Update nitrokey crate to 0.4.0

This change finally updates the version of the nitrokey crate that we
consume to 0.4.0. Along with that we update rand_core, one of its
dependencies, to 0.5.1. Further more we add cfg-if in version 0.1.10 and
getrandom in version 0.1.13, both of which are now new (non-development)
dependencies.

Import subrepo nitrokey/:nitrokey at e81057037e9b4f370b64c0a030a725bc6bdfb870
Import subrepo cfg-if/:cfg-if at 4484a6faf816ff8058088ad857b0c6bb2f4b02b2
Import subrepo getrandom/:getrandom at d661aa7e1b8cc80b47dabe3d2135b3b47d2858af
Import subrepo rand/:rand at d877ed528248b52d947e0484364a4e1ae59ca502

18 files changed, 3092 insertions, 0 deletions

diff --git a/rand/rand_distr/src/binomial.rs b/rand/rand_distr/src/binomial.rs
new file mode 100644
index 0000000..0e6bf9a
--- /dev/null
+++ b/rand/rand_distr/src/binomial.rs
@@ -0,0 +1,329 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2016-2017 The Rust Project Developers.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The binomial distribution.
+
+use rand::Rng;
+use crate::{Distribution, Uniform};
+
+/// The binomial distribution `Binomial(n, p)`.
+///
+/// This distribution has density function:
+/// `f(k) = n!/(k! (n-k)!) p^k (1-p)^(n-k)` for `k >= 0`.
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{Binomial, Distribution};
+///
+/// let bin = Binomial::new(20, 0.3).unwrap();
+/// let v = bin.sample(&mut rand::thread_rng());
+/// println!("{} is from a binomial distribution", v);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct Binomial {
+    /// Number of trials.
+    n: u64,
+    /// Probability of success.
+    p: f64,
+}
+
+/// Error type returned from `Binomial::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum Error {
+    /// `p < 0` or `nan`.
+    ProbabilityTooSmall,
+    /// `p > 1`.
+    ProbabilityTooLarge,
+}
+
+impl Binomial {
+    /// Construct a new `Binomial` with the given shape parameters `n` (number
+    /// of trials) and `p` (probability of success).
+    pub fn new(n: u64, p: f64) -> Result<Binomial, Error> {
+        if !(p >= 0.0) {
+            return Err(Error::ProbabilityTooSmall);
+        }
+        if !(p <= 1.0) {
+            return Err(Error::ProbabilityTooLarge);
+        }
+        Ok(Binomial { n, p })
+    }
+}
+
+/// Convert a `f64` to an `i64`, panicing on overflow.
+// In the future (Rust 1.34), this might be replaced with `TryFrom`.
+fn f64_to_i64(x: f64) -> i64 {
+    assert!(x < (::std::i64::MAX as f64));
+    x as i64
+}
+
+impl Distribution<u64> for Binomial {
+    #[allow(clippy::many_single_char_names)]  // Same names as in the reference.
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64 {
+        // Handle these values directly.
+        if self.p == 0.0 {
+            return 0;
+        } else if self.p == 1.0 {
+            return self.n;
+        }
+
+        // The binomial distribution is symmetrical with respect to p -> 1-p,
+        // k -> n-k switch p so that it is less than 0.5 - this allows for lower
+        // expected values we will just invert the result at the end
+        let p = if self.p <= 0.5 {
+            self.p
+        } else {
+            1.0 - self.p
+        };
+
+        let result;
+        let q = 1. - p;
+
+        // For small n * min(p, 1 - p), the BINV algorithm based on the inverse
+        // transformation of the binomial distribution is efficient. Otherwise,
+        // the BTPE algorithm is used.
+        //
+        // Voratas Kachitvichyanukul and Bruce W. Schmeiser. 1988. Binomial
+        // random variate generation. Commun. ACM 31, 2 (February 1988),
+        // 216-222. http://dx.doi.org/10.1145/42372.42381
+
+        // Threshold for prefering the BINV algorithm. The paper suggests 10,
+        // Ranlib uses 30, and GSL uses 14.
+        const BINV_THRESHOLD: f64 = 10.;
+
+        if (self.n as f64) * p < BINV_THRESHOLD &&
+           self.n <= (::std::i32::MAX as u64) {
+            // Use the BINV algorithm.
+            let s = p / q;
+            let a = ((self.n + 1) as f64) * s;
+            let mut r = q.powi(self.n as i32);
+            let mut u: f64 = rng.gen();
+            let mut x = 0;
+            while u > r as f64 {
+                u -= r;
+                x += 1;
+                r *= a / (x as f64) - s;
+            }
+            result = x;
+        } else {
+            // Use the BTPE algorithm.
+
+            // Threshold for using the squeeze algorithm. This can be freely
+            // chosen based on performance. Ranlib and GSL use 20.
+            const SQUEEZE_THRESHOLD: i64 = 20;
+
+            // Step 0: Calculate constants as functions of `n` and `p`.
+            let n = self.n as f64;
+            let np = n * p;
+            let npq = np * q;
+            let f_m = np + p;
+            let m = f64_to_i64(f_m);
+            // radius of triangle region, since height=1 also area of region
+            let p1 = (2.195 * npq.sqrt() - 4.6 * q).floor() + 0.5;
+            // tip of triangle
+            let x_m = (m as f64) + 0.5;
+            // left edge of triangle
+            let x_l = x_m - p1;
+            // right edge of triangle
+            let x_r = x_m + p1;
+            let c = 0.134 + 20.5 / (15.3 + (m as f64));
+            // p1 + area of parallelogram region
+            let p2 = p1 * (1. + 2. * c);
+
+            fn lambda(a: f64) -> f64 {
+                a * (1. + 0.5 * a)
+            }
+
+            let lambda_l = lambda((f_m - x_l) / (f_m - x_l * p));
+            let lambda_r = lambda((x_r - f_m) / (x_r * q));
+            // p1 + area of left tail
+            let p3 = p2 + c / lambda_l;
+            // p1 + area of right tail
+            let p4 = p3 + c / lambda_r;
+
+            // return value
+            let mut y: i64;
+
+            let gen_u = Uniform::new(0., p4);
+            let gen_v = Uniform::new(0., 1.);
+
+            loop {
+                // Step 1: Generate `u` for selecting the region. If region 1 is
+                // selected, generate a triangularly distributed variate.
+                let u = gen_u.sample(rng);
+                let mut v = gen_v.sample(rng);
+                if !(u > p1) {
+                    y = f64_to_i64(x_m - p1 * v + u);
+                    break;
+                }
+
+                if !(u > p2) {
+                    // Step 2: Region 2, parallelograms. Check if region 2 is
+                    // used. If so, generate `y`.
+                    let x = x_l + (u - p1) / c;
+                    v = v * c + 1.0 - (x - x_m).abs() / p1;
+                    if v > 1. {
+                        continue;
+                    } else {
+                        y = f64_to_i64(x);
+                    }
+                } else if !(u > p3) {
+                    // Step 3: Region 3, left exponential tail.
+                    y = f64_to_i64(x_l + v.ln() / lambda_l);
+                    if y < 0 {
+                        continue;
+                    } else {
+                        v *= (u - p2) * lambda_l;
+                    }
+                } else {
+                    // Step 4: Region 4, right exponential tail.
+                    y = f64_to_i64(x_r - v.ln() / lambda_r);
+                    if y > 0 && (y as u64) > self.n {
+                        continue;
+                    } else {
+                        v *= (u - p3) * lambda_r;
+                    }
+                }
+
+                // Step 5: Acceptance/rejection comparison.
+
+                // Step 5.0: Test for appropriate method of evaluating f(y).
+                let k = (y - m).abs();
+                if !(k > SQUEEZE_THRESHOLD && (k as f64) < 0.5 * npq - 1.) {
+                    // Step 5.1: Evaluate f(y) via the recursive relationship. Start the
+                    // search from the mode.
+                    let s = p / q;
+                    let a = s * (n + 1.);
+                    let mut f = 1.0;
+                    if m < y {
+                        let mut i = m;
+                        loop {
+                            i += 1;
+                            f *= a / (i as f64) - s;
+                            if i == y {
+                                break;
+                            }
+                        }
+                    } else if m > y {
+                        let mut i = y;
+                        loop {
+                            i += 1;
+                            f /= a / (i as f64) - s;
+                            if i == m {
+                                break;
+                            }
+                        }
+                    }
+                    if v > f {
+                        continue;
+                    } else {
+                        break;
+                    }
+                }
+
+                // Step 5.2: Squeezing. Check the value of ln(v) againts upper and
+                // lower bound of ln(f(y)).
+                let k = k as f64;
+                let rho = (k / npq) * ((k * (k / 3. + 0.625) + 1./6.) / npq + 0.5);
+                let t = -0.5 * k*k / npq;
+                let alpha = v.ln();
+                if alpha < t - rho {
+                    break;
+                }
+                if alpha > t + rho {
+                    continue;
+                }
+
+                // Step 5.3: Final acceptance/rejection test.
+                let x1 = (y + 1) as f64;
+                let f1 = (m + 1) as f64;
+                let z = (f64_to_i64(n) + 1 - m) as f64;
+                let w = (f64_to_i64(n) - y + 1) as f64;
+
+                fn stirling(a: f64) -> f64 {
+                    let a2 = a * a;
+                    (13860. - (462. - (132. - (99. - 140. / a2) / a2) / a2) / a2) / a / 166320.
+                }
+
+                if alpha > x_m * (f1 / x1).ln()
+                    + (n - (m as f64) + 0.5) * (z / w).ln()
+                    + ((y - m) as f64) * (w * p / (x1 * q)).ln()
+                    // We use the signs from the GSL implementation, which are
+                    // different than the ones in the reference. According to
+                    // the GSL authors, the new signs were verified to be
+                    // correct by one of the original designers of the
+                    // algorithm.
+                    + stirling(f1) + stirling(z) - stirling(x1) - stirling(w)
+                {
+                    continue;
+                }
+
+                break;
+            }
+            assert!(y >= 0);
+            result = y as u64;
+        }
+
+        // Invert the result for p < 0.5.
+        if p != self.p {
+            self.n - result
+        } else {
+            result
+        }
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use rand::Rng;
+    use crate::Distribution;
+    use super::Binomial;
+
+    fn test_binomial_mean_and_variance<R: Rng>(n: u64, p: f64, rng: &mut R) {
+        let binomial = Binomial::new(n, p).unwrap();
+
+        let expected_mean = n as f64 * p;
+        let expected_variance = n as f64 * p * (1.0 - p);
+
+        let mut results = [0.0; 1000];
+        for i in results.iter_mut() { *i = binomial.sample(rng) as f64; }
+
+        let mean = results.iter().sum::<f64>() / results.len() as f64;
+        assert!((mean as f64 - expected_mean).abs() < expected_mean / 50.0);
+
+        let variance =
+            results.iter().map(|x| (x - mean) * (x - mean)).sum::<f64>()
+            / results.len() as f64;
+        assert!((variance - expected_variance).abs() < expected_variance / 10.0);
+    }
+
+    #[test]
+    fn test_binomial() {
+        let mut rng = crate::test::rng(351);
+        test_binomial_mean_and_variance(150, 0.1, &mut rng);
+        test_binomial_mean_and_variance(70, 0.6, &mut rng);
+        test_binomial_mean_and_variance(40, 0.5, &mut rng);
+        test_binomial_mean_and_variance(20, 0.7, &mut rng);
+        test_binomial_mean_and_variance(20, 0.5, &mut rng);
+    }
+
+    #[test]
+    fn test_binomial_end_points() {
+        let mut rng = crate::test::rng(352);
+        assert_eq!(rng.sample(Binomial::new(20, 0.0).unwrap()), 0);
+        assert_eq!(rng.sample(Binomial::new(20, 1.0).unwrap()), 20);
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_binomial_invalid_lambda_neg() {
+        Binomial::new(20, -10.0).unwrap();
+    }
+}
diff --git a/rand/rand_distr/src/cauchy.rs b/rand/rand_distr/src/cauchy.rs
new file mode 100644
index 0000000..6b0e7c6
--- /dev/null
+++ b/rand/rand_distr/src/cauchy.rs
@@ -0,0 +1,120 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2016-2017 The Rust Project Developers.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The Cauchy distribution.
+
+use rand::Rng;
+use crate::{Distribution, Standard};
+use crate::utils::Float;
+
+/// The Cauchy distribution `Cauchy(median, scale)`.
+///
+/// This distribution has a density function:
+/// `f(x) = 1 / (pi * scale * (1 + ((x - median) / scale)^2))`
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{Cauchy, Distribution};
+///
+/// let cau = Cauchy::new(2.0, 5.0).unwrap();
+/// let v = cau.sample(&mut rand::thread_rng());
+/// println!("{} is from a Cauchy(2, 5) distribution", v);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct Cauchy<N> {
+    median: N,
+    scale: N,
+}
+
+/// Error type returned from `Cauchy::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum Error {
+    /// `scale <= 0` or `nan`.
+    ScaleTooSmall,
+}
+
+impl<N: Float> Cauchy<N>
+where Standard: Distribution<N>
+{
+    /// Construct a new `Cauchy` with the given shape parameters
+    /// `median` the peak location and `scale` the scale factor.
+    pub fn new(median: N, scale: N) -> Result<Cauchy<N>, Error> {
+        if !(scale > N::from(0.0)) {
+            return Err(Error::ScaleTooSmall);
+        }
+        Ok(Cauchy {
+            median,
+            scale
+        })
+    }
+}
+
+impl<N: Float> Distribution<N> for Cauchy<N>
+where Standard: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        // sample from [0, 1)
+        let x = Standard.sample(rng);
+        // get standard cauchy random number
+        // note that π/2 is not exactly representable, even if x=0.5 the result is finite
+        let comp_dev = (N::pi() * x).tan();
+        // shift and scale according to parameters
+        self.median + self.scale * comp_dev
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use crate::Distribution;
+    use super::Cauchy;
+
+    fn median(mut numbers: &mut [f64]) -> f64 {
+        sort(&mut numbers);
+        let mid = numbers.len() / 2;
+        numbers[mid]
+    }
+
+    fn sort(numbers: &mut [f64]) {
+        numbers.sort_by(|a, b| a.partial_cmp(b).unwrap());
+    }
+
+    #[test]
+    fn test_cauchy_averages() {
+        // NOTE: given that the variance and mean are undefined,
+        // this test does not have any rigorous statistical meaning.
