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//! # inferust
//!
//! **Statistical modeling for Rust** — a `statsmodels`-inspired library.
//!
//! ## Modules
//!
//! | Module | Contents |
//! |--------|---------|
//! | [`regression`] | OLS with fast Cholesky and stable SVD solvers plus full summary output |
//! | [`hypothesis`] | t-tests, chi-squared, one-way ANOVA |
//! | [`descriptive`] | Summary stats (mean, std, skewness, kurtosis, quartiles) |
//! | [`correlation`] | Pearson, Spearman, correlation matrices |
//!
//! ## OLS solver strategy
//!
//! [`regression::Ols`] defaults to a fast Cholesky solve for full-rank,
//! well-conditioned designs. Use `.stable()` or
//! [`regression::OlsSolver::Svd`] when you prefer the more robust SVD path.
//!
//! ## Quick start
//!
//! ```rust
//! use inferust::regression::Ols;
//!
//! let x = vec![vec![1.0], vec![2.0], vec![3.0], vec![4.0], vec![5.0]];
//! let y = vec![2.1, 3.9, 6.2, 7.8, 10.1];
//!
//! Ols::new()
//! .with_feature_names(vec!["hours".to_string()])
//! .fit(&x, &y)
//! .unwrap()
//! .print_summary();
//! ```
pub use ;