blup

Computes the estimated true effects (theta) from a
fitted model. These correspond to Best Linear Unbiased Predictions (BLUPs)
or empirical Bayes estimates.

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

blup function

<dl><dt>object</dt>
<dd>a fitted model object</dd>
<dt>...</dt>
<dd>additional arguments passed to methods</dd></dl>

Arguments

Best Linear Unbiased Predictions (BLUPs) — blup

<dl>

<dt>object</dt>
<dd>a fitted model object</dd>


<dt>...</dt>
<dd>additional arguments passed to methods</dd>

</dl>

blup: Best Linear Unbiased Predictions (BLUPs)

Description

Usage

Value

Arguments

See Also