

OR2logOR

OR2d

Functions for transforming between different
effect size measures.

A framework for estimating ensembles of meta-analytic, meta-regression, and
multilevel models (assuming either presence or absence of the effect, heterogeneity,
publication bias, and moderators). The RoBMA framework uses Bayesian model-averaging to
combine the competing meta-analytic models into a model ensemble, weights
the posterior parameter distributions based on posterior model probabilities
and uses Bayes factors to test for the presence or absence of the
individual components (e.g., effect vs. no effect; Bartoš et al., 2022,
<doi:10.1002/jrsm.1594>; Maier, Bartoš & Wagenmakers, 2022,
<doi:10.1037/met0000405>; Bartoš et al., 2025, <doi:10.1037/met0000737>).
Users can define a wide range of prior distributions for the effect size, heterogeneity,
publication bias (including selection models and PET-PEESE), and moderator components.
The package provides convenient functions for summary, visualizations, and fit diagnostics.

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

RoBMA

Robust Bayesian Meta-Analyses

František Bartoš

Maximilian Maier

Eric-Jan Wagenmakers

Joris Goosen

Matthew Denwood

Martyn Plummer

effect_sizes function

<dl><dt>d</dt>
<dd>Cohen's d.</dd>
<dt>r</dt>
<dd>correlation coefficient.</dd>
<dt>z</dt>
<dd>Fisher's z.</dd>
<dt>logOR</dt>
<dd>log(odds ratios).</dd>
<dt>OR</dt>
<dd>offs ratios.</dd></dl>

Arguments

Effect size transformations — effect_sizes

<dl>

<dt>d</dt>
<dd>Cohen's d.</dd>


<dt>r</dt>
<dd>correlation coefficient.</dd>


<dt>z</dt>
<dd>Fisher's z.</dd>


<dt>logOR</dt>
<dd>log(odds ratios).</dd>


<dt>OR</dt>
<dd>offs ratios.</dd>

</dl>

Effect size transformations

effect_sizes: Effect size transformations

Description

Usage

Arguments

Details

References

See Also