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diff --git a/rand/src/distributions/cauchy.rs b/rand/src/distributions/cauchy.rs
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--- a/rand/src/distributions/cauchy.rs
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@@ -1,115 +0,0 @@
-// 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 Rng;
-use distributions::Distribution;
-use std::f64::consts::PI;
-
-/// 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::distributions::{Cauchy, Distribution};
-///
-/// let cau = Cauchy::new(2.0, 5.0);
-/// let v = cau.sample(&mut rand::thread_rng());
-/// println!("{} is from a Cauchy(2, 5) distribution", v);
-/// ```
-#[derive(Clone, Copy, Debug)]
-pub struct Cauchy {
-    median: f64,
-    scale: f64
-}
-
-impl Cauchy {
-    /// Construct a new `Cauchy` with the given shape parameters
-    /// `median` the peak location and `scale` the scale factor.
-    /// Panics if `scale <= 0`.
-    pub fn new(median: f64, scale: f64) -> Cauchy {
-        assert!(scale > 0.0, "Cauchy::new called with scale factor <= 0");
-        Cauchy {
-            median,
-            scale
-        }
-    }
-}
-
-impl Distribution<f64> for Cauchy {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        // sample from [0, 1)
-        let x = rng.gen::<f64>();
-        // 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 = (PI * x).tan();
-        // shift and scale according to parameters
-        let result = self.median + self.scale * comp_dev;
-        result
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use distributions::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_median() {
-        let cauchy = Cauchy::new(10.0, 5.0);
-        let mut rng = ::test::rng(123);
-        let mut numbers: [f64; 1000] = [0.0; 1000];
-        for i in 0..1000 {
-            numbers[i] = cauchy.sample(&mut rng);
-        }
-        let median = median(&mut numbers);
-        println!("Cauchy median: {}", median);
-        assert!((median - 10.0).abs() < 0.5); // not 100% certain, but probable enough
-    }
-
-    #[test]
-    fn test_cauchy_mean() {
-        let cauchy = Cauchy::new(10.0, 5.0);
-        let mut rng = ::test::rng(123);
-        let mut sum = 0.0;
-        for _ in 0..1000 {
-            sum += cauchy.sample(&mut rng);
-        }
-        let mean = sum / 1000.0;
-        println!("Cauchy mean: {}", mean);
-        // for a Cauchy distribution the mean should not converge
-        assert!((mean - 10.0).abs() > 0.5); // not 100% certain, but probable enough
-    }
-
-    #[test]
-    #[should_panic]
-    fn test_cauchy_invalid_scale_zero() {
-        Cauchy::new(0.0, 0.0);
-    }
-
-    #[test]
-    #[should_panic]
-    fn test_cauchy_invalid_scale_neg() {
-        Cauchy::new(0.0, -10.0);
-    }
-}