diff --git a/rand_distr/CHANGELOG.md b/rand_distr/CHANGELOG.md
index a19641752f..c9ab729be8 100644
--- a/rand_distr/CHANGELOG.md
+++ b/rand_distr/CHANGELOG.md
@@ -9,6 +9,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fix panic in Binomial (#1484)
 - Move some of the computations in Binomial from `sample` to `new` (#1484)
 - Add Kolmogorov Smirnov test for sampling of `Normal` and `Binomial` (#1494)
+- Add Kolmogorov Smirnov test for more distributions (#1504)
 
 ### Added
 - Add plots for `rand_distr` distributions to documentation (#1434)
diff --git a/rand_distr/tests/cdf.rs b/rand_distr/tests/cdf.rs
index 71b808d241..8eb22740e2 100644
--- a/rand_distr/tests/cdf.rs
+++ b/rand_distr/tests/cdf.rs
@@ -6,13 +6,13 @@
 // option. This file may not be copied, modified, or distributed
 // except according to those terms.
 
+mod common;
 use core::f64;
 
 use num_traits::AsPrimitive;
 use rand::SeedableRng;
 use rand_distr::{Distribution, Normal};
-use special::Beta;
-use special::Primitive;
+use special::{Beta, Gamma, Primitive};
 
 // [1] Nonparametric Goodness-of-Fit Tests for Discrete Null Distributions
 //     by Taylor B. Arnold and John W. Emerson
@@ -121,11 +121,12 @@ pub fn test_continuous(seed: u64, dist: impl Distribution<f64>, cdf: impl Fn(f64
 
 /// Tests a distribution over integers against an analytical CDF.
 /// The analytical CDF must not have jump points which are not integers.
-pub fn test_discrete<I: AsPrimitive<f64>>(
-    seed: u64,
-    dist: impl Distribution<I>,
-    cdf: impl Fn(i64) -> f64,
-) {
+pub fn test_discrete<I, D, F>(seed: u64, dist: D, cdf: F)
+where
+    I: AsPrimitive<f64>,
+    D: Distribution<I>,
+    F: Fn(i64) -> f64,
+{
     let ecdf = sample_ecdf(seed, dist);
     let ks_statistic = kolmogorov_smirnov_statistic_discrete(ecdf, cdf);
 
@@ -159,6 +160,367 @@ fn normal() {
     }
 }
 
+#[test]
+fn skew_normal() {
+    fn cdf(x: f64, location: f64, scale: f64, shape: f64) -> f64 {
+        let norm = (x - location) / scale;
+        phi(norm) - 2.0 * owen_t(norm, shape)
+    }
+
+    let parameters = [(0.0, 1.0, 5.0), (1.0, 10.0, -5.0), (-1.0, 0.00001, 0.0)];
+
+    for (seed, (location, scale, shape)) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::SkewNormal::new(location, scale, shape).unwrap();
+        test_continuous(seed as u64, dist, |x| cdf(x, location, scale, shape));
+    }
+}
+
+#[test]
+fn cauchy() {
+    fn cdf(x: f64, median: f64, scale: f64) -> f64 {
+        (1.0 / f64::consts::PI) * ((x - median) / scale).atan() + 0.5
+    }
+
+    let parameters = [
+        (0.0, 1.0),
+        (0.0, 0.1),
+        (1.0, 10.0),
+        (1.0, 100.0),
+        (-1.0, 0.00001),
+        (-1.0, 0.0000001),
+    ];
+
+    for (seed, (median, scale)) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::Cauchy::new(median, scale).unwrap();
+        test_continuous(seed as u64, dist, |x| cdf(x, median, scale));
+    }
+}
+
+#[test]
+fn uniform() {
+    fn cdf(x: f64, a: f64, b: f64) -> f64 {
+        if x < a {
+            0.0
+        } else if x < b {
+            (x - a) / (b - a)
+        } else {
+            1.0
+        }
+    }
+
+    let parameters = [(0.0, 1.0), (-1.0, 1.0), (0.0, 100.0), (-100.0, 100.0)];
+
+    for (seed, (a, b)) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::Uniform::new(a, b).unwrap();
