HypoRS: A Statistical Hypothesis Testing Library

hypors is a Rust library designed for performing a variety of hypothesis tests, including t-tests, z-tests, proportion tests, ANOVA, Chi-square tests, and Mann-Whitney tests. This library leverages the statrs crate for statistical distributions.

Rust Crate: https://crates.io/crates/hypors

PyPI Package: Work in Progress

Features

Hypothesis Tests

Hypothesis testing is available for this suite of common distributions.

• T-Tests: One-sample, two-sample paired, and two-sample independent t-tests.
• Z-Tests: One-sample, two-sample paired, and two-sample independent z-tests.
• Proportion Tests: One-sample and two-sample proportion tests.
• ANOVA: One-way ANOVA for comparing means across multiple groups.
• Chi-Square Tests: Chi-square test for independence and goodness-of-fit tests.
• Mann-Whitney U Test: Non-parametric test for comparing two independent samples.

Sample Size Calculation

All parametrized distributions have respective modules to calculate minimum sample size required with customizable parameters for alpha and statistical power.

Additional Features:

• Customizable tail type (left, right, and two-tailed).
• Customizable alpha value for all tests.
• Confidence interval calculations for all tests.
• p-value is generated along with each statistic.
• Null and alternate hypotheses strings are also generated.

Installation

To use this library in your Rust project, add the following to your Cargo.toml:

[dependencies]
hypors = "0.3.0"

Note HypoRS relies on the following dependencies, which will be automatically included:

serde (version >=1.0.210)
statrs (version >=0.17.1)

Example Usage

Rust

Here are some examples of running tests with Rust.

T - Test

use hypors::{t::t_test, common::TailType};

let data = vec![1.2, 2.3, 1.9, 2.5, 2.8];
let population_mean = 2.0;
let tail = TailType::Two;
let alpha = 0.05;

let result = t_test(data, population_mean, tail, alpha).unwrap();
println!("Test Statistic: {}", result.test_statistic);
println!("p-value: {}", result.p_value);
println!("Confidence Interval: {}", result.confidence_interval);
println!("Null Hypothesis: {}", result.null_hypothesis);
println!("Alternate Hypothesis: {}", result.alt_hypothesis);
println!("Reject Null Hypothesis?: {}", result.reject_null);

Chi Square Test

use hypors::{chi_square::independence};

let observed = vec![10, 20, 30]; // Observed frequencies
let expected = vec![15, 15, 30]; // Expected frequencies

let result = independece(observed, expected).unwrap();
println!("Chi-Square Statistic: {}", result.test_statistic);
println!("p-value: {}", result.p_value);
println!("Null Hypothesis: {}", result.null_hypothesis);
println!("Alternate Hypothesis: {}", result.alt_hypothesis);
println!("Reject Null Hypothesis?: {}", result.reject_null);

Usage with Polars

With the polars crate having frequent minor version updates, this library has been updated to not use Polars inherently, and relies on Vec types.

Any use of polars Series types should be converted before and after using the hypors functions.

Polars to Vec Conversion:

use polars::prelude::*;
let series = Series::new("series_name", &[1, 2, 3, 4, 5]);
let vec = series.to_vec().unwrap();

Vec to Polars Conversion:

use polars::prelude::*;
let vec = vec![1, 2, 3, 4, 5];
let series = Series::new("series_name", vec);

Python

Work in Progress

Future Plans

The next step for hypors is to add Python bindings to make it accessible to the Python community. This work is currently in progress. Stay tuned for updates!

Contributing

Contributions are always welcome! If you have suggestions for improvements, bug fixes, or new features, please feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.