check_loo.brma

check_loo

Check Pareto k diagnostics for a brma model object and
warn if any values are high.

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

check_loo.brma function

<dl><dt>object</dt>
<dd>a brma model object.</dd>
<dt>unit</dt>
<dd>output/deletion unit. See <code>add_loo</code>.</dd>
<dt>...</dt>
<dd>currently unused.</dd></dl>

Arguments

Check LOO Diagnostics for brma Objects — check_loo.brma

<dl>

<dt>object</dt>
<dd>a brma model object.</dd>


<dt>unit</dt>
<dd>output/deletion unit. See <code>add_loo</code>.</dd>


<dt>...</dt>
<dd>currently unused.</dd>

</dl>

check_loo.brma: Check LOO Diagnostics for brma Objects

Description

Usage

Value

Arguments

Details