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diff --git a/rand/src/distributions/pareto.rs b/rand/src/distributions/pareto.rs
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index 744a157..0000000
--- a/rand/src/distributions/pareto.rs
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@@ -1,74 +0,0 @@
-// 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 Rng;
-use distributions::{Distribution, OpenClosed01};
-
-/// Samples floating-point numbers according to the Pareto distribution
-///
-/// # Example
-/// ```
-/// use rand::prelude::*;
-/// use rand::distributions::Pareto;
-///
-/// let val: f64 = SmallRng::from_entropy().sample(Pareto::new(1., 2.));
-/// println!("{}", val);
-/// ```
-#[derive(Clone, Copy, Debug)]
-pub struct Pareto {
-    scale: f64,
-    inv_neg_shape: f64,
-}
-
-impl Pareto {
-    /// 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.
-    ///
-    /// # Panics
-    ///
-    /// `scale` and `shape` have to be non-zero and positive.
-    pub fn new(scale: f64, shape: f64) -> Pareto {
-        assert!((scale > 0.) & (shape > 0.));
-        Pareto { scale, inv_neg_shape: -1.0 / shape }
-    }
-}
-
-impl Distribution<f64> for Pareto {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        let u: f64 = rng.sample(OpenClosed01);
-        self.scale * u.powf(self.inv_neg_shape)
-    }
-}
-
-#[cfg(test)]
-mod tests {
-    use distributions::Distribution;
-    use super::Pareto;
-
-    #[test]
-    #[should_panic]
-    fn invalid() {
-        Pareto::new(0., 0.);
-    }
-
-    #[test]
-    fn sample() {
-        let scale = 1.0;
-        let shape = 2.0;
-        let d = Pareto::new(scale, shape);
-        let mut rng = ::test::rng(1);
-        for _ in 0..1000 {
-            let r = d.sample(&mut rng);
-            assert!(r >= scale);
-        }
-    }
-}