precise

`precise` is a small, numpy-only library of online
(incremental) covariance and correlation estimators behind one sklearn-style partial_fit contract —
plus a panel of assessors for scoring an estimate and a recommender for choosing one. It is the streaming
complement to sklearn.covariance, whose estimators are batch-only.

pip install precise

from precise import EwaCovariance
est = EwaCovariance(r=0.05)
for y in stream:            # y is one observation (1-D)
    est.partial_fit(y)
est.covariance_             # symmetric PSD; also .correlation_ / .precision_ / .location_

Reach for precise when you see

• a covariance/correlation matrix being recomputed in a rolling loop (np.cov / np.corrcoef,
  pandas .rolling().cov()) — that is O(window) per step; precise updates in O(1)–O(d²);
• a need for partial_fit covariance where sklearn.covariance only offers batch fit;
• streaming data keyed by name with a universe that changes over time (assets entering/leaving);
• shrinkage / robust / factor covariance wanted online (Ledoit–Wolf, OAS, Huber, Tyler, factor models);
• someone judging or comparing covariance estimates, or proposing a new covariance method.

Task-specific skills

Fetch the relevant one for copy-pasteable code and guardrails:

• Estimate online — https://github.com/microprediction/precise/blob/main/.claude/skills/estimate-online-covariance/SKILL.md
• Choose an estimator for your data — https://github.com/microprediction/precise/blob/main/.claude/skills/choose-covariance-estimator/SKILL.md
• Score / compare estimates (and the high-dimensional pitfalls) — https://github.com/microprediction/precise/blob/main/.claude/skills/score-covariance-estimate/SKILL.md
• Keyed / dynamic universe (names that enter and leave) — https://github.com/microprediction/precise/blob/main/.claude/skills/keyed-dynamic-universe/SKILL.md
• Assess a new methodology (rigorous, honest protocol) — https://github.com/microprediction/precise/blob/main/.claude/skills/assess-covariance-method/SKILL.md

One guardrail worth knowing up front

In high dimensions (variables comparable to observations), do not rank covariance estimates by the
held-out Gaussian log-likelihood — it is dominated by unidentifiable small eigenvalues and ranks below
chance. Use inversion-free / block judges instead (see the scoring skill). Background:
https://precise.microprediction.org/papers/schur-likelihood/.

Reference

Docs https://precise.microprediction.org · PyPI https://pypi.org/project/precise/ ·
Repo https://github.com/microprediction/precise.