# Guide to RoBMA Vignettes

The `RoBMA` package provides a comprehensive set of vignettes to help users navigate different aspects of Robust Bayesian Meta-Analysis. This guide outlines the available vignettes and their specific focus to help you find the relevant information for your analysis.

## Introductory Vignettes

### Tutorial: Adjusting for Publication Bias in JASP and R

This is the main introduction to the RoBMA framework. It covers the basics of adjusting for publication bias using selection models, PET-PEESE, and Robust Bayesian Meta-Analysis. It is the recommended starting point for new users.

### Reproducing Bayesian Model-Averaged Meta-Analysis

This vignette demonstrates how to perform a classic Bayesian model-averaged meta-analysis. It focuses on reproducing standard BMA results and understanding the core components of the method.

## Advanced Modeling Features

### Robust Bayesian Model-Averaged Meta-Regression

Learn how to incorporate moderators into your meta-analysis using `RoBMA.reg()`. This vignette explains how to fit meta-regression models to account for heterogeneity explained by study-level covariates.

### Multilevel Robust Bayesian Meta-Analysis

This vignette demonstrates how to perform multilevel meta-analysis to account for dependent effect sizes (e.g., multiple estimates from the same study). It uses the spike-and-slab algorithm (`algorithm = "ss"`) to efficiently estimate models with within-study and between-study heterogeneity while adjusting for publication bias.

### Multilevel Robust Bayesian Model-Averaged Meta-Regression

This vignette demonstrates how to perform multilevel meta-regression. In addition, it illustrates how to rescale default prior distributions to work with non-standardized effect sizes.

### Z-Curve Publication Bias Diagnostics

This vignette details the use of meta-analytic z-curves for diagnosing publication bias. It explains how to interpret z-curve plots and statistics provided by the package.

## Specialized Applications

### Informed Bayesian Model-Averaged Meta-Analysis in Medicine

This vignette focuses on applying RoBMA in medical contexts. It discusses the use of informed priors tailored for medical research questions and continuous outcomes.

### Informed Bayesian Model-Averaged Meta-Analysis with Binary Outcomes

Similar to the Medicine BMA vignette, but specifically for binary outcomes. It covers the `BiBMA` models (Binomial-Normal) and appropriate prior settings for medical meta-analysis with binary data.

## Customization and Performance

### Fitting Custom Meta-Analytic Ensembles

For advanced users who need to go beyond the default model ensembles. This vignette demonstrates how to customize the ensemble of models, including specifying custom priors and model combinations.

### Fast Robust Bayesian Meta-Analysis via Spike and Slab Algorithm

For computationally intensive problems or quick approximations, the “spike-and-slab” algorithm (`algorithm = "ss"`) can be used. This vignette explains how to use this faster alternative to the default bridge sampling approach.

### Hierarchical Bayesian Model-Averaged Meta-Analysis

This vignette introduces multilevel models. It shows how to handle dependencies in the data (e.g., multiple effect sizes from the same study) using the `study_ids` argument to specify a hierarchical structure. Note that this vignette relies on multivariate parameterization that is relevant only for the bridge sampling algorithm. However, it is still helpful for describing the parameterization.