+        let cauchy = Cauchy::new(10.0, 5.0).unwrap();
+        let mut rng = crate::test::rng(123);
+        let mut numbers: [f64; 1000] = [0.0; 1000];
+        let mut sum = 0.0;
+        for i in 0..1000 {
+            numbers[i] = cauchy.sample(&mut rng);
+            sum += numbers[i];
+        }
+        let median = median(&mut numbers);
+        println!("Cauchy median: {}", median);
+        assert!((median - 10.0).abs() < 0.4); // not 100% certain, but probable enough
+        let mean = sum / 1000.0;
+        println!("Cauchy mean: {}", mean);
+        // for a Cauchy distribution the mean should not converge
+        assert!((mean - 10.0).abs() > 0.4); // not 100% certain, but probable enough
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_cauchy_invalid_scale_zero() {
+        Cauchy::new(0.0, 0.0).unwrap();
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_cauchy_invalid_scale_neg() {
+        Cauchy::new(0.0, -10.0).unwrap();
+    }
+}
diff --git a/rand/rand_distr/src/dirichlet.rs b/rand/rand_distr/src/dirichlet.rs
new file mode 100644
index 0000000..71cf73c
--- /dev/null
+++ b/rand/rand_distr/src/dirichlet.rs
@@ -0,0 +1,154 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 The Rust Project Developers.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The dirichlet distribution.
+
+use rand::Rng;
+use crate::{Distribution, Gamma, StandardNormal, Exp1, Open01};
+use crate::utils::Float;
+
+/// The dirichelet distribution `Dirichlet(alpha)`.
+///
+/// The Dirichlet distribution is a family of continuous multivariate
+/// probability distributions parameterized by a vector alpha of positive reals.
+/// It is a multivariate generalization of the beta distribution.
+///
+/// # Example
+///
+/// ```
+/// use rand::prelude::*;
+/// use rand_distr::Dirichlet;
+///
+/// let dirichlet = Dirichlet::new(vec![1.0, 2.0, 3.0]).unwrap();
+/// let samples = dirichlet.sample(&mut rand::thread_rng());
+/// println!("{:?} is from a Dirichlet([1.0, 2.0, 3.0]) distribution", samples);
+/// ```
+#[derive(Clone, Debug)]
+pub struct Dirichlet<N> {
+    /// Concentration parameters (alpha)
+    alpha: Vec<N>,
+}
+
+/// Error type returned from `Dirchlet::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum Error {
+    /// `alpha.len() < 2`.
+    AlphaTooShort,
+    /// `alpha <= 0.0` or `nan`.
+    AlphaTooSmall,
+    /// `size < 2`.
+    SizeTooSmall,
+}
+
+impl<N: Float> Dirichlet<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    /// Construct a new `Dirichlet` with the given alpha parameter `alpha`.
+    ///
+    /// Requires `alpha.len() >= 2`.
+    #[inline]
+    pub fn new<V: Into<Vec<N>>>(alpha: V) -> Result<Dirichlet<N>, Error> {
+        let a = alpha.into();
+        if a.len() < 2 {
+            return Err(Error::AlphaTooShort);
+        }
+        for &ai in &a {
+            if !(ai > N::from(0.0)) {
+                return Err(Error::AlphaTooSmall);
+            }
+        }
+
+        Ok(Dirichlet { alpha: a })
+    }
+
+    /// Construct a new `Dirichlet` with the given shape parameter `alpha` and `size`.
+    ///
+    /// Requires `size >= 2`.
+    #[inline]
+    pub fn new_with_size(alpha: N, size: usize) -> Result<Dirichlet<N>, Error> {
+        if !(alpha > N::from(0.0)) {
+            return Err(Error::AlphaTooSmall);
+        }
+        if size < 2 {
+            return Err(Error::SizeTooSmall);
+        }
+        Ok(Dirichlet {
+            alpha: vec![alpha; size],
+        })
+    }
+}
+
+impl<N: Float> Distribution<Vec<N>> for Dirichlet<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Vec<N> {
+        let n = self.alpha.len();
+        let mut samples = vec![N::from(0.0); n];
+        let mut sum = N::from(0.0);
+
+        for (s, &a) in samples.iter_mut().zip(self.alpha.iter()) {
+            let g = Gamma::new(a, N::from(1.0)).unwrap();
+            *s = g.sample(rng);
+            sum += *s;
+        }
+        let invacc = N::from(1.0) / sum;
+        for s in samples.iter_mut() {
+            *s *= invacc;
+        }
+        samples
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use super::Dirichlet;
+    use crate::Distribution;
+
+    #[test]
+    fn test_dirichlet() {
+        let d = Dirichlet::new(vec![1.0, 2.0, 3.0]).unwrap();
+        let mut rng = crate::test::rng(221);
+        let samples = d.sample(&mut rng);
+        let _: Vec<f64> = samples
+            .into_iter()
+            .map(|x| {
+                assert!(x > 0.0);
+                x
+            })
+            .collect();
+    }
+
+    #[test]
+    fn test_dirichlet_with_param() {
+        let alpha = 0.5f64;
+        let size = 2;
+        let d = Dirichlet::new_with_size(alpha, size).unwrap();
+        let mut rng = crate::test::rng(221);
+        let samples = d.sample(&mut rng);
+        let _: Vec<f64> = samples
+            .into_iter()
+            .map(|x| {
+                assert!(x > 0.0);
+                x
+            })
+            .collect();
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_dirichlet_invalid_length() {
+        Dirichlet::new_with_size(0.5f64, 1).unwrap();
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_dirichlet_invalid_alpha() {
+        Dirichlet::new_with_size(0.0f64, 2).unwrap();
+    }
+}
diff --git a/rand/rand_distr/src/exponential.rs b/rand/rand_distr/src/exponential.rs
new file mode 100644
index 0000000..8322489
--- /dev/null
+++ b/rand/rand_distr/src/exponential.rs
@@ -0,0 +1,145 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 The Rust Project Developers.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The exponential distribution.
+
+use rand::Rng;
+use crate::{ziggurat_tables, Distribution};
+use crate::utils::{ziggurat, Float};
+
+/// Samples floating-point numbers according to the exponential distribution,
+/// with rate parameter `λ = 1`. This is equivalent to `Exp::new(1.0)` or
+/// sampling with `-rng.gen::<f64>().ln()`, but faster.
+///
+/// See `Exp` for the general exponential distribution.
+///
+/// Implemented via the ZIGNOR variant[^1] of the Ziggurat method. The exact
+/// description in the paper was adjusted to use tables for the exponential
+/// distribution rather than normal.
+///
+/// [^1]: Jurgen A. Doornik (2005). [*An Improved Ziggurat Method to
+///       Generate Normal Random Samples*](
+///       https://www.doornik.com/research/ziggurat.pdf).
+///       Nuffield College, Oxford
+///
+/// # Example
+/// ```
+/// use rand::prelude::*;
+/// use rand_distr::Exp1;
+///
+/// let val: f64 = thread_rng().sample(Exp1);
+/// println!("{}", val);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct Exp1;
+
+impl Distribution<f32> for Exp1 {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f32 {
+        // TODO: use optimal 32-bit implementation
+        let x: f64 = self.sample(rng);
+        x as f32
+    }
+}
+
+// This could be done via `-rng.gen::<f64>().ln()` but that is slower.
+impl Distribution<f64> for Exp1 {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        #[inline]
+        fn pdf(x: f64) -> f64 {
+            (-x).exp()
+        }
+        #[inline]
+        fn zero_case<R: Rng + ?Sized>(rng: &mut R, _u: f64) -> f64 {
+            ziggurat_tables::ZIG_EXP_R - rng.gen::<f64>().ln()
+        }
+
+        ziggurat(rng, false,
+                 &ziggurat_tables::ZIG_EXP_X,
+                 &ziggurat_tables::ZIG_EXP_F,
+                 pdf, zero_case)
+    }
+}
+
+/// The exponential distribution `Exp(lambda)`.
+///
+/// This distribution has density function: `f(x) = lambda * exp(-lambda * x)`
+/// for `x > 0`.
+/// 
+/// Note that [`Exp1`](crate::Exp1) is an optimised implementation for `lambda = 1`.
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{Exp, Distribution};
+///
+/// let exp = Exp::new(2.0).unwrap();
+/// let v = exp.sample(&mut rand::thread_rng());
+/// println!("{} is from a Exp(2) distribution", v);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct Exp<N> {
+    /// `lambda` stored as `1/lambda`, since this is what we scale by.
+    lambda_inverse: N
+}
+
+/// Error type returned from `Exp::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum Error {
+    /// `lambda <= 0` or `nan`.
+    LambdaTooSmall,
+}
+
+impl<N: Float> Exp<N>
+where Exp1: Distribution<N>
+{
+    /// Construct a new `Exp` with the given shape parameter
+    /// `lambda`.
+    #[inline]
+    pub fn new(lambda: N) -> Result<Exp<N>, Error> {
+        if !(lambda > N::from(0.0)) {
+            return Err(Error::LambdaTooSmall);
+        }
+        Ok(Exp { lambda_inverse: N::from(1.0) / lambda })
+    }
+}
+
+impl<N: Float> Distribution<N> for Exp<N>
+where Exp1: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        rng.sample(Exp1) * self.lambda_inverse
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use crate::Distribution;
+    use super::Exp;
+
+    #[test]
+    fn test_exp() {
+        let exp = Exp::new(10.0).unwrap();
+        let mut rng = crate::test::rng(221);
+        for _ in 0..1000 {
+            assert!(exp.sample(&mut rng) >= 0.0);
+        }
+    }
+    #[test]
+    #[should_panic]
+    fn test_exp_invalid_lambda_zero() {
+        Exp::new(0.0).unwrap();
+    }
+    #[test]
+    #[should_panic]
+    fn test_exp_invalid_lambda_neg() {
+        Exp::new(-10.0).unwrap();
+    }
+}
diff --git a/rand/rand_distr/src/gamma.rs b/rand/rand_distr/src/gamma.rs
new file mode 100644
index 0000000..4018361
--- /dev/null
+++ b/rand/rand_distr/src/gamma.rs
@@ -0,0 +1,485 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 The Rust Project Developers.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The Gamma and derived distributions.
+
+use self::GammaRepr::*;
+use self::ChiSquaredRepr::*;
+
+use rand::Rng;
+use crate::normal::StandardNormal;
+use crate::{Distribution, Exp1, Exp, Open01};
+use crate::utils::Float;
+
+/// The Gamma distribution `Gamma(shape, scale)` distribution.
+///
+/// The density function of this distribution is
+///
+/// ```text
+/// f(x) =  x^(k - 1) * exp(-x / θ) / (Γ(k) * θ^k)
+/// ```
+///
+/// where `Γ` is the Gamma function, `k` is the shape and `θ` is the
+/// scale and both `k` and `θ` are strictly positive.
+///
+/// The algorithm used is that described by Marsaglia & Tsang 2000[^1],
+/// falling back to directly sampling from an Exponential for `shape
+/// == 1`, and using the boosting technique described in that paper for
+/// `shape < 1`.
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{Distribution, Gamma};
+///
+/// let gamma = Gamma::new(2.0, 5.0).unwrap();
+/// let v = gamma.sample(&mut rand::thread_rng());
+/// println!("{} is from a Gamma(2, 5) distribution", v);
+/// ```
+///
+/// [^1]: George Marsaglia and Wai Wan Tsang. 2000. "A Simple Method for
+///       Generating Gamma Variables" *ACM Trans. Math. Softw.* 26, 3
+///       (September 2000), 363-372.
+///       DOI:[10.1145/358407.358414](https://doi.acm.org/10.1145/358407.358414)
+#[derive(Clone, Copy, Debug)]
+pub struct Gamma<N> {
+    repr: GammaRepr<N>,
+}
+
+/// Error type returned from `Gamma::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum Error {
+    /// `shape <= 0` or `nan`.
+    ShapeTooSmall,
+    /// `scale <= 0` or `nan`.
+    ScaleTooSmall,
+    /// `1 / scale == 0`.
+    ScaleTooLarge,
+}
+
+#[derive(Clone, Copy, Debug)]
+enum GammaRepr<N> {
+    Large(GammaLargeShape<N>),
+    One(Exp<N>),
+    Small(GammaSmallShape<N>)
+}
+
+// These two helpers could be made public, but saving the
+// match-on-Gamma-enum branch from using them directly (e.g. if one
+// knows that the shape is always > 1) doesn't appear to be much
+// faster.
+
+/// Gamma distribution where the shape parameter is less than 1.
+///
+/// Note, samples from this require a compulsory floating-point `pow`
+/// call, which makes it significantly slower than sampling from a
+/// gamma distribution where the shape parameter is greater than or
+/// equal to 1.
+///
+/// See `Gamma` for sampling from a Gamma distribution with general
+/// shape parameters.
+#[derive(Clone, Copy, Debug)]
+struct GammaSmallShape<N> {
+    inv_shape: N,
+    large_shape: GammaLargeShape<N>
+}
+
+/// Gamma distribution where the shape parameter is larger than 1.
+///
+/// See `Gamma` for sampling from a Gamma distribution with general
+/// shape parameters.
+#[derive(Clone, Copy, Debug)]
+struct GammaLargeShape<N> {
+    scale: N,
+    c: N,
+    d: N
+}
+
+impl<N: Float> Gamma<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    /// Construct an object representing the `Gamma(shape, scale)`
+    /// distribution.
+    #[inline]
+    pub fn new(shape: N, scale: N) -> Result<Gamma<N>, Error> {
+        if !(shape > N::from(0.0)) {
+            return Err(Error::ShapeTooSmall);
+        }
+        if !(scale > N::from(0.0)) {
+            return Err(Error::ScaleTooSmall);
+        }
+
+        let repr = if shape == N::from(1.0) {
+            One(Exp::new(N::from(1.0) / scale).map_err(|_| Error::ScaleTooLarge)?)
+        } else if shape < N::from(1.0) {
+            Small(GammaSmallShape::new_raw(shape, scale))
+        } else {
+            Large(GammaLargeShape::new_raw(shape, scale))
+        };
+        Ok(Gamma { repr })
+    }
+}
+
+impl<N: Float> GammaSmallShape<N>
+where StandardNormal: Distribution<N>, Open01: Distribution<N>
+{
+    fn new_raw(shape: N, scale: N) -> GammaSmallShape<N> {
+        GammaSmallShape {
+            inv_shape: N::from(1.0) / shape,
+            large_shape: GammaLargeShape::new_raw(shape + N::from(1.0), scale)
+        }
+    }
+}
+
+impl<N: Float> GammaLargeShape<N>
+where StandardNormal: Distribution<N>, Open01: Distribution<N>
+{
+    fn new_raw(shape: N, scale: N) -> GammaLargeShape<N> {
+        let d = shape - N::from(1. / 3.);
+        GammaLargeShape {
+            scale,
+            c: N::from(1.0) / (N::from(9.) * d).sqrt(),
+            d
+        }
+    }
+}
+
+impl<N: Float> Distribution<N> for Gamma<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        match self.repr {
+            Small(ref g) => g.sample(rng),
+            One(ref g) => g.sample(rng),
+            Large(ref g) => g.sample(rng),
+        }
+    }
+}
+impl<N: Float> Distribution<N> for GammaSmallShape<N>
+where StandardNormal: Distribution<N>, Open01: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        let u: N = rng.sample(Open01);
+
+        self.large_shape.sample(rng) * u.powf(self.inv_shape)
+    }
+}
+impl<N: Float> Distribution<N> for GammaLargeShape<N>
+where StandardNormal: Distribution<N>, Open01: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        // Marsaglia & Tsang method, 2000
+        loop {
+            let x: N = rng.sample(StandardNormal);
+            let v_cbrt = N::from(1.0) + self.c * x;
+            if v_cbrt <= N::from(0.0) { // a^3 <= 0 iff a <= 0
+                continue
+            }
+
+            let v = v_cbrt * v_cbrt * v_cbrt;
+            let u: N = rng.sample(Open01);
+
+            let x_sqr = x * x;
+            if u < N::from(1.0) - N::from(0.0331) * x_sqr * x_sqr ||
+                u.ln() < N::from(0.5) * x_sqr + self.d * (N::from(1.0) - v + v.ln())
+            {
+                return self.d * v * self.scale
+            }
+        }
+    }
+}
+
+/// The chi-squared distribution `χ²(k)`, where `k` is the degrees of
+/// freedom.
