ruvector-math-wasm

Compute Wasserstein distances and Fisher metrics via WebAssembly modules.

1|Updated Feb 8, 2026

Search Tags:#statistics #wasm #webassembly #information geometry #numerical computation #optimal transport

Ready to run

npx skills add https://github.com/ricable/cli-skills-builder --skill ruvector-math-wasm

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ruvector-math-wasm
Source: https://github.com/ricable/cli-skills-builder/tree/main/.claude/skills/ruvector-math-wasm
Command: npx skills add https://github.com/ricable/cli-skills-builder --skill ruvector-math-wasm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides high-performance WebAssembly modules for complex mathematical computations in optimal transport, information geometry, and product manifold operations, directly within browser or Node.js environments.

Core Features & Use Cases

Optimal Transport: Compute Wasserstein distances and Sinkhorn approximations for comparing probability distributions.
Information Geometry: Calculate Fisher-Rao distances and work with information matrices for statistical manifolds.
Product Manifolds: Perform geodesic interpolations and other operations on product spaces.
Use Case: Analyze the similarity between two complex probability distributions using Wasserstein distance or find the shortest path between statistical models on a manifold.

Quick Start

Use the ruvector-math-wasm skill to compute the Wasserstein distance between two probability distributions p and q.

Frequently Asked Questions about ruvector-math-wasm

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I compute Wasserstein distances between probability distributions in a browser environment?▼

You can compute Wasserstein distances in the browser using WebAssembly-compiled mathematical primitives designed for near-native performance. These modules enable direct calculation of optimal transport metrics without server-side processing.

What is information geometry and how do I calculate Fisher metrics in WebAssembly?▼

Information geometry applies differential geometry to probability distributions, and you can calculate Fisher metrics using WebAssembly modules. These modules compute Fisher-Rao distances and information matrices for statistical manifolds directly in browser or Node.js environments.

Can I use WebAssembly for high-performance numerical computation in statistical modeling?▼

Yes, WebAssembly provides near-native performance for high-performance numerical computation in statistical modeling. It compiles mathematical primitives for optimal transport and information geometry to run efficiently in browser or Node.js environments.

Does WebAssembly support geodesic interpolation on product manifolds for machine learning?▼

WebAssembly supports geodesic interpolation on product manifolds by providing compiled mathematical primitives for product manifold operations. This enables complex geometric calculations required in machine learning workflows directly in browser or Node.js environments.

What's the best way to run Sinkhorn approximations for optimal transport in Node.js?▼

The best way to run Sinkhorn approximations in Node.js is using WebAssembly-compiled mathematical primitives tailored for optimal transport. This approach provides near-native performance for comparing probability distributions without leaving the Node.js environment.

When do I need WebAssembly primitives for numerical computation instead of JavaScript?▼

You need WebAssembly primitives for numerical computation when calculating complex metrics like Wasserstein distances or Fisher-Rao distances that require near-native performance. This satisfies requirements for high-performance statistical modeling and machine learning tasks in browser or Node.js environments.