Crate solow_robust 

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.

Re-exports

pub use norms_ext::Hampel;

pub use norms_ext::RamsayE;

pub use norms_ext::TrimmedMean;

Modules

norms

Robust criterion functions (norms) used by M-estimation.

norms_ext

Additional robust criterion functions (norms) for M-estimation.

scale

Robust scale estimators used to standardize residuals during IRLS.

Structs

Rlm

A robust linear model awaiting estimation.

RlmResults

The fitted result of an Rlm.

Enums

Conv

Convergence criterion for the IRLS loop.

ScaleEst

The scale estimator used to standardize residuals between IRLS steps.