+        test_continuous(seed as u64, dist, |x| cdf(x, a, b));
+    }
+}
+
+#[test]
+fn log_normal() {
+    fn cdf(x: f64, mean: f64, std_dev: f64) -> f64 {
+        if x <= 0.0 {
+            0.0
+        } else if x.is_infinite() {
+            1.0
+        } else {
+            0.5 * ((mean - x.ln()) / (std_dev * f64::consts::SQRT_2)).erfc()
+        }
+    }
+
+    let parameters = [
+        (0.0, 1.0),
+        (0.0, 0.1),
+        (0.5, 0.7),
+        (1.0, 10.0),
+        (1.0, 100.0),
+    ];
+
+    for (seed, (mean, std_dev)) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::LogNormal::new(mean, std_dev).unwrap();
+        test_continuous(seed as u64, dist, |x| cdf(x, mean, std_dev));
+    }
+}
+
+#[test]
+fn pareto() {
+    fn cdf(x: f64, scale: f64, alpha: f64) -> f64 {
+        if x <= scale {
+            0.0
+        } else {
+            1.0 - (scale / x).powf(alpha)
+        }
+    }
+
+    let parameters = [
+        (1.0, 1.0),
+        (1.0, 0.1),
+        (1.0, 10.0),
+        (1.0, 100.0),
+        (0.1, 1.0),
+        (10.0, 1.0),
+        (100.0, 1.0),
+    ];
+
+    for (seed, (scale, alpha)) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::Pareto::new(scale, alpha).unwrap();
+        test_continuous(seed as u64, dist, |x| cdf(x, scale, alpha));
+    }
+}
+
+#[test]
+fn exp() {
+    fn cdf(x: f64, lambda: f64) -> f64 {
+        1.0 - (-lambda * x).exp()
+    }
+
+    let parameters = [0.5, 1.0, 7.5, 32.0, 100.0];
+
+    for (seed, lambda) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::Exp::new(lambda).unwrap();
+        test_continuous(seed as u64, dist, |x| cdf(x, lambda));
+    }
+}
+
+#[test]
+fn weibull() {
+    fn cdf(x: f64, lambda: f64, k: f64) -> f64 {
+        if x < 0.0 {
+            return 0.0;
+        }
+
+        1.0 - (-(x / lambda).powf(k)).exp()
+    }
+
+    let parameters = [
+        (0.5, 1.0),
+        (1.0, 1.0),
+        (10.0, 0.1),
+        (0.1, 10.0),
+        (15.0, 20.0),
+        (1000.0, 0.01),
+    ];
+
+    for (seed, (lambda, k)) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::Weibull::new(lambda, k).unwrap();
+        test_continuous(seed as u64, dist, |x| cdf(x, lambda, k));
+    }
+}
+
+#[test]
+fn gumbel() {
+    fn cdf(x: f64, mu: f64, beta: f64) -> f64 {
+        (-(-(x - mu) / beta).exp()).exp()
+    }
+
+    let parameters = [
+        (0.0, 1.0),
+        (1.0, 2.0),
+        (-1.0, 0.5),
+        (10.0, 0.1),
+        (100.0, 0.0001),
+    ];
+
+    for (seed, (mu, beta)) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::Gumbel::new(mu, beta).unwrap();
+        test_continuous(seed as u64, dist, |x| cdf(x, mu, beta));
+    }
+}
+
+#[test]
+fn frechet() {
+    fn cdf(x: f64, alpha: f64, s: f64, m: f64) -> f64 {
+        if x < m {
+            return 0.0;
+        }
+
+        (-((x - m) / s).powf(-alpha)).exp()
+    }
+
+    let parameters = [
+        (0.5, 2.0, 1.0),
+        (1.0, 1.0, 1.0),
+        (10.0, 0.1, 1.0),
+        (100.0, 0.0001, 1.0),
+        (0.9999, 2.0, 1.0),
+    ];
+
+    for (seed, (alpha, s, m)) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::Frechet::new(m, s, alpha).unwrap();
+        test_continuous(seed as u64, dist, |x| cdf(x, alpha, s, m));
+    }
+}
+
+#[test]
+fn zeta() {
+    fn cdf(k: i64, s: f64) -> f64 {
+        use common::spec_func::zeta_func;
