1 Anatomy of the MBH Trifecta | 1.1 mstop — iteration budget | 1.2 nu — shrinkage | 1.3 knots — spline flexibility | 2 Auto-calibration of Huber Delta d | 2.1 Scale invariance | 2.2 Scale-mismatch warning for log-level input | 3 Overriding d for High-Volatility Series | 4 Computational Trade-off Benchmark | Practical guidance | 5 Summary

1. Introduction: Trend-Cycle Decomposition | The outlier problem | 2. Input Agnosticism: Bring Your Own Class | Example: same filter, two input formats | 3. The Filter Arsenal | 3.1 hp_filter() — Sparse Hodrick-Prescott | 3.2 hamilton_filter() — Regression-Based Alternative | 3.3 bhp_filter() — Boosted HP | 4. The Crown Jewel: mbh_filter() | The Problem with Squared Loss | The MBH Solution: Huber Loss + Boosting | Additive Model | Parameters | Quick example | 5. The macrofilter S3 Class | Printing | Accessing components | Inspecting metadata | Plotting cycles side by side | References

1 The End-Point Problem | 2 Expanding-Window Simulation | 3 The Role of boundary.knots | 4 Vintage Fan Chart | 5 The Backward Revision Test | Summary

1. Why quantify trend uncertainty? | 2. The mechanics | 3. Bands for every filter | Reading the end-point fan | A note on the Hamilton band | 4. References