solow-robust 0.2.0

Robust linear models (M-estimation) for the Solow statistical library
Documentation 
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# solow-robust

Robust linear regression by M-estimation, matching the reference's robust
linear model (RLM).

The estimator minimizes `Σ ρ((yᵢ − xᵢ·β) / σ)` for a robust criterion `ρ`
using iteratively reweighted least squares (IRLS), re-estimating the scale
`σ` from the residuals at each step.

```
use ndarray::{Array1, Array2};
use solow_robust::{norms::TukeyBiweight, Rlm};

// A noisy line near y = 2 + 0.5·x with a gross outlier at the last point.
let x: Vec<f64> = (1..=10).map(|i| i as f64).collect();
let y = Array1::from(vec![
    2.6, 3.1, 3.4, 4.1, 4.4, 5.1, 5.4, 6.1, 6.4, 100.0,
]);
let exog =
    Array2::from_shape_fn((10, 2), |(i, j)| if j == 0 { 1.0 } else { x[i] });
let res = Rlm::new(y, exog, TukeyBiweight::default())
    .unwrap()
    .fit()
    .unwrap();
assert!(res.converged);
// The redescending norm fully rejects the outlier ...
assert_eq!(res.weights[9], 0.0);
// ... so the slope stays close to the clean trend rather than ~10.
assert!((res.params[1] - 0.5).abs() < 0.05);
```

## Components

* [`norms`] — robust criterion functions ([`norms::HuberT`],
  [`norms::TukeyBiweight`], [`norms::AndrewWave`], [`norms::LeastSquares`]).
* [`scale`] — robust scale estimators ([`scale::mad`], [`scale::Huber`],
  [`scale::HuberScale`]).
* [`Rlm`] / [`RlmResults`] — the model and its fitted result.

---

Part of **[Solow](https://github.com/benovamurat/solow)** — a complete statistical-modeling, econometrics & data-visualization toolkit for Rust. · [Docs](https://docs.rs/solow-robust) · License: BSD-3-Clause