Johnides2025

<p>The data set contains Cohen's d effect sizes, standard errors, and study labels
from a meta-analysis investigating the extent to which family-based treatments for children
with mental health, physical health, and developmental disorders provide secondary benefits
to the children's siblings and caregivers johnides2025secondaryRoBMA.</p>

datasets

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

Johnides2025 function

<p>A data.frame with 3 columns and 412 observations.</p>

Format

412 effect sizes from a meta-analysis of secondary benefits of family-based treatments
by johnides2025secondary;textualRoBMA — Johnides2025

412 effect sizes from a meta-analysis of secondary benefits of family-based treatments
by johnides2025secondary;textualRoBMA

Johnides2025: 412 effect sizes from a meta-analysis of secondary benefits of family-based treatments by johnides2025secondary;textualRoBMA

Description

Usage

Arguments

Value

Format

References