How the response is interpreted | Binary classification | Multi-category classification | Notes on the classification prior

1. Loading the Package and Data | 2. Preparing the Data | 3. Training the Model | 4. Making Predictions and Evaluating Performance | 5. Visualising the Results

1. Generating a synthetic dataset | 2. Fitting the AddiVortes Model | 3. Out-of-Sample Prediction | 4. Visualising Prediction Performance | 5. Credible Intervals vs. Prediction Intervals

1. What is One-Hot Encoding? | 2. The catScaling Parameter | 3. Eskin Distance (cat.onehot = FALSE) | 4. Choosing Between One-Hot and Eskin | 5. A Synthetic Example | 6. Inspecting the Encoding | 7. Fitting the Model (One-Hot) | 8. Making Predictions | 9. Handling Unseen Category Levels | 10. Effect of catScaling | 11. Comparing One-Hot and Eskin Distances | 12. Summary of Key Points | References

1. What is Spherical Data? | 2. Coordinate Convention | 3. Generating Synthetic Spherical Data | 4. Fitting the Spherical Model | 5. Out-of-Sample Evaluation | 6. Visualising Predictions | 7. Multiple Spherical Covariates | 8. Summary of Key Points