robustcov documentation ======================= ``robustcov`` is an experimental robust covariance, heavy-tail scatter, anomaly diagnostics, and benchmark-gallery package with a C++/pybind core. The project is organized around two reader-friendly entry points: .. raw:: html

Start from your application

Topic-based gallery: finance/risk, fraud/security, sensors/quality, biomedical/images/embeddings, real ML datasets, and preprocessing.

Start from the evidence

Small-sample heavy-tail ranking, speed comparisons, OpenMP scaling, anomaly baselines, and hard contamination scenarios.

Math
and API

Understand the estimators

FastMCD, Tyler shape, Regularized Tyler, Student-t scatter, Cauchy scatter, diagnostics, and references.

Core ideas ---------- * ``FastMCD`` gives efficient classical robust covariance for separable contamination when ``n`` is comfortably larger than ``p``. * ``RegularizedCauchy`` and ``StudentTScatter`` target small-sample, high-dimensional, heavy-tailed covariance problems. * Robust-distance diagnostics turn fitted estimators into interpretable anomaly scores, profiles, QQ plots, and reports. * Optional OpenMP acceleration improves larger workloads and benchmark/report generation. .. toctree:: :maxdepth: 2 :caption: User guide installation quickstart estimator_guide use_case_gallery benchmark_gallery algorithms diagnostics openmp faq .. toctree:: :maxdepth: 2 :caption: Reference and evidence api api_stability robust_statistics_background external_results_gallery references Why not focus on MVE? --------------------- Minimum-volume ellipsoid estimators are historically important, but the benchmark evidence in this project points to a stronger niche: efficient MCD for separable outliers and regularized heavy-tail scatter for small samples. MVE may become an experimental add-on later, but it is not currently the core differentiator. .. toctree:: :maxdepth: 1 :caption: Extended material :hidden: notebooks kaggle_roadmap kaggle_examples external_demo_workflow release_readiness