+///
+/// For `k > 0` integral, this distribution is the sum of the squares
+/// of `k` independent standard normal random variables. For other
+/// `k`, this uses the equivalent characterisation
+/// `χ²(k) = Gamma(k/2, 2)`.
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{ChiSquared, Distribution};
+///
+/// let chi = ChiSquared::new(11.0).unwrap();
+/// let v = chi.sample(&mut rand::thread_rng());
+/// println!("{} is from a χ²(11) distribution", v)
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct ChiSquared<N> {
+    repr: ChiSquaredRepr<N>,
+}
+
+/// Error type returned from `ChiSquared::new` and `StudentT::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum ChiSquaredError {
+    /// `0.5 * k <= 0` or `nan`.
+    DoFTooSmall,
+}
+
+#[derive(Clone, Copy, Debug)]
+enum ChiSquaredRepr<N> {
+    // k == 1, Gamma(alpha, ..) is particularly slow for alpha < 1,
+    // e.g. when alpha = 1/2 as it would be for this case, so special-
+    // casing and using the definition of N(0,1)^2 is faster.
+    DoFExactlyOne,
+    DoFAnythingElse(Gamma<N>),
+}
+
+impl<N: Float> ChiSquared<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    /// Create a new chi-squared distribution with degrees-of-freedom
+    /// `k`.
+    pub fn new(k: N) -> Result<ChiSquared<N>, ChiSquaredError> {
+        let repr = if k == N::from(1.0) {
+            DoFExactlyOne
+        } else {
+            if !(N::from(0.5) * k > N::from(0.0)) {
+                return Err(ChiSquaredError::DoFTooSmall);
+            }
+            DoFAnythingElse(Gamma::new(N::from(0.5) * k, N::from(2.0)).unwrap())
+        };
+        Ok(ChiSquared { repr })
+    }
+}
+impl<N: Float> Distribution<N> for ChiSquared<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        match self.repr {
+            DoFExactlyOne => {
+                // k == 1 => N(0,1)^2
+                let norm: N = rng.sample(StandardNormal);
+                norm * norm
+            }
+            DoFAnythingElse(ref g) => g.sample(rng)
+        }
+    }
+}
+
+/// The Fisher F distribution `F(m, n)`.
+///
+/// This distribution is equivalent to the ratio of two normalised
+/// chi-squared distributions, that is, `F(m,n) = (χ²(m)/m) /
+/// (χ²(n)/n)`.
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{FisherF, Distribution};
+///
+/// let f = FisherF::new(2.0, 32.0).unwrap();
+/// let v = f.sample(&mut rand::thread_rng());
+/// println!("{} is from an F(2, 32) distribution", v)
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct FisherF<N> {
+    numer: ChiSquared<N>,
+    denom: ChiSquared<N>,
+    // denom_dof / numer_dof so that this can just be a straight
+    // multiplication, rather than a division.
+    dof_ratio: N,
+}
+
+/// Error type returned from `FisherF::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum FisherFError {
+    /// `m <= 0` or `nan`.
+    MTooSmall,
+    /// `n <= 0` or `nan`.
+    NTooSmall,
+}
+
+impl<N: Float> FisherF<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    /// Create a new `FisherF` distribution, with the given parameter.
+    pub fn new(m: N, n: N) -> Result<FisherF<N>, FisherFError> {
+        if !(m > N::from(0.0)) {
+            return Err(FisherFError::MTooSmall);
+        }
+        if !(n > N::from(0.0)) {
+            return Err(FisherFError::NTooSmall);
+        }
+
+        Ok(FisherF {
+            numer: ChiSquared::new(m).unwrap(),
+            denom: ChiSquared::new(n).unwrap(),
+            dof_ratio: n / m
+        })
+    }
+}
+impl<N: Float> Distribution<N> for FisherF<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        self.numer.sample(rng) / self.denom.sample(rng) * self.dof_ratio
+    }
+}
+
+/// The Student t distribution, `t(nu)`, where `nu` is the degrees of
+/// freedom.
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{StudentT, Distribution};
+///
+/// let t = StudentT::new(11.0).unwrap();
+/// let v = t.sample(&mut rand::thread_rng());
+/// println!("{} is from a t(11) distribution", v)
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct StudentT<N> {
+    chi: ChiSquared<N>,
+    dof: N
+}
+
+impl<N: Float> StudentT<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    /// Create a new Student t distribution with `n` degrees of
+    /// freedom.
+    pub fn new(n: N) -> Result<StudentT<N>, ChiSquaredError> {
+        Ok(StudentT {
+            chi: ChiSquared::new(n)?,
+            dof: n
+        })
+    }
+}
+impl<N: Float> Distribution<N> for StudentT<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        let norm: N = rng.sample(StandardNormal);
+        norm * (self.dof / self.chi.sample(rng)).sqrt()
+    }
+}
+
+/// The Beta distribution with shape parameters `alpha` and `beta`.
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{Distribution, Beta};
+///
+/// let beta = Beta::new(2.0, 5.0).unwrap();
+/// let v = beta.sample(&mut rand::thread_rng());
+/// println!("{} is from a Beta(2, 5) distribution", v);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct Beta<N> {
+    gamma_a: Gamma<N>,
+    gamma_b: Gamma<N>,
+}
+
+/// Error type returned from `Beta::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum BetaError {
+    /// `alpha <= 0` or `nan`.
+    AlphaTooSmall,
+    /// `beta <= 0` or `nan`.
+    BetaTooSmall,
+}
+
+impl<N: Float> Beta<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    /// Construct an object representing the `Beta(alpha, beta)`
+    /// distribution.
+    pub fn new(alpha: N, beta: N) -> Result<Beta<N>, BetaError> {
+        Ok(Beta {
+            gamma_a: Gamma::new(alpha, N::from(1.))
+                         .map_err(|_| BetaError::AlphaTooSmall)?,
+            gamma_b: Gamma::new(beta, N::from(1.))
+                         .map_err(|_| BetaError::BetaTooSmall)?,
+        })
+    }
+}
+
+impl<N: Float> Distribution<N> for Beta<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        let x = self.gamma_a.sample(rng);
+        let y = self.gamma_b.sample(rng);
+        x / (x + y)
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use crate::Distribution;
+    use super::{Beta, ChiSquared, StudentT, FisherF};
+
+    #[test]
+    fn test_chi_squared_one() {
+        let chi = ChiSquared::new(1.0).unwrap();
+        let mut rng = crate::test::rng(201);
+        for _ in 0..1000 {
+            chi.sample(&mut rng);
+        }
+    }
+    #[test]
+    fn test_chi_squared_small() {
+        let chi = ChiSquared::new(0.5).unwrap();
+        let mut rng = crate::test::rng(202);
+        for _ in 0..1000 {
+            chi.sample(&mut rng);
+        }
+    }
+    #[test]
+    fn test_chi_squared_large() {
+        let chi = ChiSquared::new(30.0).unwrap();
+        let mut rng = crate::test::rng(203);
+        for _ in 0..1000 {
+            chi.sample(&mut rng);
+        }
+    }
+    #[test]
+    #[should_panic]
+    fn test_chi_squared_invalid_dof() {
+        ChiSquared::new(-1.0).unwrap();
+    }
+
+    #[test]
+    fn test_f() {
+        let f = FisherF::new(2.0, 32.0).unwrap();
+        let mut rng = crate::test::rng(204);
+        for _ in 0..1000 {
+            f.sample(&mut rng);
+        }
+    }
+
+    #[test]
+    fn test_t() {
+        let t = StudentT::new(11.0).unwrap();
+        let mut rng = crate::test::rng(205);
+        for _ in 0..1000 {
+            t.sample(&mut rng);
+        }
+    }
+
+    #[test]
+    fn test_beta() {
+        let beta = Beta::new(1.0, 2.0).unwrap();
+        let mut rng = crate::test::rng(201);
+        for _ in 0..1000 {
+            beta.sample(&mut rng);
+        }
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_beta_invalid_dof() {
+        Beta::new(0., 0.).unwrap();
+    }
+}
diff --git a/rand/rand_distr/src/lib.rs b/rand/rand_distr/src/lib.rs
new file mode 100644
index 0000000..baf65ed
--- /dev/null
+++ b/rand/rand_distr/src/lib.rs
@@ -0,0 +1,134 @@
+// Copyright 2019 Developers of the Rand project.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+#![doc(html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png",
+       html_favicon_url = "https://www.rust-lang.org/favicon.ico",
+       html_root_url = "https://rust-random.github.io/rand/")]
+
+#![deny(missing_docs)]
+#![deny(missing_debug_implementations)]
+
+#![allow(clippy::excessive_precision, clippy::float_cmp, clippy::unreadable_literal)]
+#![allow(clippy::neg_cmp_op_on_partial_ord)]  // suggested fix too verbose
+
+//! Generating random samples from probability distributions.
+//!
+//! ## Re-exports
+//!
+//! This crate is a super-set of the [`rand::distributions`] module. See the
+//! [`rand::distributions`] module documentation for an overview of the core
+//! [`Distribution`] trait and implementations.
+//!
+//! The following are re-exported:
+//! 
+//! - The [`Distribution`] trait and [`DistIter`] helper type
+//! - The [`Standard`], [`Alphanumeric`], [`Uniform`], [`OpenClosed01`], [`Open01`] and [`Bernoulli`] distributions
+//! - The [`weighted`] sub-module
+//!
+//! ## Distributions
+//!
+//! This crate provides the following probability distributions:
+//!
+//! - Related to real-valued quantities that grow linearly
+//!   (e.g. errors, offsets):
+//!   - [`Normal`] distribution, and [`StandardNormal`] as a primitive
+//!   - [`Cauchy`] distribution
+//! - Related to Bernoulli trials (yes/no events, with a given probability):
+//!   - [`Binomial`] distribution
+//! - Related to positive real-valued quantities that grow exponentially
+//!   (e.g. prices, incomes, populations):
+//!   - [`LogNormal`] distribution
+//! - Related to the occurrence of independent events at a given rate:
+//!   - [`Pareto`] distribution
+//!   - [`Poisson`] distribution
+//!   - [`Exp`]onential distribution, and [`Exp1`] as a primitive
+//!   - [`Weibull`] distribution
+//! - Gamma and derived distributions:
+//!   - [`Gamma`] distribution
+//!   - [`ChiSquared`] distribution
+//!   - [`StudentT`] distribution
+//!   - [`FisherF`] distribution
+//! - Triangular distribution:
+//!   - [`Beta`] distribution
+//!   - [`Triangular`] distribution
+//! - Multivariate probability distributions
+//!   - [`Dirichlet`] distribution
+//!   - [`UnitSphere`] distribution
+//!   - [`UnitBall`] distribution
+//!   - [`UnitCircle`] distribution
+//!   - [`UnitDisc`] distribution
+
+pub use rand::distributions::{Distribution, DistIter, Standard,
+    Alphanumeric, Uniform, OpenClosed01, Open01, Bernoulli, uniform, weighted};
+
+pub use self::unit_sphere::UnitSphere;
+pub use self::unit_ball::UnitBall;
+pub use self::unit_circle::UnitCircle;
+pub use self::unit_disc::UnitDisc;
+pub use self::gamma::{Gamma, Error as GammaError, ChiSquared, ChiSquaredError,
+    FisherF, FisherFError, StudentT, Beta, BetaError};
+pub use self::normal::{Normal, Error as NormalError, LogNormal, StandardNormal};
+pub use self::exponential::{Exp, Error as ExpError, Exp1};
+pub use self::pareto::{Pareto, Error as ParetoError};
+pub use self::pert::{Pert, PertError};
+pub use self::poisson::{Poisson, Error as PoissonError};
+pub use self::binomial::{Binomial, Error as BinomialError};
+pub use self::cauchy::{Cauchy, Error as CauchyError};
+pub use self::dirichlet::{Dirichlet, Error as DirichletError};
+pub use self::triangular::{Triangular, TriangularError};
+pub use self::weibull::{Weibull, Error as WeibullError};
+pub use self::utils::Float;
+
+mod unit_sphere;
+mod unit_ball;
+mod unit_circle;
+mod unit_disc;
+mod gamma;
+mod normal;
+mod exponential;
+mod pareto;
+mod pert;
+mod poisson;
+mod binomial;
+mod cauchy;
+mod dirichlet;
+mod triangular;
+mod weibull;
+mod utils;
+mod ziggurat_tables;
+
+#[cfg(test)]
+mod test {
+    // Notes on testing
+    // 
+    // Testing random number distributions correctly is hard. The following
+    // testing is desired:
+    // 
+    // - Construction: test initialisation with a few valid parameter sets.
+    // - Erroneous usage: test that incorrect usage generates an error.
+    // - Vector: test that usage with fixed inputs (including RNG) generates a
+    //   fixed output sequence on all platforms.
+    // - Correctness at fixed points (optional): using a specific mock RNG,
+    //   check that specific values are sampled (e.g. end-points and median of
+    //   distribution).
+    // - Correctness of PDF (extra): generate a histogram of samples within a
+    //   certain range, and check this approximates the PDF. These tests are
+    //   expected to be expensive, and should be behind a feature-gate.
+    //
+    // TODO: Vector and correctness tests are largely absent so far.
+    // NOTE: Some distributions have tests checking only that samples can be
+    // generated. This is redundant with vector and correctness tests.
+
+    /// Construct a deterministic RNG with the given seed
+    pub fn rng(seed: u64) -> impl rand::RngCore {
+        // For tests, we want a statistically good, fast, reproducible RNG.
+        // PCG32 will do fine, and will be easy to embed if we ever need to.
+        const INC: u64 = 11634580027462260723;
+        rand_pcg::Pcg32::new(seed, INC)
+    }
+}
diff --git a/rand/rand_distr/src/normal.rs b/rand/rand_distr/src/normal.rs
new file mode 100644
index 0000000..882754f
--- /dev/null
+++ b/rand/rand_distr/src/normal.rs
@@ -0,0 +1,219 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 The Rust Project Developers.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The normal and derived distributions.