+        if k < 1 {
+            return 0.0;
+        }
+
+        gen_harmonic(k as u64, s) / zeta_func(s)
+    }
+
+    let parameters = [2.0, 3.7, 5.0, 100.0];
+
+    for (seed, s) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::Zeta::new(s).unwrap();
+        test_discrete(seed as u64, dist, |k| cdf(k, s));
+    }
+}
+
+#[test]
+fn zipf() {
+    fn cdf(k: i64, n: u64, s: f64) -> f64 {
+        if k < 1 {
+            return 0.0;
+        }
+        if k > n as i64 {
+            return 1.0;
+        }
+        gen_harmonic(k as u64, s) / gen_harmonic(n, s)
+    }
+
+    let parameters = [(1000, 1.0), (500, 2.0), (1000, 0.5)];
+
+    for (seed, (n, x)) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::Zipf::new(n, x).unwrap();
+        test_discrete(seed as u64, dist, |k| cdf(k, n, x));
+    }
+}
+
+#[test]
+fn gamma() {
+    fn cdf(x: f64, shape: f64, scale: f64) -> f64 {
+        if x < 0.0 {
+            return 0.0;
+        }
+
+        (x / scale).inc_gamma(shape)
+    }
+
+    let parameters = [
+        (0.5, 2.0),
+        (1.0, 1.0),
+        (10.0, 0.1),
+        (100.0, 0.0001),
+        (0.9999, 2.0),
+    ];
+
+    for (seed, (shape, scale)) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::Gamma::new(shape, scale).unwrap();
+        test_continuous(seed as u64, dist, |x| cdf(x, shape, scale));
+    }
+}
+
+#[test]
+fn chi_squared() {
+    fn cdf(x: f64, k: f64) -> f64 {
+        if x < 0.0 {
+            return 0.0;
+        }
+
+        (x / 2.0).inc_gamma(k / 2.0)
+    }
+
+    let parameters = [0.1, 1.0, 2.0, 10.0, 100.0, 1000.0];
+
+    for (seed, k) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::ChiSquared::new(k).unwrap();
+        test_continuous(seed as u64, dist, |x| cdf(x, k));
+    }
+}
+#[test]
+fn studend_t() {
+    fn cdf(x: f64, df: f64) -> f64 {
+        let h = df / (df + x.powi(2));
+        let ib = 0.5 * h.inc_beta(df / 2.0, 0.5, 0.5.ln_beta(df / 2.0));
+        if x < 0.0 {
+            ib
+        } else {
+            1.0 - ib
+        }
+    }
+
+    let parameters = [1.0, 10.0, 50.0];
+
+    for (seed, df) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::StudentT::new(df).unwrap();
+        test_continuous(seed as u64, dist, |x| cdf(x, df));
+    }
+}
+
+#[test]
+fn fisher_f() {
+    fn cdf(x: f64, m: f64, n: f64) -> f64 {
+        if (m == 1.0 && x <= 0.0) || x < 0.0 {
+            0.0
+        } else {
+            let k = m * x / (m * x + n);
+            let d1 = m / 2.0;
+            let d2 = n / 2.0;
+            k.inc_beta(d1, d2, d1.ln_beta(d2))
+        }
+    }
+
+    let parameters = [(1.0, 1.0), (1.0, 2.0), (2.0, 1.0), (50.0, 1.0)];
+
+    for (seed, (m, n)) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::FisherF::new(m, n).unwrap();
+        test_continuous(seed as u64, dist, |x| cdf(x, m, n));
+    }
+}
+
+#[test]
+fn beta() {
+    fn cdf(x: f64, alpha: f64, beta: f64) -> f64 {
+        if x < 0.0 {
+            return 0.0;
+        }
+        if x > 1.0 {
+            return 1.0;
+        }
+        let ln_beta_ab = alpha.ln_beta(beta);
+        x.inc_beta(alpha, beta, ln_beta_ab)
+    }
+
+    let parameters = [(0.5, 0.5), (2.0, 3.5), (10.0, 1.0), (100.0, 50.0)];
+
+    for (seed, (alpha, beta)) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::Beta::new(alpha, beta).unwrap();
