prior_informed

Create empirical informed prior distributions.

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_informed function

<dl><dt>name</dt>
<dd>character. Informed prior name. Supported examples include
<code>"van Erp"</code>, <code>"Oosterwijk"</code>, <code>"Cochrane"</code>, and medicine
subfields from <code>BayesTools::prior_informed_medicine_names</code>.</dd>
<dt>parameter</dt>
<dd>character. Parameter subset. Required for medicine priors
and not needed for psychology priors.</dd>
<dt>type</dt>
<dd>character. Effect-size type. Medicine priors use values such as
<code>"smd"</code>, <code>"logOR"</code>, <code>"logRR"</code>, <code>"RD"</code>, and
<code>"logHR"</code>.</dd></dl>

Arguments

Informed Prior — prior_informed

<dl>

<dt>name</dt>
<dd>character. Informed prior name. Supported examples include
<code>"van Erp"</code>, <code>"Oosterwijk"</code>, <code>"Cochrane"</code>, and medicine
subfields from <code>BayesTools::prior_informed_medicine_names</code>.</dd>


<dt>parameter</dt>
<dd>character. Parameter subset. Required for medicine priors
and not needed for psychology priors.</dd>


<dt>type</dt>
<dd>character. Effect-size type. Medicine priors use values such as
<code>"smd"</code>, <code>"logOR"</code>, <code>"logRR"</code>, <code>"RD"</code>, and
<code>"logHR"</code>.</dd>

</dl>

prior_informed: Informed Prior

Description

Usage

Value

Arguments

Details

References