+
+use rand::Rng;
+use crate::{ziggurat_tables, Distribution, Open01};
+use crate::utils::{ziggurat, Float};
+
+/// Samples floating-point numbers according to the normal distribution
+/// `N(0, 1)` (a.k.a. a standard normal, or Gaussian). This is equivalent to
+/// `Normal::new(0.0, 1.0)` but faster.
+///
+/// See `Normal` for the general normal distribution.
+///
+/// Implemented via the ZIGNOR variant[^1] of the Ziggurat method.
+///
+/// [^1]: Jurgen A. Doornik (2005). [*An Improved Ziggurat Method to
+///       Generate Normal Random Samples*](
+///       https://www.doornik.com/research/ziggurat.pdf).
+///       Nuffield College, Oxford
+///
+/// # Example
+/// ```
+/// use rand::prelude::*;
+/// use rand_distr::StandardNormal;
+///
+/// let val: f64 = thread_rng().sample(StandardNormal);
+/// println!("{}", val);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct StandardNormal;
+
+impl Distribution<f32> for StandardNormal {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f32 {
+        // TODO: use optimal 32-bit implementation
+        let x: f64 = self.sample(rng);
+        x as f32
+    }
+}
+
+impl Distribution<f64> for StandardNormal {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        #[inline]
+        fn pdf(x: f64) -> f64 {
+            (-x*x/2.0).exp()
+        }
+        #[inline]
+        fn zero_case<R: Rng + ?Sized>(rng: &mut R, u: f64) -> f64 {
+            // compute a random number in the tail by hand
+
+            // strange initial conditions, because the loop is not
+            // do-while, so the condition should be true on the first
+            // run, they get overwritten anyway (0 < 1, so these are
+            // good).
+            let mut x = 1.0f64;
+            let mut y = 0.0f64;
+
+            while -2.0 * y < x * x {
+                let x_: f64 = rng.sample(Open01);
+                let y_: f64 = rng.sample(Open01);
+
+                x = x_.ln() / ziggurat_tables::ZIG_NORM_R;
+                y = y_.ln();
+            }
+
+            if u < 0.0 { x - ziggurat_tables::ZIG_NORM_R } else { ziggurat_tables::ZIG_NORM_R - x }
+        }
+
+        ziggurat(rng, true, // this is symmetric
+                 &ziggurat_tables::ZIG_NORM_X,
+                 &ziggurat_tables::ZIG_NORM_F,
+                 pdf, zero_case)
+    }
+}
+
+/// The normal distribution `N(mean, std_dev**2)`.
+///
+/// This uses the ZIGNOR variant of the Ziggurat method, see [`StandardNormal`]
+/// for more details.
+/// 
+/// Note that [`StandardNormal`] is an optimised implementation for mean 0, and
+/// standard deviation 1.
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{Normal, Distribution};
+///
+/// // mean 2, standard deviation 3
+/// let normal = Normal::new(2.0, 3.0).unwrap();
+/// let v = normal.sample(&mut rand::thread_rng());
+/// println!("{} is from a N(2, 9) distribution", v)
+/// ```
+///
+/// [`StandardNormal`]: crate::StandardNormal
+#[derive(Clone, Copy, Debug)]
+pub struct Normal<N> {
+    mean: N,
+    std_dev: N,
+}
+
+/// Error type returned from `Normal::new` and `LogNormal::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum Error {
+    /// `std_dev < 0` or `nan`.
+    StdDevTooSmall,
+}
+
+impl<N: Float> Normal<N>
+where StandardNormal: Distribution<N>
+{
+    /// Construct a new `Normal` distribution with the given mean and
+    /// standard deviation.
+    #[inline]
+    pub fn new(mean: N, std_dev: N) -> Result<Normal<N>, Error> {
+        if !(std_dev >= N::from(0.0)) {
+            return Err(Error::StdDevTooSmall);
+        }
+        Ok(Normal {
+            mean,
+            std_dev
+        })
+    }
+}
+
+impl<N: Float> Distribution<N> for Normal<N>
+where StandardNormal: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        let n: N = rng.sample(StandardNormal);
+        self.mean + self.std_dev * n
+    }
+}
+
+
+/// The log-normal distribution `ln N(mean, std_dev**2)`.
+///
+/// If `X` is log-normal distributed, then `ln(X)` is `N(mean, std_dev**2)`
+/// distributed.
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{LogNormal, Distribution};
+///
+/// // mean 2, standard deviation 3
+/// let log_normal = LogNormal::new(2.0, 3.0).unwrap();
+/// let v = log_normal.sample(&mut rand::thread_rng());
+/// println!("{} is from an ln N(2, 9) distribution", v)
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct LogNormal<N> {
+    norm: Normal<N>
+}
+
+impl<N: Float> LogNormal<N>
+where StandardNormal: Distribution<N>
+{
+    /// Construct a new `LogNormal` distribution with the given mean
+    /// and standard deviation of the logarithm of the distribution.
+    #[inline]
+    pub fn new(mean: N, std_dev: N) -> Result<LogNormal<N>, Error> {
+        if !(std_dev >= N::from(0.0)) {
+            return Err(Error::StdDevTooSmall);
+        }
+        Ok(LogNormal { norm: Normal::new(mean, std_dev).unwrap() })
+    }
+}
+
+impl<N: Float> Distribution<N> for LogNormal<N>
+where StandardNormal: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        self.norm.sample(rng).exp()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use crate::Distribution;
+    use super::{Normal, LogNormal};
+
+    #[test]
+    fn test_normal() {
+        let norm = Normal::new(10.0, 10.0).unwrap();
+        let mut rng = crate::test::rng(210);
+        for _ in 0..1000 {
+            norm.sample(&mut rng);
+        }
+    }
+    #[test]
+    #[should_panic]
+    fn test_normal_invalid_sd() {
+        Normal::new(10.0, -1.0).unwrap();
+    }
+
+
+    #[test]
+    fn test_log_normal() {
+        let lnorm = LogNormal::new(10.0, 10.0).unwrap();
+        let mut rng = crate::test::rng(211);
+        for _ in 0..1000 {
+            lnorm.sample(&mut rng);
+        }
+    }
+    #[test]
+    #[should_panic]
+    fn test_log_normal_invalid_sd() {
+        LogNormal::new(10.0, -1.0).unwrap();
+    }
+}
diff --git a/rand/rand_distr/src/pareto.rs b/rand/rand_distr/src/pareto.rs
new file mode 100644
index 0000000..33ea382
--- /dev/null
+++ b/rand/rand_distr/src/pareto.rs
@@ -0,0 +1,89 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The Pareto distribution.
+
+use rand::Rng;
+use crate::{Distribution, OpenClosed01};
+use crate::utils::Float;
+
+/// Samples floating-point numbers according to the Pareto distribution
+///
+/// # Example
+/// ```
+/// use rand::prelude::*;
+/// use rand_distr::Pareto;
+///
+/// let val: f64 = thread_rng().sample(Pareto::new(1., 2.).unwrap());
+/// println!("{}", val);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct Pareto<N> {
+    scale: N,
+    inv_neg_shape: N,
+}
+
+/// Error type returned from `Pareto::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum Error {
+    /// `scale <= 0` or `nan`.
+    ScaleTooSmall,
+    /// `shape <= 0` or `nan`.
+    ShapeTooSmall,
+}
+
+impl<N: Float> Pareto<N>
+where OpenClosed01: Distribution<N>
+{
+    /// Construct a new Pareto distribution with given `scale` and `shape`.
+    ///
+    /// In the literature, `scale` is commonly written as x<sub>m</sub> or k and
+    /// `shape` is often written as α.
+    pub fn new(scale: N, shape: N) -> Result<Pareto<N>, Error> {
+        if !(scale > N::from(0.0)) {
+            return Err(Error::ScaleTooSmall);
+        }
+        if !(shape > N::from(0.0)) {
+            return Err(Error::ShapeTooSmall);
+        }
+        Ok(Pareto { scale, inv_neg_shape: N::from(-1.0) / shape })
+    }
+}
+
+impl<N: Float> Distribution<N> for Pareto<N>
+where OpenClosed01: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        let u: N = OpenClosed01.sample(rng);
+        self.scale * u.powf(self.inv_neg_shape)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use crate::Distribution;
+    use super::Pareto;
+
+    #[test]
+    #[should_panic]
+    fn invalid() {
+        Pareto::new(0., 0.).unwrap();
+    }
+
+    #[test]
+    fn sample() {
+        let scale = 1.0;
+        let shape = 2.0;
+        let d = Pareto::new(scale, shape).unwrap();
+        let mut rng = crate::test::rng(1);
+        for _ in 0..1000 {
+            let r = d.sample(&mut rng);
+            assert!(r >= scale);
+        }
+    }
+}
diff --git a/rand/rand_distr/src/pert.rs b/rand/rand_distr/src/pert.rs
new file mode 100644
index 0000000..040cd05
--- /dev/null
+++ b/rand/rand_distr/src/pert.rs
@@ -0,0 +1,132 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+//! The PERT distribution.
+
+use rand::Rng;
+use crate::{Distribution, Beta, StandardNormal, Exp1, Open01};
+use crate::utils::Float;
+
+/// The PERT distribution.
+///
+/// Similar to the [`Triangular`] distribution, the PERT distribution is
+/// parameterised by a range and a mode within that range. Unlike the
+/// [`Triangular`] distribution, the probability density function of the PERT
+/// distribution is smooth, with a configurable weighting around the mode.
+///
+/// # Example
+///
+/// ```rust
+/// use rand_distr::{Pert, Distribution};
+///
+/// let d = Pert::new(0., 5., 2.5).unwrap();
+/// let v = d.sample(&mut rand::thread_rng());
+/// println!("{} is from a PERT distribution", v);
+/// ```
+/// 
+/// [`Triangular`]: crate::Triangular
+#[derive(Clone, Copy, Debug)]
+pub struct Pert<N> {
+    min: N,
+    range: N,
+    beta: Beta<N>,
+}
+
+/// Error type returned from [`Pert`] constructors.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum PertError {
+    /// `max < min` or `min` or `max` is NaN.
+    RangeTooSmall,
+    /// `mode < min` or `mode > max` or `mode` is NaN.
+    ModeRange,
+    /// `shape < 0` or `shape` is NaN
+    ShapeTooSmall,
+}
+
+impl<N: Float> Pert<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    /// Set up the PERT distribution with defined `min`, `max` and `mode`.
+    ///
+    /// This is equivalent to calling `Pert::new_shape` with `shape == 4.0`.
+    #[inline]
+    pub fn new(min: N, max: N, mode: N) -> Result<Pert<N>, PertError> {
+        Pert::new_with_shape(min, max, mode, N::from(4.))
+    }
+    
+    /// Set up the PERT distribution with defined `min`, `max`, `mode` and
+    /// `shape`.
+    pub fn new_with_shape(min: N, max: N, mode: N, shape: N) -> Result<Pert<N>, PertError> {
+        if !(max > min) {
+            return Err(PertError::RangeTooSmall);
+        }
+        if !(mode >= min && max >= mode) {
+            return Err(PertError::ModeRange);
+        }
+        if !(shape >= N::from(0.)) {
+            return Err(PertError::ShapeTooSmall);
+        }
+        
+        let range = max - min;
+        let mu = (min + max + shape * mode) / (shape + N::from(2.));
+        let v = if mu == mode {
+            shape * N::from(0.5) + N::from(1.)
+        } else {
+            (mu - min) * (N::from(2.) * mode - min - max)
+                / ((mode - mu) * (max - min))
+        };
+        let w = v * (max - mu) / (mu - min);
+        let beta = Beta::new(v, w).map_err(|_| PertError::RangeTooSmall)?;
+        Ok(Pert{ min, range, beta })
+    }
+}
+
+impl<N: Float> Distribution<N> for Pert<N>
+where StandardNormal: Distribution<N>, Exp1: Distribution<N>, Open01: Distribution<N>
+{
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        self.beta.sample(rng) * self.range + self.min
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use std::f64;
+    use super::*;
+
+    #[test]
+    fn test_pert() {
+        for &(min, max, mode) in &[
+            (-1., 1., 0.),
+            (1., 2., 1.),
+            (5., 25., 25.),
+        ] {
+            let _distr = Pert::new(min, max, mode).unwrap();
+            // TODO: test correctness
+        }
+
+        for &(min, max, mode) in &[
+            (-1., 1., 2.),
+            (-1., 1., -2.),
+            (2., 1., 1.),
+        ] {
+            assert!(Pert::new(min, max, mode).is_err());
+        }
+    }
+    
+    #[test]
+    fn value_stability() {
+        let rng = crate::test::rng(860);
+        let distr = Pert::new(2., 10., 3.).unwrap();    // mean = 4, var = 12/7
+        let seq = distr.sample_iter(rng).take(5).collect::<Vec<f64>>();
+        println!("seq: {:?}", seq);
+        let expected = vec![4.631484136029422, 3.307201472321789,
+                3.29995019556348, 3.66835483991721, 3.514246139933899];
+        assert!(seq == expected);
+    }
+}
diff --git a/rand/rand_distr/src/poisson.rs b/rand/rand_distr/src/poisson.rs
new file mode 100644
index 0000000..4f4a0b7
--- /dev/null
+++ b/rand/rand_distr/src/poisson.rs
@@ -0,0 +1,233 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2016-2017 The Rust Project Developers.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The Poisson distribution.
+
+use rand::Rng;
+use crate::{Distribution, Cauchy, Standard};
+use crate::utils::Float;
+
+/// The Poisson distribution `Poisson(lambda)`.
+///
+/// This distribution has a density function:
+/// `f(k) = lambda^k * exp(-lambda) / k!` for `k >= 0`.
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{Poisson, Distribution};
+///
+/// let poi = Poisson::new(2.0).unwrap();
+/// let v: u64 = poi.sample(&mut rand::thread_rng());
+/// println!("{} is from a Poisson(2) distribution", v);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct Poisson<N> {
+    lambda: N,
+    // precalculated values
+    exp_lambda: N,
+    log_lambda: N,
+    sqrt_2lambda: N,
+    magic_val: N,
+}
+
+/// Error type returned from `Poisson::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum Error {
+    /// `lambda <= 0` or `nan`.
+    ShapeTooSmall,
+}
+
+impl<N: Float> Poisson<N>
+where Standard: Distribution<N>
+{
+    /// Construct a new `Poisson` with the given shape parameter
+    /// `lambda`.
