numeris 0.5.4

Pure-Rust numerical algorithms library — high performance with SIMD support while also supporting no-std for embedded and WASM targets.
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# numeris

**A mostly self-contained numerical algorithms library with minimal external dependencies.**

numeris is suitable for use on **embedded microprocessors** with no operating system or heap allocation (`no-std`), while still taking advantage of SIMD instructions to be highly performant on more capable hardware. The same code compiles and runs correctly in both environments.

Numeris API reference is [here](https://docs.rs/numeris/)

!!! warning "Alpha software"
    APIs are unstable and may change without notice before 1.0.

## Feature Highlights

| Area | What's included |
|---|---|
| **Matrix** | Stack-allocated `Matrix<T, M, N>` with const-generic dimensions; `DynMatrix<T>` for runtime sizes |
| **Linear Algebra** | LU, Cholesky, QR, SVD, symmetric eigendecomposition, real Schur decomposition |
| **ODE** | Fixed-step RK4 + 7 adaptive Runge-Kutta solvers + RODAS4 stiff solver |
| **Optimization** | Brent, Newton, BFGS, Gauss-Newton, Levenberg-Marquardt |
| **State Estimation** | EKF, UKF, SR-UKF, CKF, RTS smoother, batch least-squares |
| **Interpolation** | Linear, Hermite, Lagrange, cubic spline, bilinear |
| **Special Functions** | gamma, lgamma, digamma, beta, betainc, incomplete gamma, erf |
| **Statistics** | 10 distributions (Normal, Gamma, Beta, Student's t, Poisson, …) |
| **Digital Control** | Butterworth/Chebyshev IIR filters, PID controller |
| **Quaternion** | Unit quaternion rotations, SLERP, Euler angles, rotation matrices |
| **SIMD** | NEON (aarch64), SSE2/AVX/AVX-512 (x86_64) — always-on, no feature flag |

## Quick Start

Add to `Cargo.toml`:

```toml
[dependencies]
numeris = "0.5"
```

```rust
use numeris::{Matrix, Vector};

// Solve Ax = b
let a = Matrix::new([
    [2.0_f64, 1.0, -1.0],
    [-3.0, -1.0,  2.0],
    [-2.0,  1.0,  2.0],
]);
let b = Vector::from_array([8.0, -11.0, -3.0]);
let x = a.solve(&b).unwrap(); // x = [2, 3, -1]

// Cholesky decomposition
let spd = Matrix::new([[4.0_f64, 2.0], [2.0, 3.0]]);
let inv = spd.cholesky().unwrap().inverse();

// Symmetric eigendecomposition
let sym = Matrix::new([[4.0_f64, 1.0], [1.0, 3.0]]);
let eig = sym.eig_symmetric().unwrap();
let vals = eig.eigenvalues();   // sorted ascending
let vecs = eig.eigenvectors();  // columns = eigenvectors

// Quaternion rotation
use numeris::Quaternion;
let q = Quaternion::from_axis_angle(
    &Vector::from_array([0.0, 0.0, 1.0]),
    std::f64::consts::FRAC_PI_2,
);
let v = Vector::from_array([1.0, 0.0, 0.0]);
let rotated = q * v; // ≈ [0, 1, 0]
```

## Navigation

- **[Getting Started](getting-started.md)** — installation, feature flags, first examples
- **Module docs** — detailed pages for each module (use the top tabs)
- **[Performance](performance.md)** — SIMD tiers, benchmark numbers vs. nalgebra and faer
- **[No-std / Embedded](no-std.md)** — usage without std or heap allocation
- **[Design](design.md)** — architectural decisions and trait hierarchy
- **[API Reference ↗](https://docs.rs/numeris)** — full rustdoc on docs.rs