Crate rs_stats 

rs-stats — Comprehensive Statistical Library

rs-stats provides a complete, panic-free statistical toolkit for Rust: probability functions, 14 parametric distributions with fitting, automatic distribution detection, hypothesis testing, and regression analysis.

All fallible operations return StatsResult<T> — a Result<T, StatsError>.

Modules at a Glance

Module	Contents
distributions	14 distributions + unified trait interface + auto-fit API
hypothesis_tests	t-tests, ANOVA, chi-square, chi-square independence
regression	Linear, multiple linear, decision trees
prob	Mean, variance, std-dev, z-scores, erf, CDF helpers
utils	Special functions (ln_gamma, incomplete gamma/beta), combinatorics

Medical Quick-Start

Identify the distribution of a clinical measurement

use rs_stats::{auto_fit, fit_all, Distribution};
use rs_stats::distributions::lognormal::LogNormal;

// CRP levels (mg/L) from an outpatient cohort — right-skewed biomarker
let crp = vec![
    0.8, 1.2, 1.5, 2.1, 2.4, 3.2, 3.9, 5.6, 9.7, 12.4,
    22.3, 45.0, 88.0, 0.9, 1.3, 1.8, 0.7, 0.6, 0.5, 1.1,
];

// One call: auto-detect type, fit all candidates, return the best (lowest AIC)
let best = auto_fit(&crp).unwrap();
println!("Best distribution: {} (AIC={:.2})", best.name, best.aic);
// → Best distribution: LogNormal

// Fit explicitly and answer clinical questions
let crp_dist = LogNormal::fit(&crp).unwrap();
let median   = crp_dist.inverse_cdf(0.5).unwrap();
let p_high   = 1.0 - crp_dist.cdf(10.0).unwrap();  // P(CRP > 10 mg/L)
println!("Median CRP        = {:.2} mg/L", median);
println!("P(CRP > 10 mg/L)  = {:.1}%", p_high * 100.0);

Compare two treatment arms

use rs_stats::hypothesis_tests::t_test::two_sample_t_test;

// Systolic blood pressure (mmHg) — control vs. treated
let control   = vec![128.0, 132.0, 125.0, 130.0, 129.0, 131.0];
let treatment = vec![118.0, 122.0, 115.0, 120.0, 119.0, 121.0];

let result = two_sample_t_test(&control, &treatment, false).unwrap();
println!("t = {:.3}, p = {:.4}", result.t_statistic, result.p_value);
if result.p_value < 0.05 {
    println!("→ Significant BP reduction (α = 0.05)");
}

Compute reference intervals from data

use rs_stats::distributions::normal_distribution::Normal;
use rs_stats::Distribution;

// Haemoglobin (g/dL) in healthy adults — estimate the 95% reference interval
let hgb = vec![13.5, 14.2, 13.8, 15.1, 14.5, 13.9, 14.8, 15.3, 14.0, 14.6];
let dist = Normal::fit(&hgb).unwrap();

let lower = dist.inverse_cdf(0.025).unwrap();
let upper = dist.inverse_cdf(0.975).unwrap();
println!("95% reference interval: [{:.2}, {:.2}] g/dL", lower, upper);

Trait-Based Polymorphism

Use Box<dyn Distribution> to work with distributions at runtime:

use rs_stats::Distribution;
use rs_stats::distributions::{
    normal_distribution::Normal,
    lognormal::LogNormal,
};

// Choose the distribution based on data characteristics
fn best_model(skewed: bool) -> Box<dyn Distribution> {
    if skewed {
        Box::new(LogNormal::new(1.0, 0.5).unwrap())
    } else {
        Box::new(Normal::new(80.0, 10.0).unwrap())
    }
}

let model = best_model(true);
println!("Mean = {:.2}", model.mean());
println!("P95  = {:.2}", model.inverse_cdf(0.95).unwrap());

Re-exports

pub use error::StatsError;

pub use error::StatsResult;

pub use distributions::fitting::DataKind;

pub use distributions::fitting::FitResult;

pub use distributions::fitting::KsResult;

pub use distributions::fitting::auto_fit;

pub use distributions::fitting::detect_data_type;

pub use distributions::fitting::fit_all;

pub use distributions::fitting::fit_all_discrete;

pub use distributions::fitting::fit_best;

pub use distributions::fitting::fit_best_discrete;

pub use distributions::traits::DiscreteDistribution;

pub use distributions::traits::Distribution;

Modules

distributions

error

Error Types

hypothesis_tests

prob

regression

Regression module.

utils