Crate linreg_core 

linreg-core

A lightweight, self-contained linear regression library in pure Rust.

No external math dependencies. All linear algebra (matrices, QR decomposition) and statistical functions (distributions, hypothesis tests) are implemented from scratch. Compiles to WebAssembly for browser use or runs as a native Rust crate.

Live Demo →

What This Does

• OLS Regression — Ordinary Least Squares with numerically stable QR decomposition
• Regularized Regression — Ridge, Lasso, and Elastic Net via coordinate descent
• Diagnostic Tests — 8+ statistical tests for validating regression assumptions
• WASM Support — Same API works in browsers via WebAssembly

Quick Start

Native Rust

Add to Cargo.toml (no WASM overhead):

[dependencies]
linreg-core = { version = "0.6", default-features = false }

use linreg_core::core::ols_regression;

let y = vec![2.5, 3.7, 4.2, 5.1, 6.3];
let x1 = vec![1.0, 2.0, 3.0, 4.0, 5.0];
let x2 = vec![2.0, 4.0, 5.0, 4.0, 3.0];
let names = vec!["Intercept".into(), "Temp".into(), "Pressure".into()];

let result = ols_regression(&y, &[x1, x2], &names)?;
println!("R²: {}", result.r_squared);
println!("F-statistic: {}", result.f_statistic);

WebAssembly (JavaScript)

[dependencies]
linreg-core = "0.6"

Build with wasm-pack build --target web, then use in JavaScript:

import init, { ols_regression } from './linreg_core.js';
await init();

const result = JSON.parse(ols_regression(
    JSON.stringify([2.5, 3.7, 4.2, 5.1, 6.3]),
    JSON.stringify([[1,2,3,4,5], [2,4,5,4,3]]),
    JSON.stringify(["Intercept", "X1", "X2"])
));
console.log("R²:", result.r_squared);

Regularized Regression

use linreg_core::regularized::{ridge, lasso};
use linreg_core::linalg::Matrix;

let x = Matrix::new(100, 3, vec![0.0; 300]);
let y = vec![0.0; 100];

// Ridge regression (L2 penalty - shrinks coefficients, handles multicollinearity)
let ridge_result = ridge::ridge_fit(&x, &y, &ridge::RidgeFitOptions {
    lambda: 1.0,
    intercept: true,
    standardize: true,
    ..Default::default()
})?;

// Lasso regression (L1 penalty - does variable selection by zeroing coefficients)
let lasso_result = lasso::lasso_fit(&x, &y, &lasso::LassoFitOptions {
    lambda: 0.1,
    intercept: true,
    standardize: true,
    ..Default::default()
})?;

Diagnostic Tests

After fitting a model, validate its assumptions:

Test	Tests For	Use When
diagnostics::rainbow_test	Linearity	Checking if relationships are linear
diagnostics::harvey_collier_test	Functional form	Suspecting model misspecification
diagnostics::breusch_pagan_test	Heteroscedasticity	Variance changes with predictors
diagnostics::white_test	Heteroscedasticity	More general than Breusch-Pagan
diagnostics::shapiro_wilk_test	Normality	Small to moderate samples (n ≤ 5000)
diagnostics::jarque_bera_test	Normality	Large samples, skewness/kurtosis
diagnostics::anderson_darling_test	Normality	Tail-sensitive, any sample size
diagnostics::durbin_watson_test	Autocorrelation	Time series or ordered data
diagnostics::cooks_distance_test	Influential points	Identifying high-impact observations

use linreg_core::diagnostics::{rainbow_test, breusch_pagan_test, RainbowMethod};

// Rainbow test for linearity
let rainbow = rainbow_test(&y, &[x1.clone(), x2.clone()], 0.5, RainbowMethod::R)?;
if rainbow.r_result.as_ref().map_or(false, |r| r.p_value < 0.05) {
    println!("Warning: relationship may be non-linear");
}

// Breusch-Pagan test for heteroscedasticity
let bp = breusch_pagan_test(&y, &[x1, x2])?;
if bp.p_value < 0.05 {
    println!("Warning: residuals have non-constant variance");
}

Feature Flags

Flag	Default	Description
wasm	Yes	Enables WASM bindings and browser support
validation	No	Includes test data for validation tests

For native-only builds (smaller binary, no WASM deps):

linreg-core = { version = "0.6", default-features = false }

Why This Library?

