hypors 0.3.0

Hypothesis Testing with Rust
Documentation 
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#[cfg(test)]
mod tests_chi_square {
    use hypors::chi_square::{
        chi2_sample_size_gof, chi2_sample_size_ind, chi2_sample_size_variance, goodness_of_fit,
        independence, variance,
    };
    use hypors::common::TailType;

    const EPSILON: f64 = 0.001; // Tolerance for floating-point comparisons

    #[test]
    fn test_variance() {
        let data = vec![2.0, 3.0, 5.0, 7.0, 11.0];
        let pop_variance = 5.0;
        let alpha = 0.05;

        let result = variance(data, pop_variance, TailType::Two, alpha).unwrap();

        let expected_chi_square_stat = 10.24;
        let expected_p_value = 0.073;
        let expected_null_hypothesis = "H0: σ² = 5";
        let expected_alt_hypothesis = "Ha: σ² ≠ 5";

        assert!((result.test_statistic - expected_chi_square_stat).abs() < EPSILON);
        assert!((result.p_value - expected_p_value).abs() < EPSILON);

        assert_eq!(result.null_hypothesis, expected_null_hypothesis);
        assert_eq!(result.alt_hypothesis, expected_alt_hypothesis);

        assert_eq!(result.reject_null, false);
    }

    #[test]
    fn test_independence() {
        let contingency_table = vec![vec![20.0, 30.0], vec![50.0, 10.0]];
        let alpha = 0.05;

        let result = independence(&contingency_table, alpha).unwrap();

        let expected_chi_square_stat = 22.131;
        let expected_p_value = 0.000;
        let expected_null_hypothesis = "H0: Variables are independent";
        let expected_alt_hypothesis = "Ha: Variables are not independent";

        assert!((result.test_statistic - expected_chi_square_stat).abs() < EPSILON);
        assert!((result.p_value - expected_p_value).abs() < EPSILON);

        assert_eq!(result.null_hypothesis, expected_null_hypothesis);
        assert_eq!(result.alt_hypothesis, expected_alt_hypothesis);

        assert_eq!(result.reject_null, true);
    }

    #[test]
    fn test_goodness_of_fit() {
        let observed = vec![30.0, 10.0, 20.0];
        let expected = vec![25.0, 15.0, 20.0];
        let alpha = 0.05;

        let result = goodness_of_fit(observed, expected, alpha).unwrap();

        let expected_chi_square_stat = 2.666;
        let expected_p_value = 0.263;
        let expected_null_hypothesis = "H0: Observed distribution matches expected distribution";
        let expected_alt_hypothesis =
            "Ha: Observed distribution does not match expected distribution";

        assert!((result.test_statistic - expected_chi_square_stat).abs() < EPSILON);
        assert!((result.p_value - expected_p_value).abs() < EPSILON);

        assert_eq!(result.null_hypothesis, expected_null_hypothesis);
        assert_eq!(result.alt_hypothesis, expected_alt_hypothesis);

        assert_eq!(result.reject_null, false);
    }

    #[test]
    fn test_chi2_sample_size_gof() {
        let expected_counts = vec![20, 30, 50];
        let alpha = 0.05;

        let n = chi2_sample_size_gof(&expected_counts, alpha);
        let expected_sample_size = 480.0;

        assert!(
            (n - expected_sample_size).abs() < 1.0,
            "Sample size is incorrect"
        );
    }

    #[test]
    fn test_chi2_sample_size_ind() {
        let expected_counts = vec![20, 30, 50];
        let alpha = 0.05;

        let n = chi2_sample_size_ind(&expected_counts, alpha);
        let expected_sample_size = 480.0;

        assert!(
            (n - expected_sample_size).abs() < 1.0,
            "Sample size is incorrect"
        );
    }

    #[test]
    fn test_chi2_sample_size_variance() {
        let effect_size = 0.5;
        let alpha = 0.05;
        let power = 0.80;
        let variance = 1.0;

        let n = chi2_sample_size_variance(effect_size, alpha, power, variance);
        let expected_sample_size = 121.0;

        assert!(
            (n - expected_sample_size).abs() < 1.0,
            "Sample size is incorrect"
        );
    }
}