statsrust - Statistical Analysis Library for Rust

statsrust is a high-performance statistical analysis library for Rust developers, designed with emphasis on numerical stability, mathematical correctness, and user-friendly API.

Features

• Descriptive Statistics: Comprehensive implementation of measures of central tendency, position, and variability
• Kernel Density Estimation: Multiple kernel functions with efficient implementation
• Normal Distribution Model: Complete algebraic operations on normal distributions
• Numerical Stability: Carefully designed algorithms to prevent overflow, underflow, and cancellation errors
• Comprehensive Error Handling: Detailed error messages for edge cases
• Generic Input Support: Works with various numeric types

Installation

Add this to your Cargo.toml:

[dependencies]
statsrust = "0.1.0"  # Replace with the latest version

Quick Start

use statsrust::*;

fn main() -> Result<(), StatError> {
    let data = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    
    // Basic descriptive statistics
    let mean = mean(&data)?;
    let median = median(&data)?;
    let variance = variance(&data, None)?;
    
    println!("Mean: {:.2}, Median: {:.2}, Variance: {:.2}", mean, median, variance);
    
    // Kernel Density Estimation
    let kde = kde(&data, 0.5, "normal", false)?;
    println!("Density at 3.0: {:.4}", kde(3.0));
    
    // Normal distribution operations
    let dist = NormalDist::from_samples(&data)?;
    println!("Distribution: N(μ={:.2}, σ={:.2})", dist.mean(), dist.stdev());
    
    Ok(())
}

Key Highlights

Numerical Stability

Our algorithms are designed to avoid common numerical issues:

• Geometric mean uses logarithmic transformation to prevent overflow
• Variance calculation uses centered data to avoid catastrophic cancellation
• Inverse CDF approximations maintain precision while balancing performance

Kernel Density Estimation

Efficient implementation with multiple kernel functions:

• Gaussian, Epanechnikov, Triangular, Quartic, Triweight, and more
• Optimized for bounded kernels using binary search
• Supports both PDF and CDF estimation
• Random sample generation from estimated distributions

Normal Distribution Model

Complete algebraic operations:

let dist1 = NormalDist::new(0.0, 1.0)?;
let dist2 = NormalDist::new(1.0, 2.0)?;

// Distribution operations
let sum_dist = dist1 + dist2;  // N(1.0, √5)
let scaled_dist = dist1 * 2.0; // N(0.0, 2.0)
let overlap = dist1.overlap(&dist2); // Calculate distribution overlap

Documentation

For comprehensive documentation:

• API Reference
• Mathematical Foundations
• Usage Guide
• Contribution Guidelines

Contributing

We welcome contributions! Please see our Contribution Guidelines for details on how to report bugs, suggest enhancements, or submit pull requests.

License

This project is dual-licensed under:

• Apache License 2.0
• MIT License

Documentation content is licensed under:

• Creative Commons Attribution 4.0 International License (CC BY 4.0)

This document is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Original author: statsrust Authors