+    pub fn new(lambda: N) -> Result<Poisson<N>, Error> {
+        if !(lambda > N::from(0.0)) {
+            return Err(Error::ShapeTooSmall);
+        }
+        let log_lambda = lambda.ln();
+        Ok(Poisson {
+            lambda,
+            exp_lambda: (-lambda).exp(),
+            log_lambda,
+            sqrt_2lambda: (N::from(2.0) * lambda).sqrt(),
+            magic_val: lambda * log_lambda - (N::from(1.0) + lambda).log_gamma(),
+        })
+    }
+}
+
+impl<N: Float> Distribution<N> for Poisson<N>
+where Standard: Distribution<N>
+{
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        // using the algorithm from Numerical Recipes in C
+
+        // for low expected values use the Knuth method
+        if self.lambda < N::from(12.0) {
+            let mut result = N::from(0.);
+            let mut p = N::from(1.0);
+            while p > self.exp_lambda {
+                p *= rng.gen::<N>();
+                result += N::from(1.);
+            }
+            result - N::from(1.)
+        }
+        // high expected values - rejection method
+        else {
+            // we use the Cauchy distribution as the comparison distribution
+            // f(x) ~ 1/(1+x^2)
+            let cauchy = Cauchy::new(N::from(0.0), N::from(1.0)).unwrap();
+            let mut result;
+
+            loop {
+                let mut comp_dev;
+
+                loop {
+                    // draw from the Cauchy distribution
+                    comp_dev = rng.sample(cauchy);
+                    // shift the peak of the comparison ditribution
+                    result = self.sqrt_2lambda * comp_dev + self.lambda;
+                    // repeat the drawing until we are in the range of possible values
+                    if result >= N::from(0.0) {
+                        break;
+                    }
+                }
+                // now the result is a random variable greater than 0 with Cauchy distribution
+                // the result should be an integer value
+                result = result.floor();
+
+                // this is the ratio of the Poisson distribution to the comparison distribution
+                // the magic value scales the distribution function to a range of approximately 0-1
+                // since it is not exact, we multiply the ratio by 0.9 to avoid ratios greater than 1
+                // this doesn't change the resulting distribution, only increases the rate of failed drawings
+                let check = N::from(0.9) * (N::from(1.0) + comp_dev * comp_dev)
+                    * (result * self.log_lambda - (N::from(1.0) + result).log_gamma() - self.magic_val).exp();
+
+                // check with uniform random value - if below the threshold, we are within the target distribution
+                if rng.gen::<N>() <= check {
+                    break;
+                }
+            }
+            result
+        }
+    }
+}
+
+impl<N: Float> Distribution<u64> for Poisson<N>
+where Standard: Distribution<N>
+{
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64 {
+        let result: N = self.sample(rng);
+        result.to_u64().unwrap()
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use crate::Distribution;
+    use super::Poisson;
+
+    #[test]
+    fn test_poisson_10() {
+        let poisson = Poisson::new(10.0).unwrap();
+        let mut rng = crate::test::rng(123);
+        let mut sum_u64 = 0;
+        let mut sum_f64 = 0.;
+        for _ in 0..1000 {
+            let s_u64: u64 = poisson.sample(&mut rng);
+            let s_f64: f64 = poisson.sample(&mut rng);
+            sum_u64 += s_u64;
+            sum_f64 += s_f64;
+        }
+        let avg_u64 = (sum_u64 as f64) / 1000.0;
+        let avg_f64 = sum_f64 / 1000.0;
+        println!("Poisson averages: {} (u64)  {} (f64)", avg_u64, avg_f64);
+        for &avg in &[avg_u64, avg_f64] {
+            assert!((avg - 10.0).abs() < 0.5); // not 100% certain, but probable enough
+        }
+    }
+
+    #[test]
+    fn test_poisson_15() {
+        // Take the 'high expected values' path
+        let poisson = Poisson::new(15.0).unwrap();
+        let mut rng = crate::test::rng(123);
+        let mut sum_u64 = 0;
+        let mut sum_f64 = 0.;
+        for _ in 0..1000 {
+            let s_u64: u64 = poisson.sample(&mut rng);
+            let s_f64: f64 = poisson.sample(&mut rng);
+            sum_u64 += s_u64;
+            sum_f64 += s_f64;
+        }
+        let avg_u64 = (sum_u64 as f64) / 1000.0;
+        let avg_f64 = sum_f64 / 1000.0;
+        println!("Poisson average: {} (u64)  {} (f64)", avg_u64, avg_f64);
+        for &avg in &[avg_u64, avg_f64] {
+            assert!((avg - 15.0).abs() < 0.5); // not 100% certain, but probable enough
+        }
+    }
+
+    #[test]
+    fn test_poisson_10_f32() {
+        let poisson = Poisson::new(10.0f32).unwrap();
+        let mut rng = crate::test::rng(123);
+        let mut sum_u64 = 0;
+        let mut sum_f32 = 0.;
+        for _ in 0..1000 {
+            let s_u64: u64 = poisson.sample(&mut rng);
+            let s_f32: f32 = poisson.sample(&mut rng);
+            sum_u64 += s_u64;
+            sum_f32 += s_f32;
+        }
+        let avg_u64 = (sum_u64 as f32) / 1000.0;
+        let avg_f32 = sum_f32 / 1000.0;
+        println!("Poisson averages: {} (u64)  {} (f32)", avg_u64, avg_f32);
+        for &avg in &[avg_u64, avg_f32] {
+            assert!((avg - 10.0).abs() < 0.5); // not 100% certain, but probable enough
+        }
+    }
+
+    #[test]
+    fn test_poisson_15_f32() {
+        // Take the 'high expected values' path
+        let poisson = Poisson::new(15.0f32).unwrap();
+        let mut rng = crate::test::rng(123);
+        let mut sum_u64 = 0;
+        let mut sum_f32 = 0.;
+        for _ in 0..1000 {
+            let s_u64: u64 = poisson.sample(&mut rng);
+            let s_f32: f32 = poisson.sample(&mut rng);
+            sum_u64 += s_u64;
+            sum_f32 += s_f32;
+        }
+        let avg_u64 = (sum_u64 as f32) / 1000.0;
+        let avg_f32 = sum_f32 / 1000.0;
+        println!("Poisson average: {} (u64)  {} (f32)", avg_u64, avg_f32);
+        for &avg in &[avg_u64, avg_f32] {
+            assert!((avg - 15.0).abs() < 0.5); // not 100% certain, but probable enough
+        }
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_poisson_invalid_lambda_zero() {
+        Poisson::new(0.0).unwrap();
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_poisson_invalid_lambda_neg() {
+        Poisson::new(-10.0).unwrap();
+    }
+}
diff --git a/rand/rand_distr/src/triangular.rs b/rand/rand_distr/src/triangular.rs
new file mode 100644
index 0000000..dd0bbfb
--- /dev/null
+++ b/rand/rand_distr/src/triangular.rs
@@ -0,0 +1,125 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+//! The triangular distribution.
+
+use rand::Rng;
+use crate::{Distribution, Standard};
+use crate::utils::Float;
+
+/// The triangular distribution.
+/// 
+/// A continuous probability distribution parameterised by a range, and a mode
+/// (most likely value) within that range.
+///
+/// The probability density function is triangular. For a similar distribution
+/// with a smooth PDF, see the [`Pert`] distribution.
+///
+/// # Example
+///
+/// ```rust
+/// use rand_distr::{Triangular, Distribution};
+///
+/// let d = Triangular::new(0., 5., 2.5).unwrap();
+/// let v = d.sample(&mut rand::thread_rng());
+/// println!("{} is from a triangular distribution", v);
+/// ```
+///
+/// [`Pert`]: crate::Pert
+#[derive(Clone, Copy, Debug)]
+pub struct Triangular<N> {
+    min: N,
+    max: N,
+    mode: N,
+}
+
+/// Error type returned from [`Triangular::new`].
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum TriangularError {
+    /// `max < min` or `min` or `max` is NaN.
+    RangeTooSmall,
+    /// `mode < min` or `mode > max` or `mode` is NaN.
+    ModeRange,
+}
+
+impl<N: Float> Triangular<N>
+where Standard: Distribution<N>
+{
+    /// Set up the Triangular distribution with defined `min`, `max` and `mode`.
+    #[inline]
+    pub fn new(min: N, max: N, mode: N) -> Result<Triangular<N>, TriangularError> {
+        if !(max >= min) {
+            return Err(TriangularError::RangeTooSmall);
+        }
+        if !(mode >= min && max >= mode) {
+            return Err(TriangularError::ModeRange);
+        }
+        Ok(Triangular { min, max, mode })
+    }
+}
+
+impl<N: Float> Distribution<N> for Triangular<N>
+where Standard: Distribution<N>
+{
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        let f: N = rng.sample(Standard);
+        let diff_mode_min = self.mode - self.min;
+        let range = self.max - self.min;
+        let f_range = f * range;
+        if f_range < diff_mode_min {
+            self.min + (f_range * diff_mode_min).sqrt()
+        } else {
+            self.max - ((range - f_range) * (self.max - self.mode)).sqrt()
+        }
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use std::f64;
+    use rand::{Rng, rngs::mock};
+    use super::*;
+
+    #[test]
+    fn test_triangular() {
+        let mut half_rng = mock::StepRng::new(0x8000_0000_0000_0000, 0);
+        assert_eq!(half_rng.gen::<f64>(), 0.5);
+        for &(min, max, mode, median) in &[
+            (-1., 1., 0., 0.),
+            (1., 2., 1., 2. - 0.5f64.sqrt()),
+            (5., 25., 25., 5. + 200f64.sqrt()),
+            (1e-5, 1e5, 1e-3, 1e5 - 4999999949.5f64.sqrt()),
+            (0., 1., 0.9, 0.45f64.sqrt()),
+            (-4., -0.5, -2., -4.0 + 3.5f64.sqrt()),
+        ] {
+            println!("{} {} {} {}", min, max, mode, median);
+            let distr = Triangular::new(min, max, mode).unwrap();
+            // Test correct value at median:
+            assert_eq!(distr.sample(&mut half_rng), median);
+        }
+
+        for &(min, max, mode) in &[
+            (-1., 1., 2.),
+            (-1., 1., -2.),
+            (2., 1., 1.),
+        ] {
+            assert!(Triangular::new(min, max, mode).is_err());
+        }
+    }
+    
+    #[test]
+    fn value_stability() {
+        let rng = crate::test::rng(860);
+        let distr = Triangular::new(2., 10., 3.).unwrap();
+        let seq = distr.sample_iter(rng).take(5).collect::<Vec<f64>>();
+        println!("seq: {:?}", seq);
+        let expected = vec![5.74373257511361, 7.890059162791258,
+                4.7256280652553455, 2.9474808121184077, 3.058301946314053];
+        assert!(seq == expected);
+    }
+}
diff --git a/rand/rand_distr/src/unit_ball.rs b/rand/rand_distr/src/unit_ball.rs
new file mode 100644
index 0000000..9d61627
--- /dev/null
+++ b/rand/rand_distr/src/unit_ball.rs
@@ -0,0 +1,69 @@
+// Copyright 2019 Developers of the Rand project.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+use rand::Rng;
+use crate::{Distribution, Uniform, uniform::SampleUniform};
+use crate::utils::Float;
+
+/// Samples uniformly from the unit ball (surface and interior) in three
+/// dimensions.
+///
+/// Implemented via rejection sampling.
+///
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{UnitBall, Distribution};
+///
+/// let v: [f64; 3] = UnitBall.sample(&mut rand::thread_rng());
+/// println!("{:?} is from the unit ball.", v)
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct UnitBall;
+
+impl<N: Float + SampleUniform> Distribution<[N; 3]> for UnitBall {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> [N; 3] {
+        let uniform = Uniform::new(N::from(-1.), N::from(1.));
+        let mut x1;
+        let mut x2;
+        let mut x3;
+        loop {
+            x1 = uniform.sample(rng);
+            x2 = uniform.sample(rng);
+            x3 = uniform.sample(rng);
+            if x1*x1 + x2*x2 + x3*x3 <= N::from(1.) {
+                break;
+            }
+        }
+        [x1, x2, x3]
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use crate::Distribution;
+    use super::UnitBall;
+
+    #[test]
+    fn value_stability() {
+        let mut rng = crate::test::rng(2);
+        let expected = [
+                [0.018035709265959987, -0.4348771383120438, -0.07982762085055706],
+                [0.10588569388223945, -0.4734350111375454, -0.7392104908825501],
+                [0.11060237642041049, -0.16065642822852677, -0.8444043930440075]
+            ];
+        let samples: [[f64; 3]; 3] = [
+                UnitBall.sample(&mut rng),
+                UnitBall.sample(&mut rng),
+                UnitBall.sample(&mut rng),
+            ];
+        assert_eq!(samples, expected);
+    }
+}
diff --git a/rand/rand_distr/src/unit_circle.rs b/rand/rand_distr/src/unit_circle.rs
new file mode 100644
index 0000000..5863a1a
--- /dev/null
+++ b/rand/rand_distr/src/unit_circle.rs
@@ -0,0 +1,99 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+use rand::Rng;
+use crate::{Distribution, Uniform, uniform::SampleUniform};
+use crate::utils::Float;
+
+/// Samples uniformly from the edge of the unit circle in two dimensions.
+///
+/// Implemented via a method by von Neumann[^1].
+///
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{UnitCircle, Distribution};
+///
+/// let v: [f64; 2] = UnitCircle.sample(&mut rand::thread_rng());
+/// println!("{:?} is from the unit circle.", v)
+/// ```
+///
+/// [^1]: von Neumann, J. (1951) [*Various Techniques Used in Connection with
+///       Random Digits.*](https://mcnp.lanl.gov/pdf_files/nbs_vonneumann.pdf)
+///       NBS Appl. Math. Ser., No. 12. Washington, DC: U.S. Government Printing
+///       Office, pp. 36-38.
+#[derive(Clone, Copy, Debug)]
+pub struct UnitCircle;
+
+impl<N: Float + SampleUniform> Distribution<[N; 2]> for UnitCircle {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> [N; 2] {
+        let uniform = Uniform::new(N::from(-1.), N::from(1.));
+        let mut x1;
+        let mut x2;
+        let mut sum;
+        loop {
+            x1 = uniform.sample(rng);
+            x2 = uniform.sample(rng);
+            sum = x1*x1 + x2*x2;
+            if sum < N::from(1.) {
+                break;
+            }
+        }
+        let diff = x1*x1 - x2*x2;
+        [diff / sum, N::from(2.)*x1*x2 / sum]
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use crate::Distribution;
+    use super::UnitCircle;
+
+    /// Assert that two numbers are almost equal to each other.
+    ///
+    /// On panic, this macro will print the values of the expressions with their
+    /// debug representations.