+        test_continuous(seed as u64, dist, |x| cdf(x, alpha, beta));
+    }
+}
+
+#[test]
+fn triangular() {
+    fn cdf(x: f64, a: f64, b: f64, c: f64) -> f64 {
+        if x <= a {
+            0.0
+        } else if a < x && x <= c {
+            (x - a).powi(2) / ((b - a) * (c - a))
+        } else if c < x && x < b {
+            1.0 - (b - x).powi(2) / ((b - a) * (b - c))
+        } else {
+            1.0
+        }
+    }
+
+    let parameters = [
+        (0.0, 1.0, 0.0001),
+        (0.0, 1.0, 0.9999),
+        (0.0, 1.0, 0.5),
+        (0.0, 100.0, 50.0),
+        (-100.0, 100.0, 0.0),
+    ];
+
+    for (seed, (a, b, c)) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::Triangular::new(a, b, c).unwrap();
+        test_continuous(seed as u64, dist, |x| cdf(x, a, b, c));
+    }
+}
+
 fn binomial_cdf(k: i64, p: f64, n: u64) -> f64 {
     if k < 0 {
         return 0.0;
@@ -195,3 +557,327 @@ fn binomial() {
         });
     }
 }
+
+#[test]
+fn geometric() {
+    fn cdf(k: i64, p: f64) -> f64 {
+        if k < 0 {
+            0.0
+        } else {
+            1.0 - (1.0 - p).powi(1 + k as i32)
+        }
+    }
+
+    let parameters = [0.3, 0.5, 0.7, 0.0000001, 0.9999];
+
+    for (seed, p) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::Geometric::new(p).unwrap();
+        test_discrete(seed as u64, dist, |k| cdf(k, p));
+    }
+}
+
+#[test]
+fn hypergeometric() {
+    fn cdf(x: i64, n: u64, k: u64, n_: u64) -> f64 {
+        let min = if n_ + k > n { n_ + k - n } else { 0 };
+        let max = k.min(n_);
+        if x < min as i64 {
+            return 0.0;
+        } else if x >= max as i64 {
+            return 1.0;
+        }
+
+        (min..x as u64 + 1).fold(0.0, |acc, k_| {
+            acc + (ln_binomial(k, k_) + ln_binomial(n - k, n_ - k_) - ln_binomial(n, n_)).exp()
+        })
+    }
+
+    let parameters = [
+        (15, 13, 10),
+        (25, 15, 5),
+        (60, 10, 7),
+        (70, 20, 50),
+        (100, 50, 10),
+        // (100, 50, 49), // Fail case
+    ];
+
+    for (seed, (n, k, n_)) in parameters.into_iter().enumerate() {
+        let dist = rand_distr::Hypergeometric::new(n, k, n_).unwrap();
+        test_discrete(seed as u64, dist, |x| cdf(x, n, k, n_));
+    }
+}
+
+#[test]
+fn poisson() {
+    use rand_distr::Poisson;
+    fn cdf(k: i64, lambda: f64) -> f64 {
+        use common::spec_func::gamma_lr;
+
+        if k < 0 || lambda <= 0.0 {
+            return 0.0;
+        }
+
+        1.0 - gamma_lr(k as f64 + 1.0, lambda)
+    }
+    let parameters = [
+        0.1, 1.0, 7.5,
+        // 1e9, passed case but too slow
+        // 1.844E+19,  // fail case
+    ];
+
+    for (seed, lambda) in parameters.into_iter().enumerate() {
+        let dist = Poisson::new(lambda).unwrap();
+        test_discrete::<f64, Poisson<f64>, _>(seed as u64, dist, |k| cdf(k, lambda));
+    }
+}
+
+fn ln_factorial(n: u64) -> f64 {
+    (n as f64 + 1.0).lgamma().0
+}
+
+fn ln_binomial(n: u64, k: u64) -> f64 {
+    ln_factorial(n) - ln_factorial(k) - ln_factorial(n - k)
+}
+
+fn gen_harmonic(n: u64, m: f64) -> f64 {
+    match n {
+        0 => 1.0,
+        _ => (0..n).fold(0.0, |acc, x| acc + (x as f64 + 1.0).powf(-m)),
+    }
+}
+
+/// [1] Patefield, M. (2000). Fast and Accurate Calculation of Owen’s T Function.
+///     Journal of Statistical Software, 5(5), 1–25.
