How do I install stdlib for a browser-based project using Webpack?

Install individual packages via npm (e.g., `npm install @stdlib/math-base-special-sin`) and import them in your code; the README confirms compatibility with bundlers like Webpack, and you can use ES Module builds from dedicated branches for direct browser use.

What's the difference between stdlib and Math.js?

stdlib is more comprehensive and modular, with a focus on scientific computing, native performance optimizations, and TypeScript support, while Math.js is simpler, offering expression parsing and basic math operations with an easier learning curve. stdlib is better for advanced numerical work, but Math.js may suffice for straightforward calculations.

Can I use stdlib in Deno or Observable notebooks?

Yes, stdlib provides pre-built Deno imports from dedicated branches and UMD bundles for CDN usage in Observable, as mentioned in the README's environment builds section, making it versatile across different JavaScript runtimes.

Does stdlib include machine learning algorithms?

No, stdlib focuses on foundational numerical and statistical functions, such as probability distributions and linear algebra via BLAS, but doesn't provide high-level ML algorithms; you'd need to combine it with other libraries for tasks like neural networks.

How to generate reproducible random numbers with stdlib?

Use one of the 40+ seedable pseudorandom number generators (PRNGs) like `@stdlib/random/base/mt19937`, which allow setting seeds for deterministic output, as highlighted in the features list for reliable simulations.

Is stdlib suitable for real-time applications in the browser?

It can be, but performance depends on the use case; native add-ons aren't available in browsers, so JavaScript fallbacks are used, which might be slower for intensive computations compared to server-side Node.js with BLAS integration.

stdlib — Numerical Standard Library for JS/TS

What is stdlib?

stdlib is a standard library for JavaScript and TypeScript that provides a comprehensive suite of tools for numerical and scientific computation. It includes hundreds of high-performance functions for mathematics, statistics, data processing, and visualization, enabling developers to perform advanced computations directly in browser and Node.js environments. The project aims to make the web a preferred platform for numerical computing by offering robust, well-tested, and modular APIs.

Target Audience

JavaScript and TypeScript developers working on data science, numerical analysis, scientific computing, or any application requiring advanced mathematical and statistical operations. It is also suitable for educators and researchers looking to leverage web technologies for computational tasks.

Value Proposition

Developers choose stdlib for its extensive, modular, and high-quality numerical libraries that are specifically designed for JavaScript/TypeScript ecosystems. Its fully decomposable architecture allows for precise customization, and its rigorous testing and documentation ensure reliability. Unlike piecing together disparate packages, stdlib offers a cohesive, standardized suite of tools with native performance optimizations and TypeScript support.

Overview

✨ The fundamental numerical library for JavaScript and TypeScript. ✨

Use Cases

Best For

Performing statistical analysis and probability modeling in Node.js applications
Building data visualization tools with integrated plotting capabilities
Developing scientific simulations or numerical algorithms for the web
Creating educational platforms that teach mathematics or data science concepts
Implementing machine learning preprocessing or mathematical utilities in JavaScript
Vendoring optimized numerical libraries for server-side applications

Not Ideal For

Projects requiring only basic arithmetic or simple math functions where a lighter library like Math.js would be more efficient.
Rapid prototyping in data science workflows that rely on integrated environments like Jupyter notebooks with Python/R kernels.
Applications with strict bundle size constraints where installing individual stdlib packages still adds significant overhead compared to handwritten utilities.
Teams without prior experience in numerical computing who may find the advanced APIs and terminology challenging to navigate.

Pros & Cons

Pros

Vast Numerical Library

Offers over 150 special math functions, 35+ probability distributions, and 200+ utilities, providing comprehensive coverage for scientific computing tasks directly in JavaScript/TypeScript.

Modular Architecture

Fully decomposable design allows installing individual packages or namespaces, minimizing bundle sizes and enabling precise customization without bloating dependencies.

TypeScript and Performance

Every function includes TypeScript declarations for type safety, and native add-ons for BLAS libraries with JavaScript fallbacks ensure high-performance computations where needed.

Rigorous Quality Assurance

Emphasizes thorough testing, documentation, and code coverage, with a REPL environment for interactive learning, ensuring reliability for production use.

Cons

Installation Complexity

The README outlines multiple installation methods (e.g., individual packages, namespaces, custom bundles) which can be confusing, and setting up native dependencies for optimal performance requires additional tools like gcc and gfortran.

Heavy Footprint

Installing the complete library (@stdlib/stdlib) is substantial and may include unused functionality, leading to slower installation times and larger node_modules, which isn't ideal for lightweight projects.

Ecosystem Immaturity

While comprehensive, it lacks the deep integration with popular data science tools (e.g., pandas, scikit-learn) found in Python or R ecosystems, making it less suitable for hybrid workflows.

Open Source Alternative To

stdlib is an open-source alternative to the following products:

Matlab

MATLAB is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks for matrix manipulations, algorithm development, and data analysis.

SciPy

SciPy is an open-source Python library used for scientific and technical computing, providing modules for optimization, linear algebra, integration, interpolation, and other mathematical tasks.

Julia

Julia is a high-level, high-performance programming language for technical computing, with syntax that is familiar to users of other technical computing environments.

R

R is a programming language and free software environment for statistical computing and graphics, widely used among statisticians and data miners.

N

NumPy

stdlib

What is stdlib?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Open Source Alternative To

Frequently Asked Questions

Related Projects

Found a gem we're missing?

stdlib

What is stdlib?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Open Source Alternative To

Frequently Asked Questions

Related Projects

Found a gem we're missing?