+    macro_rules! assert_almost_eq {
+        ($a:expr, $b:expr, $prec:expr) => (
+            let diff = ($a - $b).abs();
+            if diff > $prec {
+                panic!(format!(
+                    "assertion failed: `abs(left - right) = {:.1e} < {:e}`, \
+                     (left: `{}`, right: `{}`)",
+                    diff, $prec, $a, $b));
+            }
+        );
+    }
+
+    #[test]
+    fn norm() {
+        let mut rng = crate::test::rng(1);
+        for _ in 0..1000 {
+            let x: [f64; 2] = UnitCircle.sample(&mut rng);
+            assert_almost_eq!(x[0]*x[0] + x[1]*x[1], 1., 1e-15);
+        }
+    }
+
+    #[test]
+    fn value_stability() {
+        let mut rng = crate::test::rng(2);
+        let expected = [
+                [-0.9965658683520504, -0.08280380447614634],
+                [-0.9790853270389644, -0.20345004884984505],
+                [-0.8449189758898707, 0.5348943112253227],
+            ];
+        let samples: [[f64; 2]; 3] = [
+                UnitCircle.sample(&mut rng),
+                UnitCircle.sample(&mut rng),
+                UnitCircle.sample(&mut rng),
+            ];
+        assert_eq!(samples, expected);
+    }
+}
diff --git a/rand/rand_distr/src/unit_disc.rs b/rand/rand_distr/src/unit_disc.rs
new file mode 100644
index 0000000..97abc2f
--- /dev/null
+++ b/rand/rand_distr/src/unit_disc.rs
@@ -0,0 +1,66 @@
+// Copyright 2019 Developers of the Rand project.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+use rand::Rng;
+use crate::{Distribution, Uniform, uniform::SampleUniform};
+use crate::utils::Float;
+
+/// Samples uniformly from the unit disc in two dimensions.
+///
+/// Implemented via rejection sampling.
+///
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{UnitDisc, Distribution};
+///
+/// let v: [f64; 2] = UnitDisc.sample(&mut rand::thread_rng());
+/// println!("{:?} is from the unit Disc.", v)
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct UnitDisc;
+
+impl<N: Float + SampleUniform> Distribution<[N; 2]> for UnitDisc {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> [N; 2] {
+        let uniform = Uniform::new(N::from(-1.), N::from(1.));
+        let mut x1;
+        let mut x2;
+        loop {
+            x1 = uniform.sample(rng);
+            x2 = uniform.sample(rng);
+            if x1*x1 + x2*x2 <= N::from(1.) {
+                break;
+            }
+        }
+        [x1, x2]
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use crate::Distribution;
+    use super::UnitDisc;
+
+    #[test]
+    fn value_stability() {
+        let mut rng = crate::test::rng(2);
+        let expected = [
+                [0.018035709265959987, -0.4348771383120438],
+                [-0.07982762085055706, 0.7765329819820659],
+                [0.21450745997299503, 0.7398636984333291]
+            ];
+        let samples: [[f64; 2]; 3] = [
+                UnitDisc.sample(&mut rng),
+                UnitDisc.sample(&mut rng),
+                UnitDisc.sample(&mut rng),
+            ];
+        assert_eq!(samples, expected);
+    }
+}
diff --git a/rand/rand_distr/src/unit_sphere.rs b/rand/rand_distr/src/unit_sphere.rs
new file mode 100644
index 0000000..8e0c361
--- /dev/null
+++ b/rand/rand_distr/src/unit_sphere.rs
@@ -0,0 +1,94 @@
+// Copyright 2018-2019 Developers of the Rand project.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+use rand::Rng;
+use crate::{Distribution, Uniform, uniform::SampleUniform};
+use crate::utils::Float;
+
+/// Samples uniformly from the surface of the unit sphere in three dimensions.
+///
+/// Implemented via a method by Marsaglia[^1].
+///
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{UnitSphere, Distribution};
+///
+/// let v: [f64; 3] = UnitSphere.sample(&mut rand::thread_rng());
+/// println!("{:?} is from the unit sphere surface.", v)
+/// ```
+///
+/// [^1]: Marsaglia, George (1972). [*Choosing a Point from the Surface of a
+///       Sphere.*](https://doi.org/10.1214/aoms/1177692644)
+///       Ann. Math. Statist. 43, no. 2, 645--646.
+#[derive(Clone, Copy, Debug)]
+pub struct UnitSphere;
+
+impl<N: Float + SampleUniform> Distribution<[N; 3]> for UnitSphere {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> [N; 3] {
+        let uniform = Uniform::new(N::from(-1.), N::from(1.));
+        loop {
+            let (x1, x2) = (uniform.sample(rng), uniform.sample(rng));
+            let sum = x1*x1 + x2*x2;
+            if sum >= N::from(1.) {
+                continue;
+            }
+            let factor = N::from(2.) * (N::from(1.0) - sum).sqrt();
+            return [x1 * factor, x2 * factor, N::from(1.) - N::from(2.)*sum];
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use crate::Distribution;
+    use super::UnitSphere;
+
+    /// Assert that two numbers are almost equal to each other.
+    ///
+    /// On panic, this macro will print the values of the expressions with their
+    /// debug representations.
+    macro_rules! assert_almost_eq {
+        ($a:expr, $b:expr, $prec:expr) => (
+            let diff = ($a - $b).abs();
+            if diff > $prec {
+                panic!(format!(
+                    "assertion failed: `abs(left - right) = {:.1e} < {:e}`, \
+                     (left: `{}`, right: `{}`)",
+                    diff, $prec, $a, $b));
+            }
+        );
+    }
+
+    #[test]
+    fn norm() {
+        let mut rng = crate::test::rng(1);
+        for _ in 0..1000 {
+            let x: [f64; 3] = UnitSphere.sample(&mut rng);
+            assert_almost_eq!(x[0]*x[0] + x[1]*x[1] + x[2]*x[2], 1., 1e-15);
+        }
+    }
+
+    #[test]
+    fn value_stability() {
+        let mut rng = crate::test::rng(2);
+        let expected = [
+                [0.03247542860231647, -0.7830477442152738, 0.6211131755296027],
+                [-0.09978440840914075, 0.9706650829833128, -0.21875184231323952],
+                [0.2735582468624679, 0.9435374242279655, -0.1868234852870203],
+            ];
+        let samples: [[f64; 3]; 3] = [
+                UnitSphere.sample(&mut rng),
+                UnitSphere.sample(&mut rng),
+                UnitSphere.sample(&mut rng),
+            ];
+        assert_eq!(samples, expected);
+    }
+}
diff --git a/rand/rand_distr/src/utils.rs b/rand/rand_distr/src/utils.rs
new file mode 100644
index 0000000..75b3500
--- /dev/null
+++ b/rand/rand_distr/src/utils.rs
@@ -0,0 +1,234 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! Math helper functions
+
+use rand::Rng;
+use crate::ziggurat_tables;
+use rand::distributions::hidden_export::IntoFloat;
+use core::{cmp, ops};
+
+/// Trait for floating-point scalar types
+/// 
+/// This allows many distributions to work with `f32` or `f64` parameters and is
+/// potentially extensible. Note however that the `Exp1` and `StandardNormal`
+/// distributions are implemented exclusively for `f32` and `f64`.
+/// 
+/// The bounds and methods are based purely on internal
+/// requirements, and will change as needed.
+pub trait Float: Copy + Sized + cmp::PartialOrd
+    + ops::Neg<Output = Self>
+    + ops::Add<Output = Self>
+    + ops::Sub<Output = Self>
+    + ops::Mul<Output = Self>
+    + ops::Div<Output = Self>
+    + ops::AddAssign + ops::SubAssign + ops::MulAssign + ops::DivAssign
+{
+    /// The constant π
+    fn pi() -> Self;
+    /// Support approximate representation of a f64 value
+    fn from(x: f64) -> Self;
+    /// Support converting to an unsigned integer.
+    fn to_u64(self) -> Option<u64>;
+    
+    /// Take the absolute value of self
+    fn abs(self) -> Self;
+    /// Take the largest integer less than or equal to self
+    fn floor(self) -> Self;
+    
+    /// Take the exponential of self
+    fn exp(self) -> Self;
+    /// Take the natural logarithm of self
+    fn ln(self) -> Self;
+    /// Take square root of self
+    fn sqrt(self) -> Self;
+    /// Take self to a floating-point power
+    fn powf(self, power: Self) -> Self;
+    
+    /// Take the tangent of self
+    fn tan(self) -> Self;
+    /// Take the logarithm of the gamma function of self
+    fn log_gamma(self) -> Self;
+}
+
+impl Float for f32 {
+    #[inline]
+    fn pi() -> Self { core::f32::consts::PI }
+    #[inline]
+    fn from(x: f64) -> Self { x as f32 }
+    #[inline]
+    fn to_u64(self) -> Option<u64> {
+        if self >= 0. && self <= ::core::u64::MAX as f32 {
+            Some(self as u64)
+        } else {
+            None
+        }
+    }
+    
+    #[inline]
+    fn abs(self) -> Self { self.abs() }
+    #[inline]
+    fn floor(self) -> Self { self.floor() }
+    
+    #[inline]
+    fn exp(self) -> Self { self.exp() }
+    #[inline]
+    fn ln(self) -> Self { self.ln() }
+    #[inline]
+    fn sqrt(self) -> Self { self.sqrt() }
+    #[inline]
+    fn powf(self, power: Self) -> Self { self.powf(power) }
+    
+    #[inline]
+    fn tan(self) -> Self { self.tan() }
+    #[inline]
+    fn log_gamma(self) -> Self {
+        let result = log_gamma(self.into());
+        assert!(result <= ::core::f32::MAX.into());
+        assert!(result >= ::core::f32::MIN.into());
+        result as f32
+    }
+}
+
+impl Float for f64 {
+    #[inline]
+    fn pi() -> Self { core::f64::consts::PI }
+    #[inline]
+    fn from(x: f64) -> Self { x }
+    #[inline]
+    fn to_u64(self) -> Option<u64> {
+        if self >= 0. && self <= ::core::u64::MAX as f64 {
+            Some(self as u64)
+        } else {
+            None
+        }
+    }
+    
+    #[inline]
+    fn abs(self) -> Self { self.abs() }
+    #[inline]
+    fn floor(self) -> Self { self.floor() }
+    
+    #[inline]
+    fn exp(self) -> Self { self.exp() }
+    #[inline]
+    fn ln(self) -> Self { self.ln() }
+    #[inline]
+    fn sqrt(self) -> Self { self.sqrt() }
+    #[inline]
+    fn powf(self, power: Self) -> Self { self.powf(power) }
+    
+    #[inline]
+    fn tan(self) -> Self { self.tan() }
+    #[inline]
+    fn log_gamma(self) -> Self { log_gamma(self) }
+}
+
+/// Calculates ln(gamma(x)) (natural logarithm of the gamma
+/// function) using the Lanczos approximation.
+///
+/// The approximation expresses the gamma function as:
+/// `gamma(z+1) = sqrt(2*pi)*(z+g+0.5)^(z+0.5)*exp(-z-g-0.5)*Ag(z)`
+/// `g` is an arbitrary constant; we use the approximation with `g=5`.
+///
+/// Noting that `gamma(z+1) = z*gamma(z)` and applying `ln` to both sides:
+/// `ln(gamma(z)) = (z+0.5)*ln(z+g+0.5)-(z+g+0.5) + ln(sqrt(2*pi)*Ag(z)/z)`
+///
+/// `Ag(z)` is an infinite series with coefficients that can be calculated
+/// ahead of time - we use just the first 6 terms, which is good enough
+/// for most purposes.
+pub(crate) fn log_gamma(x: f64) -> f64 {
+    // precalculated 6 coefficients for the first 6 terms of the series
+    let coefficients: [f64; 6] = [
+        76.18009172947146,
+        -86.50532032941677,
+        24.01409824083091,
+        -1.231739572450155,
+        0.1208650973866179e-2,
+        -0.5395239384953e-5,
+    ];
+
+    // (x+0.5)*ln(x+g+0.5)-(x+g+0.5)
+    let tmp = x + 5.5;
+    let log = (x + 0.5) * tmp.ln() - tmp;
+
+    // the first few terms of the series for Ag(x)
+    let mut a = 1.000000000190015;
+    let mut denom = x;
+    for &coeff in &coefficients {
+        denom += 1.0;
+        a += coeff / denom;
+    }
+
+    // get everything together
+    // a is Ag(x)
+    // 2.5066... is sqrt(2pi)
+    log + (2.5066282746310005 * a / x).ln()
+}
+
+/// Sample a random number using the Ziggurat method (specifically the
+/// ZIGNOR variant from Doornik 2005). Most of the arguments are
+/// directly from the paper:
+///
+/// * `rng`: source of randomness
+/// * `symmetric`: whether this is a symmetric distribution, or one-sided with P(x < 0) = 0.
+/// * `X`: the $x_i$ abscissae.
+/// * `F`: precomputed values of the PDF at the $x_i$, (i.e. $f(x_i)$)
+/// * `F_DIFF`: precomputed values of $f(x_i) - f(x_{i+1})$
+/// * `pdf`: the probability density function
+/// * `zero_case`: manual sampling from the tail when we chose the
+///    bottom box (i.e. i == 0)
+
+// the perf improvement (25-50%) is definitely worth the extra code
+// size from force-inlining.
+#[inline(always)]
+pub(crate) fn ziggurat<R: Rng + ?Sized, P, Z>(
+            rng: &mut R,
+            symmetric: bool,
+            x_tab: ziggurat_tables::ZigTable,
+            f_tab: ziggurat_tables::ZigTable,
+            mut pdf: P,
+            mut zero_case: Z)
+            -> f64 where P: FnMut(f64) -> f64, Z: FnMut(&mut R, f64) -> f64 {
+    loop {
+        // As an optimisation we re-implement the conversion to a f64.
+        // From the remaining 12 most significant bits we use 8 to construct `i`.
+        // This saves us generating a whole extra random number, while the added
+        // precision of using 64 bits for f64 does not buy us much.
+        let bits = rng.next_u64();
+        let i = bits as usize & 0xff;
+
+        let u = if symmetric {
+            // Convert to a value in the range [2,4) and substract to get [-1,1)
+            // We can't convert to an open range directly, that would require
+            // substracting `3.0 - EPSILON`, which is not representable.
+            // It is possible with an extra step, but an open range does not
+            // seem neccesary for the ziggurat algorithm anyway.
+            (bits >> 12).into_float_with_exponent(1) - 3.0
+        } else {
+            // Convert to a value in the range [1,2) and substract to get (0,1)
+            (bits >> 12).into_float_with_exponent(0)
+            - (1.0 - std::f64::EPSILON / 2.0)
+        };
+        let x = u * x_tab[i];
+
+        let test_x = if symmetric { x.abs() } else {x};
+
+        // algebraically equivalent to |u| < x_tab[i+1]/x_tab[i] (or u < x_tab[i+1]/x_tab[i])
+        if test_x < x_tab[i + 1] {
+            return x;
+        }
+        if i == 0 {
+            return zero_case(rng, u);
+        }
+        // algebraically equivalent to f1 + DRanU()*(f0 - f1) < 1
+        if f_tab[i + 1] + (f_tab[i] - f_tab[i + 1]) * rng.gen::<f64>() < pdf(x) {
+            return x;
+        }
+    }
+}
diff --git a/rand/rand_distr/src/weibull.rs b/rand/rand_distr/src/weibull.rs
new file mode 100644
index 0000000..ddde380
--- /dev/null
+++ b/rand/rand_distr/src/weibull.rs
@@ -0,0 +1,86 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The Weibull distribution.
