---
title: "MinCovDet (FAST-MCD) in JavaScript with @kanaries/ml"
description: "Compute robust multivariate location, covariance, support masks, and Mahalanobis distances with the MinCovDet JavaScript and TypeScript implementation in @kanaries/ml."
canonical_url: "https://ml.kanaries.net/docs/apis/covariance/minCovDet"
markdown_url: "https://ml.kanaries.net/docs/apis/covariance/minCovDet.md"
---
# MinCovDet (FAST-MCD) in JavaScript

## Algorithm overview

Minimum Covariance Determinant finds a subset whose covariance determinant is small, then reweights observations using robust Mahalanobis distances. This makes location and covariance estimates resistant to multivariate outliers. The implementation uses FAST-MCD elemental starts, C-steps, consistency correction, and chi-square reweighting.

## JavaScript implementation

`Covariance.MinCovDet` runs in browsers and Node.js without a Python service. It is appropriate for moderate tabular datasets; repeated matrix inversions make it substantially more expensive than empirical covariance.

## Quick start example

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

const X = [[0, 0], [.1, .2], [.2, .1], [-.1, 0], [8, 8]];
const robust = new Covariance.MinCovDet({ supportFraction: .8, randomState: 42 });
robust.fit(X);

console.log(robust.location, robust.covariance);
console.log(robust.support, robust.mahalanobis(X));
```

## Detailed API reference

```ts
new Covariance.MinCovDet({
  supportFraction?: number; // (0, 1]; default uses sklearn's h formula
  randomState?: number;
  assumeCentered?: boolean; // default false
})
```

- `fit(X): void` estimates raw and reweighted robust covariance.
- `mahalanobis(X): number[]` returns squared robust Mahalanobis distances.
- `location`, `covariance`, `precision`, and `support` expose reweighted results.
- `rawLocation`, `rawCovariance`, and `rawSupport` expose the pre-reweighting FAST-MCD result.