Estimate Online Covariance

by microprediction

Estimate Online Covariance is a Data skill for Claude Code, published by microprediction in precise.

333 stars58 forkson microprediction/preciseAdded 2026/08/12Repository updated 2026/06/24

View source on GitHub Download repo ZIP

covariance-estimationcovariance-matrixinvestment-portfolioportfolio

Install in seconds

Install Estimate Online Covariance

Copy Estimate Online Covariance into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/microprediction/precise/tree/main/.claude/skills/estimate-online-covariance ~/.claude/skills/estimate-online-covariance

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/microprediction/precise.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: .claude/skills/estimate-online-covariance/SKILL.md in microprediction/precise
Installs to: ~/.claude/skills/estimate-online-covariance
Collection: One of 6 skills cataloged from this repository
Category: Data — 668 skills

What Estimate Online Covariance does

Estimate Online Covariance updates covariance, correlation, or precision matrices one observation at a time with precise. Use it for streaming data or as a partial_fit alternative to batch-only sklearn.covariance estimators.

Estimate Online Covariance is cataloged under Data on DirSkills. Estimate Online Covariance comes from a repository tagged covariance-estimation, covariance-matrix, investment-portfolio and portfolio.

Documentation

README

Excerpt

Estimate online covariance with precise

precise provides sklearn-style estimators with a single partial_fit contract. Pure numpy.

pip install precise

The pattern

import numpy as np
from precise import EwaCovariance        # exponentially weighted; recency-biased

est = EwaCovariance(r=0.05)              # r in (0,1]; larger = faster forgetting
for y in stream:                         # y is a 1-D array (one observation)
    est.partial_fit(y)

est.covariance_     # (d, d) ndarray, symmetric and PSD by construction
est.correlation_    # unit-diagonal correlation
est.precision_      # inverse covariance (when well-conditioned)
est.location_       # running mean
est.n_samples_      # observations seen

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Estimate Online Covariance

What else does microprediction publish alongside Estimate Online Covariance?
Estimate Online Covariance is one of 6 skills that DirSkills catalogs from microprediction/precise, the repository it ships in. Its siblings there include Choose Covariance Estimator, Covariance Assessment and Covariance Scoring. Each one is a separate skill with its own page in this directory, installs the same way Estimate Online Covariance does, and is maintained by microprediction in that same repository. The rest of the collection is listed on the microprediction/precise page.
How does Estimate Online Covariance compare to other Data skills?
Estimate Online Covariance ranks #611 by stars among the 668 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Estimate Online Covariance against them. Open each page to compare what they document and how they install.