+
+use rand::Rng;
+use crate::{Distribution, OpenClosed01};
+use crate::utils::Float;
+
+/// Samples floating-point numbers according to the Weibull distribution
+///
+/// # Example
+/// ```
+/// use rand::prelude::*;
+/// use rand_distr::Weibull;
+///
+/// let val: f64 = thread_rng().sample(Weibull::new(1., 10.).unwrap());
+/// println!("{}", val);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct Weibull<N> {
+    inv_shape: N,
+    scale: N,
+}
+
+/// Error type returned from `Weibull::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum Error {
+    /// `scale <= 0` or `nan`.
+    ScaleTooSmall,
+    /// `shape <= 0` or `nan`.
+    ShapeTooSmall,
+}
+
+impl<N: Float> Weibull<N>
+where OpenClosed01: Distribution<N>
+{
+    /// Construct a new `Weibull` distribution with given `scale` and `shape`.
+    pub fn new(scale: N, shape: N) -> Result<Weibull<N>, Error> {
+        if !(scale > N::from(0.0)) {
+            return Err(Error::ScaleTooSmall);
+        }
+        if !(shape > N::from(0.0)) {
+            return Err(Error::ShapeTooSmall);
+        }
+        Ok(Weibull { inv_shape: N::from(1.)/shape, scale })
+    }
+}
+
+impl<N: Float> Distribution<N> for Weibull<N>
+where OpenClosed01: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        let x: N = rng.sample(OpenClosed01);
+        self.scale * (-x.ln()).powf(self.inv_shape)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use crate::Distribution;
+    use super::Weibull;
+
+    #[test]
+    #[should_panic]
+    fn invalid() {
+        Weibull::new(0., 0.).unwrap();
+    }
+
+    #[test]
+    fn sample() {
+        let scale = 1.0;
+        let shape = 2.0;
+        let d = Weibull::new(scale, shape).unwrap();
+        let mut rng = crate::test::rng(1);
+        for _ in 0..1000 {
+            let r = d.sample(&mut rng);
+            assert!(r >= 0.);
+        }
+    }
+}
diff --git a/rand/rand_distr/src/ziggurat_tables.rs b/rand/rand_distr/src/ziggurat_tables.rs
new file mode 100644
index 0000000..ca1ce30
--- /dev/null
+++ b/rand/rand_distr/src/ziggurat_tables.rs
@@ -0,0 +1,279 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 The Rust Project Developers.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+// Tables for distributions which are sampled using the ziggurat
+// algorithm. Autogenerated by `ziggurat_tables.py`.
+
+pub type ZigTable = &'static [f64; 257];
+pub const ZIG_NORM_R: f64 = 3.654152885361008796;
+pub static ZIG_NORM_X: [f64; 257] =
+    [3.910757959537090045, 3.654152885361008796, 3.449278298560964462, 3.320244733839166074,
+     3.224575052047029100, 3.147889289517149969, 3.083526132001233044, 3.027837791768635434,
+     2.978603279880844834, 2.934366867207854224, 2.894121053612348060, 2.857138730872132548,
+     2.822877396825325125, 2.790921174000785765, 2.760944005278822555, 2.732685359042827056,
+     2.705933656121858100, 2.680514643284522158, 2.656283037575502437, 2.633116393630324570,
+     2.610910518487548515, 2.589575986706995181, 2.569035452680536569, 2.549221550323460761,
+     2.530075232158516929, 2.511544441625342294, 2.493583041269680667, 2.476149939669143318,
+     2.459208374333311298, 2.442725318198956774, 2.426670984935725972, 2.411018413899685520,
+     2.395743119780480601, 2.380822795170626005, 2.366237056715818632, 2.351967227377659952,
+     2.337996148795031370, 2.324308018869623016, 2.310888250599850036, 2.297723348901329565,
+     2.284800802722946056, 2.272108990226823888, 2.259637095172217780, 2.247375032945807760,
+     2.235313384928327984, 2.223443340090905718, 2.211756642882544366, 2.200245546609647995,
+     2.188902771624720689, 2.177721467738641614, 2.166695180352645966, 2.155817819875063268,
+     2.145083634046203613, 2.134487182844320152, 2.124023315687815661, 2.113687150684933957,
+     2.103474055713146829, 2.093379631137050279, 2.083399693996551783, 2.073530263516978778,
+     2.063767547809956415, 2.054107931648864849, 2.044547965215732788, 2.035084353727808715,
+     2.025713947862032960, 2.016433734904371722, 2.007240830558684852, 1.998132471356564244,
+     1.989106007615571325, 1.980158896898598364, 1.971288697931769640, 1.962493064942461896,
+     1.953769742382734043, 1.945116560006753925, 1.936531428273758904, 1.928012334050718257,
+     1.919557336591228847, 1.911164563769282232, 1.902832208548446369, 1.894558525668710081,
+     1.886341828534776388, 1.878180486290977669, 1.870072921069236838, 1.862017605397632281,
+     1.854013059758148119, 1.846057850283119750, 1.838150586580728607, 1.830289919680666566,
+     1.822474540091783224, 1.814703175964167636, 1.806974591348693426, 1.799287584547580199,
+     1.791640986550010028, 1.784033659547276329, 1.776464495522344977, 1.768932414909077933,
+     1.761436365316706665, 1.753975320315455111, 1.746548278279492994, 1.739154261283669012,
+     1.731792314050707216, 1.724461502945775715, 1.717160915015540690, 1.709889657069006086,
+     1.702646854797613907, 1.695431651932238548, 1.688243209434858727, 1.681080704722823338,
+     1.673943330923760353, 1.666830296159286684, 1.659740822855789499, 1.652674147080648526,
+     1.645629517902360339, 1.638606196773111146, 1.631603456932422036, 1.624620582830568427,
+     1.617656869570534228, 1.610711622367333673, 1.603784156023583041, 1.596873794420261339,
+     1.589979870021648534, 1.583101723393471438, 1.576238702733332886, 1.569390163412534456,
+     1.562555467528439657, 1.555733983466554893, 1.548925085471535512, 1.542128153226347553,
+     1.535342571438843118, 1.528567729435024614, 1.521803020758293101, 1.515047842773992404,
+     1.508301596278571965, 1.501563685112706548, 1.494833515777718391, 1.488110497054654369,
+     1.481394039625375747, 1.474683555695025516, 1.467978458615230908, 1.461278162507407830,
+     1.454582081885523293, 1.447889631277669675, 1.441200224845798017, 1.434513276002946425,
+     1.427828197027290358, 1.421144398672323117, 1.414461289772464658, 1.407778276843371534,
+     1.401094763676202559, 1.394410150925071257, 1.387723835686884621, 1.381035211072741964,
+     1.374343665770030531, 1.367648583594317957, 1.360949343030101844, 1.354245316759430606,
+     1.347535871177359290, 1.340820365893152122, 1.334098153216083604, 1.327368577624624679,
+     1.320630975217730096, 1.313884673146868964, 1.307128989027353860, 1.300363230327433728,
+     1.293586693733517645, 1.286798664489786415, 1.279998415710333237, 1.273185207661843732,
+     1.266358287014688333, 1.259516886060144225, 1.252660221891297887, 1.245787495544997903,
+     1.238897891102027415, 1.231990574742445110, 1.225064693752808020, 1.218119375481726552,
+     1.211153726239911244, 1.204166830140560140, 1.197157747875585931, 1.190125515422801650,
+     1.183069142678760732, 1.175987612011489825, 1.168879876726833800, 1.161744859441574240,
+     1.154581450355851802, 1.147388505416733873, 1.140164844363995789, 1.132909248648336975,
+     1.125620459211294389, 1.118297174115062909, 1.110938046009249502, 1.103541679420268151,
+     1.096106627847603487, 1.088631390649514197, 1.081114409698889389, 1.073554065787871714,
+     1.065948674757506653, 1.058296483326006454, 1.050595664586207123, 1.042844313139370538,
+     1.035040439828605274, 1.027181966030751292, 1.019266717460529215, 1.011292417434978441,
+     1.003256679539591412, 0.995156999629943084, 0.986990747093846266, 0.978755155288937750,
+     0.970447311058864615, 0.962064143217605250, 0.953602409875572654, 0.945058684462571130,
+     0.936429340280896860, 0.927710533396234771, 0.918898183643734989, 0.909987953490768997,
+     0.900975224455174528, 0.891855070726792376, 0.882622229578910122, 0.873271068082494550,
+     0.863795545546826915, 0.854189171001560554, 0.844444954902423661, 0.834555354079518752,
+     0.824512208745288633, 0.814306670128064347, 0.803929116982664893, 0.793369058833152785,
+     0.782615023299588763, 0.771654424216739354, 0.760473406422083165, 0.749056662009581653,
+     0.737387211425838629, 0.725446140901303549, 0.713212285182022732, 0.700661841097584448,
+     0.687767892786257717, 0.674499822827436479, 0.660822574234205984, 0.646695714884388928,
+     0.632072236375024632, 0.616896989996235545, 0.601104617743940417, 0.584616766093722262,
+     0.567338257040473026, 0.549151702313026790, 0.529909720646495108, 0.509423329585933393,
+     0.487443966121754335, 0.463634336771763245, 0.437518402186662658, 0.408389134588000746,
+     0.375121332850465727, 0.335737519180459465, 0.286174591747260509, 0.215241895913273806,
+     0.000000000000000000];
+pub static ZIG_NORM_F: [f64; 257] =
+    [0.000477467764586655, 0.001260285930498598, 0.002609072746106363, 0.004037972593371872,
+     0.005522403299264754, 0.007050875471392110, 0.008616582769422917, 0.010214971439731100,
+     0.011842757857943104, 0.013497450601780807, 0.015177088307982072, 0.016880083152595839,
+     0.018605121275783350, 0.020351096230109354, 0.022117062707379922, 0.023902203305873237,
+     0.025705804008632656, 0.027527235669693315, 0.029365939758230111, 0.031221417192023690,
+     0.033093219458688698, 0.034980941461833073, 0.036884215688691151, 0.038802707404656918,
+     0.040736110656078753, 0.042684144916619378, 0.044646552251446536, 0.046623094902089664,
+     0.048613553216035145, 0.050617723861121788, 0.052635418276973649, 0.054666461325077916,
+     0.056710690106399467, 0.058767952921137984, 0.060838108349751806, 0.062921024437977854,
+     0.065016577971470438, 0.067124653828023989, 0.069245144397250269, 0.071377949059141965,
+     0.073522973714240991, 0.075680130359194964, 0.077849336702372207, 0.080030515814947509,
+     0.082223595813495684, 0.084428509570654661, 0.086645194450867782, 0.088873592068594229,
+     0.091113648066700734, 0.093365311913026619, 0.095628536713353335, 0.097903279039215627,
+     0.100189498769172020, 0.102487158942306270, 0.104796225622867056, 0.107116667775072880,
+     0.109448457147210021, 0.111791568164245583, 0.114145977828255210, 0.116511665626037014,
+     0.118888613443345698, 0.121276805485235437, 0.123676228202051403, 0.126086870220650349,
+     0.128508722280473636, 0.130941777174128166, 0.133386029692162844, 0.135841476571757352,
+     0.138308116449064322, 0.140785949814968309, 0.143274978974047118, 0.145775208006537926,
+     0.148286642733128721, 0.150809290682410169, 0.153343161060837674, 0.155888264725064563,
+     0.158444614156520225, 0.161012223438117663, 0.163591108232982951, 0.166181285765110071,
+     0.168782774801850333, 0.171395595638155623, 0.174019770082499359, 0.176655321444406654,
+     0.179302274523530397, 0.181960655600216487, 0.184630492427504539, 0.187311814224516926,
+     0.190004651671193070, 0.192709036904328807, 0.195425003514885592, 0.198152586546538112,
+     0.200891822495431333, 0.203642749311121501, 0.206405406398679298, 0.209179834621935651,
+     0.211966076307852941, 0.214764175252008499, 0.217574176725178370, 0.220396127481011589,
+     0.223230075764789593, 0.226076071323264877, 0.228934165415577484, 0.231804410825248525,
+     0.234686861873252689, 0.237581574432173676, 0.240488605941449107, 0.243408015423711988,
+     0.246339863502238771, 0.249284212419516704, 0.252241126056943765, 0.255210669955677150,
+     0.258192911338648023, 0.261187919133763713, 0.264195763998317568, 0.267216518344631837,
+     0.270250256366959984, 0.273297054069675804, 0.276356989296781264, 0.279430141762765316,
+     0.282516593084849388, 0.285616426816658109, 0.288729728483353931, 0.291856585618280984,
+     0.294997087801162572, 0.298151326697901342, 0.301319396102034120, 0.304501391977896274,
+     0.307697412505553769, 0.310907558127563710, 0.314131931597630143, 0.317370638031222396,
+     0.320623784958230129, 0.323891482377732021, 0.327173842814958593, 0.330470981380537099,
+     0.333783015832108509, 0.337110066638412809, 0.340452257045945450, 0.343809713148291340,
+     0.347182563958251478, 0.350570941482881204, 0.353974980801569250, 0.357394820147290515,
+     0.360830600991175754, 0.364282468130549597, 0.367750569780596226, 0.371235057669821344,
+     0.374736087139491414, 0.378253817247238111, 0.381788410875031348, 0.385340034841733958,
+     0.388908860020464597, 0.392495061461010764, 0.396098818517547080, 0.399720314981931668,
+     0.403359739222868885, 0.407017284331247953, 0.410693148271983222, 0.414387534042706784,
+     0.418100649839684591, 0.421832709231353298, 0.425583931339900579, 0.429354541031341519,
+     0.433144769114574058, 0.436954852549929273, 0.440785034667769915, 0.444635565397727750,
+     0.448506701509214067, 0.452398706863882505, 0.456311852680773566, 0.460246417814923481,
+     0.464202689050278838, 0.468180961407822172, 0.472181538469883255, 0.476204732721683788,
+     0.480250865911249714, 0.484320269428911598, 0.488413284707712059, 0.492530263646148658,
+     0.496671569054796314, 0.500837575128482149, 0.505028667945828791, 0.509245245998136142,
+     0.513487720749743026, 0.517756517232200619, 0.522052074674794864, 0.526374847174186700,
+     0.530725304406193921, 0.535103932383019565, 0.539511234259544614, 0.543947731192649941,
+     0.548413963257921133, 0.552910490428519918, 0.557437893621486324, 0.561996775817277916,
+     0.566587763258951771, 0.571211506738074970, 0.575868682975210544, 0.580559996103683473,
+     0.585286179266300333, 0.590047996335791969, 0.594846243770991268, 0.599681752622167719,
+     0.604555390700549533, 0.609468064928895381, 0.614420723892076803, 0.619414360609039205,
