Overview | Prerequisites | Performance Measures | Method Replacement Strategy | Computing Measures: Step-by-Step Guide | Step 1: Set Up Your Environment | Step 2: Define DGMs and Methods | Step 3: Compute Performance Measures | Understanding the Parameters | Core Parameters | Measure Selection | Column Mapping | Control Parameters | Contributing to the Package

Overview | Prerequisites | Directory Structure | Computing Results: Step-by-Step Guide | Step 1: Set Up Your Environment | Step 2: Define DGMs and Method Information | Step 3: Download Presimulated Datasets | Step 4: Compute Results for Each DGM | Contributing to the Package

Random-Effects Model | Heterogeneity | Funnel Plot | Meta-Regression with a Continuous Moderator | Comparing Models with Bayes Factors | Adding a Categorical Moderator | Model Diagnostics | References

Dataset | Comparing Fixed and Random Effects | From Two Fits to Model Averaging | Adding Effect Averaging | Meta-Regression with Model Averaging | Customizing Prior Components | Other brma Features | References

References

Binomial Random-Effects Model | Default Prior Distributions and the Auxiliary Parameter | Other Inference Helpers | References

Reproducing Informed Bayesian Model-Averaged Meta-Analysis (BMA) | Visualizing the Results | Adjusting for Publication Bias with Robust Bayesian Meta-Analysis | Footnotes | References

Binomial-Normal Bayesian Model-Averaged Meta-Analysis | References

Random-Effects Model | Adding a Scale Predictor | Different Variables in Location and Scale | Scale Predictor Prior Distributions | Other Inference Helpers | References

Multilevel Random-Effects Model | Heterogeneity: $\tau$ and $\rho$ | Other Inference Helpers | References

When to Use Multilevel RoBMA | Loading the Data | Prior Distributions | Fitting the Multilevel Model | Interpreting the Results | Components Summary | Model-Averaged Estimates | Model Types Summary | Visualizing the Weight Function | Comparison with Single-Level RoBMA | References

Rescaling Prior Distributions for Non-Standardized Effect Sizes | Interpreting the Results | Visual Fit Assessment | References

Specifying Prior Distributions | Default Prior Distributions | Basic example | Effect-size measures and their scales | Standardized effect-size inputs | Non-standardized inputs | Meta-regression | Per-moderator overrides | Informed Empirical Prior Distributions | Custom Prior Distributions | Prior Distributions and Bayesian Model Averaging | Publication-Bias Prior Distributions | General Considerations and Reporting | References

Default Bias-Adjustment Prior Distributions | Default Fits | PET | PEESE | Weight Function Selection Model | Asymmetric Sampling Funnels | Flexible Bias Prior Distributions | PET without truncation | PEESE without truncation | Selection-model Weight Prior Distributions | Other Inference Helpers | References

Dataset | Default RoBMA Ensemble | Preset Ensembles | Custom Bias Ensembles | Other Inference Helpers | References

Data | Frequentist Meta-Regression | Bayesian Meta-Regression Specification | Continuous vs. Categorical Moderators and Prior Distributions | Prior Distributions | Bayesian Model-Averaged Meta-Regression | Publication-Bias-Adjusted Model-Averaged Meta-Regression | References

Set-up | Lui (2015) | Data manipulation | Effect size transformations | Re-analysis with random effect meta-analysis | PET-PEESE | Selection models | Robust Bayesian meta-analysis | Specifying Different Prior Distributions | References

Getting Started | Applied Examples | Example 1: Ease-of-Retrieval Effect in the Few/Many Paradigm | Example 2: Social Comparison and Behavior Change | Example 3: ChatGPT and Learning Performance | Example 4: Framing Effects from Many Labs 2 | Conclusions | References

Simulated Data | Model Specification | Maginal Likelihoods | Spike and Slab Priors | References

Bayesian Two-Sample T-Test | Kitchen Rolls Data Set | Implementation with BayesTools | Footnotes | References

Overview | Available Data-Generating Mechanisms | Downloading Precomputed Results | Retrieving Precomputed Results | Retrieving a Specific Repetition | Retrieving All Repetitions for a Condition | Retrieving by Method | Retrieving All Results

Overview | What Are Presimulated Datasets? | Why Use Presimulated Datasets? | Available Data-Generating Mechanisms | Downloading Presimulated Datasets | Retrieving Presimulated Datasets | Retrieving a Single Repetition | Retrieving All Repetitions for a Condition

Overview | File Structure and Naming | 1. Main Method Function: method.{METHOD_NAME}() | Key Requirements for the Main Function: | 2. Settings Function: method_settings.{METHOD_NAME}() | 3. Extra Columns Function: method_extra_columns.{METHOD_NAME}() | Using Your New Method

Overview | File Structure and Naming | 1. Main DGM Function: dgm.{DGM_NAME}() | Key Requirements for the Main Function: | 2. Validation Function: validate_dgm_setting.{DGM_NAME}() | Key Points for Validation: | 3. Conditions Function: dgm_conditions.{DGM_NAME}() | Using Your New DGM

Overview | Available Data-Generating Mechanisms | Downloading Precomputed Measures | Retrieving Precomputed Measures | Retrieving Specific Measures | Retrieving All Measures | Filtering by Method or Setting | Visualizing Precomputed Results