u-numflow

Domain-agnostic mathematical primitives in Rust

Overview

u-numflow provides foundational mathematical, statistical, and probabilistic building blocks. Entirely domain-agnostic with no external dependencies beyond rand.

Modules

| Module | Description | |--------|-------------| | stats | Descriptive statistics (mean, variance, skewness, kurtosis) with Welford's online algorithm and Neumaier summation | | distributions | Probability distributions: Uniform, Triangular, PERT, Normal, LogNormal | | special | Special functions: normal/t/F/chi² CDF, inverse normal CDF, regularized incomplete beta/gamma, erf | | transforms | Data transformations: Box-Cox (λ via MLE golden-section search), inverse Box-Cox | | matrix | Dense matrix operations: determinant, inverse, Cholesky decomposition, Jacobi eigenvalue decomposition | | random | Seeded RNG, Fisher-Yates shuffle, weighted sampling, random subset selection | | collections | Specialized data structures: Union-Find with path compression and union-by-rank |

Design Philosophy

• Numerical stability first — Welford's algorithm for variance, Neumaier summation for accumulation
• Reproducibility — Seeded RNG support for deterministic experiments
• Property-based testing — Mathematical invariants verified via proptest

Quick Start

[dependencies]
u-numflow = "0.2"

use u_numflow::stats::OnlineStats;
use u_numflow::distributions::{PertDistribution, Distribution};
use u_numflow::random::Rng;

// Online statistics with numerical stability
let mut stats = OnlineStats::new();
for x in [1.0, 2.0, 3.0, 4.0, 5.0] {
    stats.push(x);
}
assert_eq!(stats.mean(), 3.0);

// PERT distribution sampling
let pert = PertDistribution::new(1.0, 4.0, 7.0);
let mut rng = Rng::seed_from_u64(42);
let sample = pert.sample(&mut rng);

// Seeded shuffling for reproducibility
let mut items = vec![1, 2, 3, 4, 5];
u_numflow::random::shuffle(&mut items, &mut rng);

// Box-Cox transformation (non-normal data normalization)
use u_numflow::transforms::{estimate_lambda, box_cox};
let data = [1.0, 2.0, 4.0, 8.0, 16.0];
let lambda = estimate_lambda(&data, -2.0, 2.0).unwrap(); // MLE via golden-section
let transformed = box_cox(&data, lambda).unwrap();

Build & Test

cargo build
cargo test

Dependencies

• rand 0.9 — Random number generation
• proptest 1.4 — Property-based testing (dev only)

License

MIT License — see LICENSE.

Related

• u-metaheur — Metaheuristic optimization (GA, SA, ALNS, CP)
• u-geometry — Computational geometry
• u-schedule — Scheduling framework
• u-nesting — 2D/3D nesting and bin packing