A Rust crate for assessing the normality of a data sample. It provides several common statistical tests to determine if a set of data is likely drawn from a normal distribution.
All test implementations are generic and can work with f32 or f64 data types. The implementations are ported from well-established algorithms found in popular R packages.
- Shapiro-Wilk Test
- Lilliefors (Kolmogorov-Smirnov) Test
- Anderson-Darling Test
- Jarque-Bera Test
- Pearson Chi-squared Test
- Cramer-von Mises Test
- D'Agostino's K-squared Test
- Anscombe-Glynn Kurtosis Test
- Energy Test
Either run cargo add normality or add the crate to your Cargo.toml:
[dependencies]
normality = "2"use normality::{shapiro_wilk, Error};
fn main() -> Result<(), Error> {
// Sample data that is likely from a normal distribution
let data = vec![-1.1, -0.8, -0.5, -0.2, 0.0, 0.2, 0.5, 0.8, 1.1, 1.3];
// Perform the Shapiro-Wilk test
let result = shapiro_wilk(data)?;
println!("Shapiro-Wilk Test Results:");
println!(" W-statistic: {:.4}", result.statistic);
println!(" p-value: {:.4}", result.p_value);
// Interpretation: A high p-value (e.g., > 0.05) suggests that the data
// does not significantly deviate from a normal distribution.
if result.p_value > 0.05 {
println!("Conclusion: The sample is likely from a normal distribution.");
} else {
println!("Conclusion: The sample is not likely from a normal distribution.");
}
Ok(())
}The accuracy of the implemented tests has been verified against their R equivalents. Running the integration tests for this crate requires a local installation of R and for the Rscript executable to be available in the system's PATH.
This project is licensed under the MIT License.