missing Copy impls for distributions.

src/distributions/gamma.rs

@@ -51,10 +51,12 @@ use super::{Distribution, Exp};
 /// for Generating Gamma Variables" *ACM Trans. Math. Softw.* 26, 3
 /// (September 2000),
 /// 363-372. DOI:[10.1145/358407.358414](http://doi.acm.org/10.1145/358407.358414)
+#[derive(Copy)]
 pub struct Gamma {
     repr: GammaRepr,
 }
 
+#[derive(Copy)]
 enum GammaRepr {
     Large(GammaLargeShape),
     One(Exp),
@@ -75,6 +77,7 @@ enum GammaRepr {
 ///
 /// See `Gamma` for sampling from a Gamma distribution with general
 /// shape parameters.
+#[derive(Copy)]
 struct GammaSmallShape {
     inv_shape: f64,
     large_shape: GammaLargeShape
@@ -84,6 +87,7 @@ struct GammaSmallShape {
 ///
 /// See `Gamma` for sampling from a Gamma distribution with general
 /// shape parameters.
+#[derive(Copy)]
 struct GammaLargeShape {
     scale: f64,
     c: f64,
@@ -182,10 +186,12 @@ impl Distribution<f64> for GammaLargeShape {
 /// let v = chi.sample(&mut rand::thread_rng());
 /// println!("{} is from a χ²(11) distribution", v)
 /// ```
+#[derive(Copy)]
 pub struct ChiSquared {
     repr: ChiSquaredRepr,
 }
 
+#[derive(Copy)]
 enum ChiSquaredRepr {
     // 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-
@@ -235,6 +241,7 @@ impl Distribution<f64> for ChiSquared {
 /// let v = f.sample(&mut rand::thread_rng());
 /// println!("{} is from an F(2, 32) distribution", v)
 /// ```
+#[derive(Copy)]
 pub struct FisherF {
     numer: ChiSquared,
     denom: ChiSquared,
@@ -275,6 +282,7 @@ impl Distribution<f64> for FisherF {
 /// let v = t.sample(&mut rand::thread_rng());
 /// println!("{} is from a t(11) distribution", v)
 /// ```
+#[derive(Copy)]
 pub struct StudentT {
     chi: ChiSquared,
     dof: f64

src/distributions/mod.rs

@@ -50,6 +50,7 @@ impl <'a, D: Distribution<T>, T> Distribution<T> for &'a D {
 
 /// A wrapper for generating types that implement `Distribution` via the
 /// `Rand` trait.
+#[derive(Copy)]
 pub struct RandDistribution<Sup> {
     _marker: marker::PhantomData<fn() -> Sup>,
 }
@@ -74,6 +75,8 @@ pub struct Weighted<T> {
     pub item: T,
 }
 
+impl <T: Copy> Copy for Weighted<T> {}
+
 /// A distribution that selects from a finite collection of weighted items.
 ///
 /// Each item has an associated weight that influences how likely it

src/distributions/range.rs

@@ -53,6 +53,8 @@ pub struct Range<X> {
     accept_zone: X
 }
 
+impl <X: Copy> Copy for Range<X> {}
+
 impl<X: RangeDistribution + PartialOrd> Range<X> {
     /// Create a new `Range` instance that samples uniformly from
     /// `[low, high)`. Panics if `low >= high`.