Crate anofox_regression 

A robust statistics library for regression analysis.

This library provides sklearn-style regression estimators with full statistical inference support including standard errors, t-statistics, p-values, and confidence intervals.

Example

use statistics::prelude::*;

// Create and fit an OLS model
let fitted = OlsRegressor::builder()
    .with_intercept(true)
    .confidence_level(0.95)
    .build()
    .fit(&x, &y)?;

// Make predictions
let predictions = fitted.predict(&x_new);

// Access statistics
let stats = fitted.result();
println!("R² = {}", stats.r_squared);

Re-exports

pub use crate::core::BinomialFamily;

pub use crate::core::BinomialLink;

pub use crate::core::GlmFamily;

pub use crate::core::IntervalType;

pub use crate::core::LambdaScaling;

pub use crate::core::NaAction;

pub use crate::core::NaError;

pub use crate::core::NaHandler;

pub use crate::core::NaInfo;

pub use crate::core::NegativeBinomialFamily;

pub use crate::core::PoissonFamily;

pub use crate::core::PoissonLink;

pub use crate::core::PredictionResult;

pub use crate::core::PredictionType;

pub use crate::core::RegressionOptions;

pub use crate::core::RegressionOptionsBuilder;

pub use crate::core::RegressionResult;

pub use crate::core::TweedieFamily;

pub use crate::solvers::BinomialRegressor;

pub use crate::solvers::FittedBinomial;

pub use crate::solvers::FittedIsotonic;

pub use crate::solvers::FittedNegativeBinomial;

pub use crate::solvers::FittedPoisson;

pub use crate::solvers::FittedQuantile;

pub use crate::solvers::FittedRegressor;

pub use crate::solvers::IsotonicRegressor;

pub use crate::solvers::NegativeBinomialRegressor;

pub use crate::solvers::OutOfBounds;

pub use crate::solvers::PoissonRegressor;

pub use crate::solvers::QuantileRegressor;

pub use crate::solvers::Regressor;

Modules

core

Core types for regression analysis.

diagnostics

Regression diagnostics (leverage, Cook’s distance, VIF, etc.).

distributions

Statistical distributions (t, F).

inference

Statistical inference (standard errors, p-values, confidence intervals).

prelude

Prelude module for convenient imports.

solvers

Regression solvers implementing various estimation methods.

utils

Utility functions and helpers.