statrs 0.5.1

Statistical computing library for Rust

statrs

Build Status MIT licensed Crates.io

Current Version: v0.5.0

Should work for both nightly and stable Rust.

NOTE: While I will try to maintain backwards compatibility as much as possible, since this is still a 0.x.x project the API is not considered stable and thus subject to possible breaking changes up until v1.0.0

Description

Statrs provides a host of statistical utilities for Rust scientific computing. Included are a number of common distributions that can be sampled (i.e. Normal, Exponential, Student's T, Gamma, Uniform, etc.) plus common statistical functions like the gamma function, beta function, and error function.

This library is a work-in-progress port of the statistical capabilities in the C# Math.NET library. All unit tests in the library borrowed from Math.NET when possible and filled-in when not.

This library is a work-in-progress and not complete. Planned for future releases are continued implementations of distributions as well as porting over more statistical utilities

Please check out the documentation here

Usage

Add the following to your Cargo.toml

[dependencies]
statrs = "0.5.0"

and this to your crate root

extern crate statrs;

Examples

Statrs v0.5.0 comes with a number of commonly used distributions including Normal, Gamma, Student's T, Exponential, Weibull, etc. The common use case is to set up the distributions and sample from them which depends on the Rand crate for random number generation

use rand;
use statrs::distribution::{Exponential, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Exponential::new(0.5).unwrap();
print!("{}", n.Sample::<StdRng>(&mut r);

Statrs also comes with a number of useful utility traits for more detailed introspection of distributions

use statrs::distribution::{Exponential, Univariate, Continuous};
use statrs::statistics::{Mean, Variance, Entropy, Skewness};

let n = Exponential::new(1.0).unwrap();
assert_eq!(n.mean(), 1.0);
assert_eq!(n.variance(), 1.0);
assert_eq!(n.entropy(), 1.0);
assert_eq!(n.skewness(), 2.0);
assert_eq!(n.cdf(1.0), 0.6321205588285576784045);
assert_eq!(n.pdf(1.0), 0.3678794411714423215955);

as well as utility functions including erf, gamma, ln_gamma, beta, etc