+///     https://doi.org/10.18637/jss.v005.i05
+///
+/// This function is ported to Rust from the Fortran code provided in the paper
+fn owen_t(h: f64, a: f64) -> f64 {
+    let absh = h.abs();
+    let absa = a.abs();
+    let ah = absa * absh;
+
+    let mut t;
+    if absa <= 1.0 {
+        t = tf(absh, absa, ah);
+    } else if absh <= 0.67 {
+        t = 0.25 - znorm1(absh) * znorm1(ah) - tf(ah, 1.0 / absa, absh);
+    } else {
+        let normh = znorm2(absh);
+        let normah = znorm2(ah);
+        t = 0.5 * (normh + normah) - normh * normah - tf(ah, 1.0 / absa, absh);
+    }
+
+    if a < 0.0 {
+        t = -t;
+    }
+
+    fn tf(h: f64, a: f64, ah: f64) -> f64 {
+        let rtwopi = 0.159_154_943_091_895_35;
+        let rrtpi = 0.398_942_280_401_432_7;
+
+        let c2 = [
+            0.999_999_999_999_999_9,
+            -0.999_999_999_999_888,
+            0.999_999_999_982_907_5,
+            -0.999_999_998_962_825,
+            0.999_999_966_604_593_7,
+            -0.999_999_339_862_724_7,
+            0.999_991_256_111_369_6,
+            -0.999_917_776_244_633_8,
+            0.999_428_355_558_701_4,
+            -0.996_973_117_207_23,
+            0.987_514_480_372_753,
+            -0.959_158_579_805_728_8,
+            0.892_463_055_110_067_1,
+            -0.768_934_259_904_64,
+            0.588_935_284_684_846_9,
+            -0.383_803_451_604_402_55,
+            0.203_176_017_010_453,
+            -8.281_363_160_700_499e-2,
+            2.416_798_473_575_957_8e-2,
+            -4.467_656_666_397_183e-3,
+            3.914_116_940_237_383_6e-4,
+        ];
+
+        let pts = [
+            3.508_203_967_645_171_6e-3,
+            3.127_904_233_803_075_6e-2,
+            8.526_682_628_321_945e-2,
+            0.162_450_717_308_122_77,
+            0.258_511_960_491_254_36,
+            0.368_075_538_406_975_3,
+            0.485_010_929_056_047,
+            0.602_775_141_526_185_7,
+            0.714_778_842_177_532_3,
+            0.814_755_109_887_601,
+            0.897_110_297_559_489_7,
+            0.957_238_080_859_442_6,
+            0.991_788_329_746_297,
+        ];
+
+        let wts = [
+            1.883_143_811_532_350_3e-2,
+            1.856_708_624_397_765e-2,
+            1.804_209_346_122_338_5e-2,
+            1.726_382_960_639_875_2e-2,
+            1.624_321_997_598_985_8e-2,
+            1.499_459_203_411_670_5e-2,
+            1.353_547_446_966_209e-2,
+            1.188_635_160_582_016_5e-2,
+            1.007_037_724_277_743_2e-2,
+            8.113_054_574_229_958e-3,
+            6.041_900_952_847_024e-3,
+            3.886_221_701_074_205_7e-3,
+            1.679_303_108_454_609e-3,
+        ];
+
+        let hrange = [
+            0.02, 0.06, 0.09, 0.125, 0.26, 0.4, 0.6, 1.6, 1.7, 2.33, 2.4, 3.36, 3.4, 4.8,
+        ];
+        let arange = [0.025, 0.09, 0.15, 0.36, 0.5, 0.9, 0.99999];
+
+        let select = [
+            [1, 1, 2, 13, 13, 13, 13, 13, 13, 13, 13, 16, 16, 16, 9],
+            [1, 2, 2, 3, 3, 5, 5, 14, 14, 15, 15, 16, 16, 16, 9],
+            [2, 2, 3, 3, 3, 5, 5, 15, 15, 15, 15, 16, 16, 16, 10],
+            [2, 2, 3, 5, 5, 5, 5, 7, 7, 16, 16, 16, 16, 16, 10],
+            [2, 3, 3, 5, 5, 6, 6, 8, 8, 17, 17, 17, 12, 12, 11],
+            [2, 3, 5, 5, 5, 6, 6, 8, 8, 17, 17, 17, 12, 12, 12],
+            [2, 3, 4, 4, 6, 6, 8, 8, 17, 17, 17, 17, 17, 12, 12],
+            [2, 3, 4, 4, 6, 6, 18, 18, 18, 18, 17, 17, 17, 12, 12],
+        ];
+
+        let ihint = hrange.iter().position(|&r| h < r).unwrap_or(14);
+
+        let iaint = arange.iter().position(|&r| a < r).unwrap_or(7);
+
+        let icode = select[iaint][ihint];
+        let m = [
+            2, 3, 4, 5, 7, 10, 12, 18, 10, 20, 30, 20, 4, 7, 8, 20, 13, 0,
+        ][icode - 1];
+        let method = [1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4, 4, 4, 4, 5, 6][icode - 1];
+
+        match method {
+            1 => {
+                let hs = -0.5 * h * h;
+                let dhs = hs.exp();
+                let as_ = a * a;
+                let mut j = 1;
+                let mut jj = 1;
+                let mut aj = rtwopi * a;
+                let mut tf = rtwopi * a.atan();
+                let mut dj = dhs - 1.0;
+                let mut gj = hs * dhs;
+                loop {
+                    tf += dj * aj / (jj as f64);
