---
title: "Covariance Estimation and Graphical Lasso in JavaScript"
description: "Estimate empirical, shrunk, Ledoit-Wolf, OAS, and sparse inverse covariance matrices in JavaScript or TypeScript."
canonical_url: "https://ml.kanaries.net/docs/apis/covariance/covarianceEstimators"
markdown_url: "https://ml.kanaries.net/docs/apis/covariance/covarianceEstimators.md"
---
# Covariance estimation in JavaScript

## Algorithm overview

Covariance matrices describe joint feature variation and power Mahalanobis distance, Gaussian scoring, and graphical models. Shrinkage improves conditioning when data is limited; graphical lasso produces a sparse precision matrix.

## JavaScript implementation

Choose `EmpiricalCovariance` for maximum-likelihood covariance, `ShrunkCovariance` for a fixed shrinkage amount, `LedoitWolf` or `OAS` for data-driven shrinkage, and `GraphicalLasso` for L1-regularized conditional dependence.

## Quick start

```ts
import { Covariance } from '@kanaries/ml';

const X = [[0, 1], [1, 0], [2, 1], [3, 2]];
const model = new Covariance.OAS();
model.fit(X);
console.log(model.mahalanobis([[1, 1]]));
```

## Detailed API reference

Every estimator exposes `location`, `covariance`, and `precision`. The adaptive shrinkage classes expose `shrinkageValue`; `GraphicalLasso` uses block coordinate descent, accepts `alpha`, `maxIter`, `tol`, `enetTol`, and `assumeCentered`, and exposes `nIter`.