Introduction | Setup and Required Libraries | Load Baseline and Mixture Data | Define Analysis Parameters and Output Setup | Supported Classifiers to Evaluate | Run Automated Classifier Comparison | Summarize and Visualize Classifier Performance | Plot Accuracy and Kappa for Each Model | Conclusion | Appendix: Additional Visualizations and Statistics

Introduction | Required packages | Load the data | Write HISEA formatted files (optional) | Analysis parameters | 1) Run with Data frame inputs | 2) Run with HISEA-formatted files | Compare results: table + plot | Visualize bootstrap distributions (if available) | Interpretation & Reasoning (how to read results)

Introduction | Data Simulation of Classification Outputs | Running Classic HISEA Estimators with run_hisea_estimates() | Displaying and Plotting Results | Conclusion

Introduction | Purpose of run_hisea_estimates() | What you need to provide | How it works internally | 1. Linear Discriminant Analysis (LDA) | 2. Random Forest (RF) | 3. Conditional Inference Tree (CTREE) | Conclusion

Overview | Data preparation and import | Assignment cross-validation | Assignment of unknown individuals | From assignment matrices to RHISEA inputs | Mixed-stock estimation with RHISEA

1) Data Preparation | 2) Using run_hisea_all() with Built-in Models | 3) Testing Parameters: phi_method and type in RF Model | 4) Using run_hisea_estimates() with External Models and Comparison | 5) Tuning Built-in Random Forest Models with run_hisea_all() | 6) Tuning External Random Forest Models with run_hisea_estimates() and Comparison

Introduction | 1. Load baseline and mixture data from installed package | 2. Define global parameters for simulation and analysis | 3. Utility functions for reading/writing HISEA files and Fortran outputs | 4. Simulation setup and results containers | 5. Main simulation loop | 6. Aggregate and summarize results | 7. Export results to Excel | 8. Visualization: Compare R vs Fortran estimators | 9. Visualization: Compare R_MASS vs Fortran estimators | 10. Boxplot for all estimators and methods | 11. Diagnostics: Alert if large divergence detected | Conclusion