prior_PEESE

Create PEESE publication-bias regression priors.

A framework for Bayesian meta-analysis, including model estimation,
prior specification, model comparison, prediction, summaries, visualizations,
and diagnostics. The package fits single and model-averaged meta-analytic,
meta-regression, multilevel, publication bias adjusted, and generalized linear
mixed models The model-averaged meta-analytic models combine competing models
based on their predictive performance, weight inference by posterior model probabilities,
and test model components using Bayes factors (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 specify flexible prior distributions for effect sizes, heterogeneity,
publication bias (including selection models and PET-PEESE), and moderators.

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

prior_PEESE function

<dl><dt>distribution</dt>
<dd>character. Prior distribution name.</dd>
<dt>parameters</dt>
<dd>list. Distribution parameters.</dd>
<dt>truncation</dt>
<dd>list with <code>lower</code> and <code>upper</code> truncation bounds.</dd>
<dt>prior_weights</dt>
<dd>numeric prior model weight.</dd></dl>

Arguments

PEESE Prior — prior_PEESE

<dl>

<dt>distribution</dt>
<dd>character. Prior distribution name.</dd>


<dt>parameters</dt>
<dd>list. Distribution parameters.</dd>


<dt>truncation</dt>
<dd>list with <code>lower</code> and <code>upper</code> truncation bounds.</dd>


<dt>prior_weights</dt>
<dd>numeric prior model weight.</dd>

</dl>

prior_PEESE: PEESE Prior

Description

Usage

Value

Arguments

Details

See Also