• Zero dependencies — No nalgebra, no statrs, no ndarray. Pure Rust.
• Validated — Outputs match R’s lm() and Python’s statsmodels
• WASM-ready — Same code runs natively and in browsers
• Small — Core is ~2000 lines, compiles quickly
• Permissive license — MIT OR Apache-2.0

Module Structure

• core — OLS regression, coefficients, residuals, VIF
• regularized — Ridge, Lasso, Elastic Net, regularization paths
• cross_validation — K-Fold Cross Validation for model evaluation
• diagnostics — All diagnostic tests (linearity, heteroscedasticity, normality, autocorrelation)
• distributions — Statistical distributions (t, F, χ², normal, beta, gamma)
• linalg — Matrix operations, QR decomposition, linear system solver
• error — Error types and Result alias

Links

• Repository
• Documentation
• Examples

Disclaimer

This library is under active development and has not reached 1.0 stability. While outputs are validated against R and Python implementations, do not use this library for critical applications (medical, financial, safety-critical systems) without independent verification. See the LICENSE for full terms. The software is provided “as is” without warranty of any kind.

Re-exports

pub use core::aic;

pub use core::aic_python;

pub use core::bic;

pub use core::bic_python;

pub use core::log_likelihood;

pub use core::RegressionOutput;

pub use core::VifResult;

pub use diagnostics::BGTestType;

pub use diagnostics::BreuschGodfreyResult;

pub use diagnostics::CooksDistanceResult;

pub use diagnostics::DiagnosticTestResult;

pub use diagnostics::RainbowMethod;

pub use diagnostics::RainbowSingleResult;

pub use diagnostics::RainbowTestOutput;

pub use diagnostics::ResetType;

pub use diagnostics::WhiteMethod;

pub use diagnostics::WhiteSingleResult;

pub use diagnostics::WhiteTestOutput;

pub use cross_validation::CVResult;

pub use cross_validation::FoldResult;

pub use cross_validation::KFoldOptions;

pub use cross_validation::kfold_cv_elastic_net;

pub use cross_validation::kfold_cv_lasso;

pub use cross_validation::kfold_cv_ols;

pub use cross_validation::kfold_cv_ridge;

pub use loess::loess_fit;

pub use loess::LoessFit;

pub use loess::LoessOptions;

pub use weighted_regression::wls_regression;

pub use weighted_regression::WlsFit;

pub use diagnostics::rainbow_test as rainbow_test_core;

pub use diagnostics::white_test as white_test_core;

pub use error::error_json;

pub use error::error_to_json;

pub use error::Error;

pub use error::Result;

Modules

core

Core OLS regression implementation.

cross_validation

K-Fold Cross Validation for linear regression models.

diagnostics

Statistical diagnostic tests for linear regression assumptions.

distributions

Custom statistical special functions and distribution utilities (CDF/SF/quantiles), primarily to avoid pulling in statrs for regression diagnostics.

error

Error types for the linear regression library.

linalg

Minimal Linear Algebra module to replace nalgebra dependency.

loess

LOESS (Locally Estimated Scatterplot Smoothing)

regularized

Ridge and Lasso regression (glmnet-compatible implementations).

serialization

Model serialization module for saving and loading regression models.

stats

Basic statistical utility functions.

wasm

WASM-specific bindings for linreg-core

weighted_regression

Weighted regression methods

Macros

impl_serialization

Macro to generate ModelSave and ModelLoad implementations for a model type.