https://github.com/jbrown1618/vector

A linear algebra library for TypeScript and JavaScript
https://github.com/jbrown1618/vector

cholesky-decomposition gauss-jordan least-squares linear-algebra linear-algebra-library linear-regression lu-decomposition matrix matrix-calculations matrix-factorization matrix-multiplication qr-decomposition singular-value-decomposition svd typescript vector vector-math

Last synced: 21 days ago
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A linear algebra library for TypeScript and JavaScript

• Host: GitHub
• URL: https://github.com/jbrown1618/vector
• Owner: jbrown1618
• License: mit
• Created: 2018-06-22T01:38:33.000Z (over 7 years ago)
• Default Branch: main
• Last Pushed: 2025-12-03T03:20:50.000Z (2 months ago)
• Last Synced: 2025-12-06T04:32:47.464Z (2 months ago)
• Topics: cholesky-decomposition, gauss-jordan, least-squares, linear-algebra, linear-algebra-library, linear-regression, lu-decomposition, matrix, matrix-calculations, matrix-factorization, matrix-multiplication, qr-decomposition, singular-value-decomposition, svd, typescript, vector, vector-math
• Language: TypeScript
• Homepage:
• Size: 2.97 MB
• Stars: 14
• Watchers: 1
• Forks: 1
• Open Issues: 17
• Metadata Files:
  • Readme: README.md
  • Contributing: docs/CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: docs/CODE_OF_CONDUCT.md

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README

          
# Vector

A linear algebra library written in TypeScript that focuses on generality, extensibility, and ease of use.

## Features

- Core:

  - Basic manipulation of vectors and matrices

  - Out-of-the-box support for `number` and `ComplexNumber`

  - Extensible to support general scalar types

- Matrix Operations:

  - Matrix determinants

  - Matrix exponentials

  - Elementary row operations

  - Gauss-Jordan elimination

  - Eigenvalue / Eigenvector finding

- Matrix Factorizations:

  - Cholesky Decomposition

  - LU Decomposition

  - QR Decomposition

  - Singular Value Decomposition

- Applications:

  - Calculus: Differentiation via finite differences

  - Statistics: Least-Squares Regression for arbitrary model functions

  - Statistics: Principal component analysis

- Machine learning models

  - Regularized linear regression

  - Logistic Regression

  - Support Vector Machines

- And more to come!

## Using Vector

See our [Usage Guide](./docs/USAGE.md) for more on how to use **Vector** in your project.

## Contributing to Vector

See our [Contribution Guide](./docs/CONTRIBUTING.md) for contribution guidelines and coding standards.

## Documentation

See the [API Documentation](./docs/api/vector.md)