Package: tidyabc 0.0.1

Introduction | Step 1: Define the Model, Data, and Priors (Recap) | Step 2: Run ABC-Adaptive | Step 3: Visualize the Results | Step 4: Diagnose Convergence | Step 5: Visualize Parameter Evolution | Step 6: Check Parameter Correlations | Conclusion

Introduction | Step 1: Define the Simulation Function | Step 2: Define the Scoring Function | Step 3: Generate Observed Data (Ground Truth) | Step 4: Visualize the Observed Data | Step 5: Define Prior Distributions | Step 6: Run ABC Rejection Sampling | Step 7: Plot the Results | Conclusion

Introduction | Reparameterised statistical distributions | Core Functions | Selected Examples | List of Additional Families | Specialized Random Number Generators | Distribution family S3 class: | Creating dist_fns Objects | From Standard Families | Creating Multiple Distributions with pmap_dist_fns | Manipulating dist_fns Objects in Data Frames | Applying Transformations: Truncation Example | Combining dist_fns: Mixture Distributions | Fitting Empirical Distributions | From Quantiles using empirical_cdf | Ensemble of Quantile-Based Distributions as a Mixture | From Samples using empirical_data | Weighted Sample Fitting | Using wquantile for Quantiles from Weighted Data | Conclusion

Introduction | Step 1: Define the Model and Data (Recap) | Step 2: Inform Scoring Weights (Optional but Recommended) | Step 3: Run ABC-SMC | Step 4: Visualize the Results | Step 5: Diagnose Convergence | Step 6: Visualize Parameter Evolution | Step 7: Check Parameter Correlations | Conclusion

Introduction | 1. Writing the Simulation Function (sim_fn) | Inputs | Output | Example | 2. Writing the Scoring Function (scorer_fn) | Combining Scorer Outputs and Comparison to Observed Scores | Score Weights (scoreweights) | 3. Debugging and Parallelisation | 4. The Convergence Function (converged_fn) | Inputs to converged_fn | Output of converged_fn | The default_termination_fn | 5. Writing a Custom Convergence Function | Structure | Example: Convergence Based on ESS and Distance Reduction | Example: Convergence Based on Parameter Variance | Key Considerations for Custom Functions | Conclusion