+                    if j >= m {
+                        return tf;
+                    }
+                    j += 1;
+                    jj += 2;
+                    aj *= as_;
+                    dj = gj - dj;
+                    gj *= hs / (j as f64);
+                }
+            }
+            2 => {
+                let maxii = m + m + 1;
+                let mut ii = 1;
+                let mut tf = 0.0;
+                let hs = h * h;
+                let as_ = -a * a;
+                let mut vi = rrtpi * a * (-0.5 * ah * ah).exp();
+                let mut z = znorm1(ah) / h;
+                let y = 1.0 / hs;
+                loop {
+                    tf += z;
+                    if ii >= maxii {
+                        tf *= rrtpi * (-0.5 * hs).exp();
+                        return tf;
+                    }
+                    z = y * (vi - (ii as f64) * z);
+                    vi *= as_;
+                    ii += 2;
+                }
+            }
+            3 => {
+                let mut i = 1;
+                let mut ii = 1;
+                let mut tf = 0.0;
+                let hs = h * h;
+                let as_ = a * a;
+                let mut vi = rrtpi * a * (-0.5 * ah * ah).exp();
+                let mut zi = znorm1(ah) / h;
+                let y = 1.0 / hs;
+                loop {
+                    tf += zi * c2[i - 1];
+                    if i > m {
+                        tf *= rrtpi * (-0.5 * hs).exp();
+                        return tf;
+                    }
+                    zi = y * ((ii as f64) * zi - vi);
+                    vi *= as_;
+                    i += 1;
+                    ii += 2;
+                }
+            }
+            4 => {
+                let maxii = m + m + 1;
+                let mut ii = 1;
+                let mut tf = 0.0;
+                let hs = h * h;
+                let as_ = -a * a;
+                let mut ai = rtwopi * a * (-0.5 * hs * (1.0 - as_)).exp();
+                let mut yi = 1.0;
+                loop {
+                    tf += ai * yi;
+                    if ii >= maxii {
+                        return tf;
+                    }
+                    ii += 2;
+                    yi = (1.0 - hs * yi) / (ii as f64);
+                    ai *= as_;
+                }
+            }
+            5 => {
+                let mut tf = 0.0;
+                let as_ = a * a;
+                let hs = -0.5 * h * h;
+                for i in 0..m {
+                    let r = 1.0 + as_ * pts[i];
+                    tf += wts[i] * (hs * r).exp() / r;
+                }
+                tf *= a;
+                tf
+            }
+            6 => {
+                let normh = znorm2(h);
+                let mut tf = 0.5 * normh * (1.0 - normh);
+                let y = 1.0 - a;
+                let r = (y / (1.0 + a)).atan();
+                if r != 0.0 {
+                    tf -= rtwopi * r * (-0.5 * y * h * h / r).exp();
+                }
+                tf
+            }
+            _ => 0.0,
+        }
+    }
+
+    // P(0 ≤ Z ≤ x)
+    fn znorm1(x: f64) -> f64 {
+        phi(x) - 0.5
+    }
+
+    // P(x ≤ Z < ∞)
+    fn znorm2(x: f64) -> f64 {
+        1.0 - phi(x)
+    }
+
+    t
+}
+
+/// standard normal cdf
+fn phi(x: f64) -> f64 {
+    normal_cdf(x, 0.0, 1.0)
+}
diff --git a/rand_distr/tests/common/mod.rs b/rand_distr/tests/common/mod.rs
new file mode 100644
index 0000000000..4fa8db7de6
--- /dev/null
+++ b/rand_distr/tests/common/mod.rs
@@ -0,0 +1 @@
+pub mod spec_func;
diff --git a/rand_distr/tests/common/spec_func.rs b/rand_distr/tests/common/spec_func.rs
new file mode 100644
index 0000000000..bd39a79fb4
--- /dev/null
+++ b/rand_distr/tests/common/spec_func.rs
@@ -0,0 +1,155 @@
+// MIT License
+
+// Copyright (c) 2016 Michael Ma
+
+// Permission is hereby granted, free of charge, to any person obtaining a copy
+// of this software and associated documentation files (the "Software"), to deal
+// in the Software without restriction, including without limitation the rights
+// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the Software is
+// furnished to do so, subject to the following conditions:
+
+// The above copyright notice and this permission notice shall be included in all
+// copies or substantial portions of the Software.
