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diff --git a/rand/rand_distr/src/pert.rs b/rand/rand_distr/src/pert.rs
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--- a/rand/rand_distr/src/pert.rs
+++ /dev/null
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-// 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);
-    }
-}