+     0.624450015550274240, 0.629528779928128279, 0.634651799290960050, 0.639820277456438991,
+     0.645035480824251883, 0.650298743114294586, 0.655611470583224665, 0.660975147780241357,
+     0.666391343912380640, 0.671861719900766374, 0.677388036222513090, 0.682972161648791376,
+     0.688616083008527058, 0.694321916130032579, 0.700091918140490099, 0.705928501336797409,
+     0.711834248882358467, 0.717811932634901395, 0.723864533472881599, 0.729995264565802437,
+     0.736207598131266683, 0.742505296344636245, 0.748892447223726720, 0.755373506511754500,
+     0.761953346841546475, 0.768637315803334831, 0.775431304986138326, 0.782341832659861902,
+     0.789376143571198563, 0.796542330428254619, 0.803849483176389490, 0.811307874318219935,
+     0.818929191609414797, 0.826726833952094231, 0.834716292992930375, 0.842915653118441077,
+     0.851346258465123684, 0.860033621203008636, 0.869008688043793165, 0.878309655816146839,
+     0.887984660763399880, 0.898095921906304051, 0.908726440060562912, 0.919991505048360247,
+     0.932060075968990209, 0.945198953453078028, 0.959879091812415930, 0.977101701282731328,
+     1.000000000000000000];
+pub const ZIG_EXP_R: f64 = 7.697117470131050077;
+pub static ZIG_EXP_X: [f64; 257] =
+    [8.697117470131052741, 7.697117470131050077, 6.941033629377212577, 6.478378493832569696,
+     6.144164665772472667, 5.882144315795399869, 5.666410167454033697, 5.482890627526062488,
+     5.323090505754398016, 5.181487281301500047, 5.054288489981304089, 4.938777085901250530,
+     4.832939741025112035, 4.735242996601741083, 4.644491885420085175, 4.559737061707351380,
+     4.480211746528421912, 4.405287693473573185, 4.334443680317273007, 4.267242480277365857,
+     4.203313713735184365, 4.142340865664051464, 4.084051310408297830, 4.028208544647936762,
+     3.974606066673788796, 3.923062500135489739, 3.873417670399509127, 3.825529418522336744,
+     3.779270992411667862, 3.734528894039797375, 3.691201090237418825, 3.649195515760853770,
+     3.608428813128909507, 3.568825265648337020, 3.530315889129343354, 3.492837654774059608,
+     3.456332821132760191, 3.420748357251119920, 3.386035442460300970, 3.352149030900109405,
+     3.319047470970748037, 3.286692171599068679, 3.255047308570449882, 3.224079565286264160,
+     3.193757903212240290, 3.164053358025972873, 3.134938858084440394, 3.106389062339824481,
+     3.078380215254090224, 3.050890016615455114, 3.023897504455676621, 2.997382949516130601,
+     2.971327759921089662, 2.945714394895045718, 2.920526286512740821, 2.895747768600141825,
+     2.871364012015536371, 2.847360965635188812, 2.823725302450035279, 2.800444370250737780,
+     2.777506146439756574, 2.754899196562344610, 2.732612636194700073, 2.710636095867928752,
+     2.688959688741803689, 2.667573980773266573, 2.646469963151809157, 2.625639026797788489,
+     2.605072938740835564, 2.584763820214140750, 2.564704126316905253, 2.544886627111869970,
+     2.525304390037828028, 2.505950763528594027, 2.486819361740209455, 2.467904050297364815,
+     2.449198932978249754, 2.430698339264419694, 2.412396812688870629, 2.394289099921457886,
+     2.376370140536140596, 2.358635057409337321, 2.341079147703034380, 2.323697874390196372,
+     2.306486858283579799, 2.289441870532269441, 2.272558825553154804, 2.255833774367219213,
+     2.239262898312909034, 2.222842503111036816, 2.206569013257663858, 2.190438966723220027,
+     2.174449009937774679, 2.158595893043885994, 2.142876465399842001, 2.127287671317368289,
+     2.111826546019042183, 2.096490211801715020, 2.081275874393225145, 2.066180819490575526,
+     2.051202409468584786, 2.036338080248769611, 2.021585338318926173, 2.006941757894518563,
+     1.992404978213576650, 1.977972700957360441, 1.963642687789548313, 1.949412758007184943,
+     1.935280786297051359, 1.921244700591528076, 1.907302480018387536, 1.893452152939308242,
+     1.879691795072211180, 1.866019527692827973, 1.852433515911175554, 1.838931967018879954,
+     1.825513128903519799, 1.812175288526390649, 1.798916770460290859, 1.785735935484126014,
+     1.772631179231305643, 1.759600930889074766, 1.746643651946074405, 1.733757834985571566,
+     1.720942002521935299, 1.708194705878057773, 1.695514524101537912, 1.682900062917553896,
+     1.670349953716452118, 1.657862852574172763, 1.645437439303723659, 1.633072416535991334,
+     1.620766508828257901, 1.608518461798858379, 1.596327041286483395, 1.584191032532688892,
+     1.572109239386229707, 1.560080483527888084, 1.548103603714513499, 1.536177455041032092,
+     1.524300908219226258, 1.512472848872117082, 1.500692176842816750, 1.488957805516746058,
+     1.477268661156133867, 1.465623682245745352, 1.454021818848793446, 1.442462031972012504,
+     1.430943292938879674, 1.419464582769983219, 1.408024891569535697, 1.396623217917042137,
+     1.385258568263121992, 1.373929956328490576, 1.362636402505086775, 1.351376933258335189,
+     1.340150580529504643, 1.328956381137116560, 1.317793376176324749, 1.306660610415174117,
+     1.295557131686601027, 1.284481990275012642, 1.273434238296241139, 1.262412929069615330,
+     1.251417116480852521, 1.240445854334406572, 1.229498195693849105, 1.218573192208790124,
+     1.207669893426761121, 1.196787346088403092, 1.185924593404202199, 1.175080674310911677,
+     1.164254622705678921, 1.153445466655774743, 1.142652227581672841, 1.131873919411078511,
+     1.121109547701330200, 1.110358108727411031, 1.099618588532597308, 1.088889961938546813,
+     1.078171191511372307, 1.067461226479967662, 1.056759001602551429, 1.046063435977044209,
+     1.035373431790528542, 1.024687873002617211, 1.014005623957096480, 1.003325527915696735,
+     0.992646405507275897, 0.981967053085062602, 0.971286240983903260, 0.960602711668666509,
+     0.949915177764075969, 0.939222319955262286, 0.928522784747210395, 0.917815182070044311,
+     0.907098082715690257, 0.896370015589889935, 0.885629464761751528, 0.874874866291025066,
+     0.864104604811004484, 0.853317009842373353, 0.842510351810368485, 0.831682837734273206,
+     0.820832606554411814, 0.809957724057418282, 0.799056177355487174, 0.788125868869492430,
+     0.777164609759129710, 0.766170112735434672, 0.755139984181982249, 0.744071715500508102,
+     0.732962673584365398, 0.721810090308756203, 0.710611050909655040, 0.699362481103231959,
+     0.688061132773747808, 0.676703568029522584, 0.665286141392677943, 0.653804979847664947,
+     0.642255960424536365, 0.630634684933490286, 0.618936451394876075, 0.607156221620300030,
+     0.595288584291502887, 0.583327712748769489, 0.571267316532588332, 0.559100585511540626,
+     0.546820125163310577, 0.534417881237165604, 0.521885051592135052, 0.509211982443654398,
+     0.496388045518671162, 0.483401491653461857, 0.470239275082169006, 0.456886840931420235,
+     0.443327866073552401, 0.429543940225410703, 0.415514169600356364, 0.401214678896277765,
+     0.386617977941119573, 0.371692145329917234, 0.356399760258393816, 0.340696481064849122,
+     0.324529117016909452, 0.307832954674932158, 0.290527955491230394, 0.272513185478464703,
+     0.253658363385912022, 0.233790483059674731, 0.212671510630966620, 0.189958689622431842,
+     0.165127622564187282, 0.137304980940012589, 0.104838507565818778, 0.063852163815001570,
+     0.000000000000000000];
+pub static ZIG_EXP_F: [f64; 257] =
+    [0.000167066692307963, 0.000454134353841497, 0.000967269282327174, 0.001536299780301573,
+     0.002145967743718907, 0.002788798793574076, 0.003460264777836904, 0.004157295120833797,
+     0.004877655983542396, 0.005619642207205489, 0.006381905937319183, 0.007163353183634991,
+     0.007963077438017043, 0.008780314985808977, 0.009614413642502212, 0.010464810181029981,
+     0.011331013597834600, 0.012212592426255378, 0.013109164931254991, 0.014020391403181943,
+     0.014945968011691148, 0.015885621839973156, 0.016839106826039941, 0.017806200410911355,
+     0.018786700744696024, 0.019780424338009740, 0.020787204072578114, 0.021806887504283581,
+     0.022839335406385240, 0.023884420511558174, 0.024942026419731787, 0.026012046645134221,
+     0.027094383780955803, 0.028188948763978646, 0.029295660224637411, 0.030414443910466622,
+     0.031545232172893622, 0.032687963508959555, 0.033842582150874358, 0.035009037697397431,
+     0.036187284781931443, 0.037377282772959382, 0.038578995503074871, 0.039792391023374139,
+     0.041017441380414840, 0.042254122413316254, 0.043502413568888197, 0.044762297732943289,
+     0.046033761076175184, 0.047316792913181561, 0.048611385573379504, 0.049917534282706379,
+     0.051235237055126281, 0.052564494593071685, 0.053905310196046080, 0.055257689676697030,
+     0.056621641283742870, 0.057997175631200659, 0.059384305633420280, 0.060783046445479660,
+     0.062193415408541036, 0.063615431999807376, 0.065049117786753805, 0.066494496385339816,
+     0.067951593421936643, 0.069420436498728783, 0.070901055162371843, 0.072393480875708752,
+     0.073897746992364746, 0.075413888734058410, 0.076941943170480517, 0.078481949201606435,
+     0.080033947542319905, 0.081597980709237419, 0.083174093009632397, 0.084762330532368146,
+     0.086362741140756927, 0.087975374467270231, 0.089600281910032886, 0.091237516631040197,
+     0.092887133556043569, 0.094549189376055873, 0.096223742550432825, 0.097910853311492213,
+     0.099610583670637132, 0.101322997425953631, 0.103048160171257702, 0.104786139306570145,
+     0.106537004050001632, 0.108300825451033755, 0.110077676405185357, 0.111867631670056283,
+     0.113670767882744286, 0.115487163578633506, 0.117316899211555525, 0.119160057175327641,
+     0.121016721826674792, 0.122886979509545108, 0.124770918580830933, 0.126668629437510671,
+     0.128580204545228199, 0.130505738468330773, 0.132445327901387494, 0.134399071702213602,
+     0.136367070926428829, 0.138349428863580176, 0.140346251074862399, 0.142357645432472146,
+     0.144383722160634720, 0.146424593878344889, 0.148480375643866735, 0.150551185001039839,
+     0.152637142027442801, 0.154738369384468027, 0.156854992369365148, 0.158987138969314129,
+     0.161134939917591952, 0.163298528751901734, 0.165478041874935922, 0.167673618617250081,
+     0.169885401302527550, 0.172113535315319977, 0.174358169171353411, 0.176619454590494829,
+     0.178897546572478278, 0.181192603475496261, 0.183504787097767436, 0.185834262762197083,
+     0.188181199404254262, 0.190545769663195363, 0.192928149976771296, 0.195328520679563189,
+     0.197747066105098818, 0.200183974691911210, 0.202639439093708962, 0.205113656293837654,
+     0.207606827724221982, 0.210119159388988230, 0.212650861992978224, 0.215202151075378628,
+     0.217773247148700472, 0.220364375843359439, 0.222975768058120111, 0.225607660116683956,
+     0.228260293930716618, 0.230933917169627356, 0.233628783437433291, 0.236345152457059560,
+     0.239083290262449094, 0.241843469398877131, 0.244625969131892024, 0.247431075665327543,
+     0.250259082368862240, 0.253110290015629402, 0.255985007030415324, 0.258883549749016173,
+     0.261806242689362922, 0.264753418835062149, 0.267725419932044739, 0.270722596799059967,
+     0.273745309652802915, 0.276793928448517301, 0.279868833236972869, 0.282970414538780746,
+     0.286099073737076826, 0.289255223489677693, 0.292439288161892630, 0.295651704281261252,
+     0.298892921015581847, 0.302163400675693528, 0.305463619244590256, 0.308794066934560185,
+     0.312155248774179606, 0.315547685227128949, 0.318971912844957239, 0.322428484956089223,
+     0.325917972393556354, 0.329440964264136438, 0.332998068761809096, 0.336589914028677717,
+     0.340217149066780189, 0.343880444704502575, 0.347580494621637148, 0.351318016437483449,
+     0.355093752866787626, 0.358908472948750001, 0.362762973354817997, 0.366658079781514379,
+     0.370594648435146223, 0.374573567615902381, 0.378595759409581067, 0.382662181496010056,
+     0.386773829084137932, 0.390931736984797384, 0.395136981833290435, 0.399390684475231350,
+     0.403694012530530555, 0.408048183152032673, 0.412454465997161457, 0.416914186433003209,
+     0.421428728997616908, 0.425999541143034677, 0.430628137288459167, 0.435316103215636907,
+     0.440065100842354173, 0.444876873414548846, 0.449753251162755330, 0.454696157474615836,
+     0.459707615642138023, 0.464789756250426511, 0.469944825283960310, 0.475175193037377708,
+     0.480483363930454543, 0.485871987341885248, 0.491343869594032867, 0.496901987241549881,
+     0.502549501841348056, 0.508289776410643213, 0.514126393814748894, 0.520063177368233931,
+     0.526104213983620062, 0.532253880263043655, 0.538516872002862246, 0.544898237672440056,
+     0.551403416540641733, 0.558038282262587892, 0.564809192912400615, 0.571723048664826150,
+     0.578787358602845359, 0.586010318477268366, 0.593400901691733762, 0.600968966365232560,
+     0.608725382079622346, 0.616682180915207878, 0.624852738703666200, 0.633251994214366398,
+     0.641896716427266423, 0.650805833414571433, 0.660000841079000145, 0.669506316731925177,
+     0.679350572264765806, 0.689566496117078431, 0.700192655082788606, 0.711274760805076456,
+     0.722867659593572465, 0.735038092431424039, 0.747868621985195658, 0.761463388849896838,
+     0.775956852040116218, 0.791527636972496285, 0.808421651523009044, 0.826993296643051101,
+     0.847785500623990496, 0.871704332381204705, 0.900469929925747703, 0.938143680862176477,
+     1.000000000000000000];