Rigorously and honestly assess a NEW or proposed covariance / correlation / precision estimator, or a new covariance scoring rule, using precise. Use when someone proposes, asks to evaluate, or wants to compare a covariance methodology. Covers implementing it to the contract, conformance, benchmarking against the registry, out-of-sample validation, and statistically defensible inference.
The English summary is being prepared. Showing the original description.
microprediction/precise☆ 337Updated Oct 6, 2026
Pick which precise covariance estimator to use for a given dataset. Use when you have data X and are unsure which estimator fits its dimension, conditioning, or tail behavior. Wraps precise.suggest() and covariance_features().
The English summary is being prepared. Showing the original description.
microprediction/precise☆ 337Updated Oct 6, 2026
Estimate a covariance / correlation / precision matrix incrementally with precise. Use when data arrives as a stream and you want the matrix updated per observation, or when you want an online (partial_fit) drop-in for sklearn.covariance, which is batch-only.
The English summary is being prepared. Showing the original description.
microprediction/precise☆ 337Updated Oct 6, 2026
Maintain an online covariance over named series whose set changes over time (e.g. assets entering and leaving). Use when observations arrive as dicts keyed by name rather than fixed-length vectors. Wraps precise's keyed / FixedUniverse / DynamicUniverse adapters.
The English summary is being prepared. Showing the original description.
microprediction/precise☆ 337Updated Oct 6, 2026
Score and compare covariance estimates with precise's assessor panel. Use when you need to judge an estimate out-of-sample or rank competing estimators — and especially in high dimensions, where the plain held-out likelihood is misleading.
The English summary is being prepared. Showing the original description.
microprediction/precise☆ 337Updated Oct 6, 2026