+
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+// SOFTWARE.
+
+/// https://docs.rs/statrs/latest/src/statrs/function/gamma.rs.html#260-347
+use special::Primitive;
+
+pub fn gamma_lr(a: f64, x: f64) -> f64 {
+    if a.is_nan() || x.is_nan() {
+        return f64::NAN;
+    }
+
+    if a <= 0.0 || a == f64::INFINITY {
+        panic!("a must be positive and finite");
+    }
+    if x <= 0.0 || x == f64::INFINITY {
+        panic!("x must be positive and finite");
+    }
+
+    const ACC: f64 = 0.0000000000000011102230246251565;
+
+    if a.abs() < ACC {
+        return 1.0;
+    }
+
+    if x.abs() < ACC {
+        return 0.0;
+    }
+
+    let eps = 0.000000000000001;
+    let big = 4503599627370496.0;
+    let big_inv = 2.220_446_049_250_313e-16;
+
+    let ax = a * x.ln() - x - a.lgamma().0;
+    if ax < -709.782_712_893_384 {
+        if a < x {
+            return 1.0;
+        }
+        return 0.0;
+    }
+    if x <= 1.0 || x <= a {
+        let mut r2 = a;
+        let mut c2 = 1.0;
+        let mut ans2 = 1.0;
+        loop {
+            r2 += 1.0;
+            c2 *= x / r2;
+            ans2 += c2;
+
+            if c2 / ans2 <= eps {
+                break;
+            }
+        }
+        return ax.exp() * ans2 / a;
+    }
+
+    let mut y = 1.0 - a;
+    let mut z = x + y + 1.0;
+    let mut c = 0;
+
+    let mut p3 = 1.0;
+    let mut q3 = x;
+    let mut p2 = x + 1.0;
+    let mut q2 = z * x;
+    let mut ans = p2 / q2;
+
+    loop {
+        y += 1.0;
+        z += 2.0;
+        c += 1;
+        let yc = y * f64::from(c);
+
+        let p = p2 * z - p3 * yc;
+        let q = q2 * z - q3 * yc;
+
+        p3 = p2;
+        p2 = p;
+        q3 = q2;
+        q2 = q;
+
+        if p.abs() > big {
+            p3 *= big_inv;
+            p2 *= big_inv;
+            q3 *= big_inv;
+            q2 *= big_inv;
+        }
+
+        if q != 0.0 {
+            let nextans = p / q;
+            let error = ((ans - nextans) / nextans).abs();
+            ans = nextans;
+
+            if error <= eps {
+                break;
+            }
+        }
+    }
+    1.0 - ax.exp() * ans
+}
+
+/// https://docs.rs/spfunc/latest/src/spfunc/zeta.rs.html#20-43
+pub fn zeta_func(s: f64) -> f64 {
+    fn update_akn(akn: &mut Vec<f64>, s: f64) {
+        let n = akn.len() - 1;
+
+        let n1 = n as f64 + 1.0;
+
+        akn.iter_mut().enumerate().for_each(|(k, a)| {
+            let num = n1;
+            let den = n + 1 - k;
+            *a *= num / den as f64;
+        });
+        let p1 = -1.0 / n1;
+        let p2 = (n1 / (n1 + 1.0)).powf(s);
+
+        akn.push(p1 * p2 * akn[n]);
+    }
+
+    if s == 1.0 {
+        return f64::INFINITY;
+    }
+
+    let mut akn = vec![1.0];
+    let mut two_pow = 0.5;
+    let head = 1.0 / (1.0 - 2.0.powf(1.0 - s));
+    let mut tail_prev = 0.0;
+    let mut tail = two_pow * akn[0];
+
+    while (tail - tail_prev).abs() >= f64::EPSILON {
+        update_akn(&mut akn, s);
+        two_pow /= 2.0;
+        tail_prev = tail;
+        tail += two_pow * akn.iter().sum::<f64>();
+    }
+
+    head * tail
+}