chore: Bumped rand to 0.9.0 and mechanically updated usage

Cargo.toml

@@ -13,7 +13,7 @@ edition = "2021"
 include = ["CHANGELOG.md", "LICENSE.md", "src/", "tests/"]
 
 # When changing MSRV: Also update the README
-rust-version = "1.65.0"
+rust-version = "1.87.0"
 
 [lib]
 name = "statrs"
@@ -36,12 +36,12 @@ approx = "0.5.0"
 num-traits = "0.2.14"
 
 [dependencies.rand]
-version = "0.8"
+version = "0.9.0"
 optional = true
 default-features = false
 
 [dependencies.nalgebra]
-version = "0.33"
+version = "0.34"
 optional = true
 default-features = false
 
@@ -50,7 +50,7 @@ criterion = "0.5"
 anyhow = "1.0"
 
 [dev-dependencies.nalgebra]
-version = "0.33"
+version = "0.34"
 default-features = false
 features = ["macros"]
 

README.md

@@ -53,7 +53,7 @@ If you'd like to modify where the data is downloaded, you can use the environmen
 
 ## Minimum supported Rust version (MSRV)
 
-This crate requires a Rust version of 1.65.0 or higher. Increases in MSRV will be considered a semver non-breaking API change and require a version increase (PATCH until 1.0.0, MINOR after 1.0.0).
+This crate requires a Rust version of 1.87.0 or higher. Increases in MSRV will be considered a semver non-breaking API change and require a version increase (PATCH until 1.0.0, MINOR after 1.0.0).
 
 ## Precision
 Floating-point numbers cannot always represent decimal values exactly, which can introduce small (and in some cases catastrophically large) errors in computations.

benches/order_statistics.rs

@@ -4,15 +4,15 @@ use rand::prelude::*;
 use statrs::statistics::*;
 
 fn bench_order_statistic(c: &mut Criterion) {
-    let mut rng = thread_rng();
+    let mut rng = rand::rng();
     let to_random_owned = |data: &[f64]| -> Data<Vec<f64>> {
-        let mut rng = thread_rng();
+        let mut rng = rand::rng();
         let mut owned = data.to_vec();
         owned.shuffle(&mut rng);
         Data::new(owned)
     };
-    let k = black_box(rng.gen());
-    let tau = black_box(rng.gen_range(0.0..1.0));
+    let k = black_box(rng.random_range(..=usize::MAX));
+    let tau = black_box(rng.random_range(0.0..1.0));
     let mut group = c.benchmark_group("order statistic");
     let data: Vec<_> = (0..100).map(|x| x as f64).collect();
     group.bench_function("order_statistic", |b| {

src/distribution/bernoulli.rs

@@ -86,15 +86,15 @@ impl core::fmt::Display for Bernoulli {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<bool> for Bernoulli {
+impl ::rand::distr::Distribution<bool> for Bernoulli {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> bool {
-        rng.gen_bool(self.p())
+        rng.random_bool(self.p())
     }
 }
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for Bernoulli {
+impl ::rand::distr::Distribution<f64> for Bernoulli {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         rng.sample::<bool, _>(self) as u8 as f64
     }

src/distribution/beta.rs

@@ -116,7 +116,7 @@ impl core::fmt::Display for Beta {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for Beta {
+impl ::rand::distr::Distribution<f64> for Beta {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         // Generated by sampling two gamma distributions and normalizing.
         let x = super::gamma::sample_unchecked(rng, self.shape_a, 1.0);

src/distribution/binomial.rs

@@ -113,10 +113,10 @@ impl core::fmt::Display for Binomial {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<u64> for Binomial {
+impl ::rand::distr::Distribution<u64> for Binomial {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> u64 {
         (0..self.n).fold(0, |acc, _| {
-            let n: f64 = rng.gen();
+            let n: f64 = rng.random();
             if n < self.p {
                 acc + 1
             } else {
@@ -128,7 +128,7 @@ impl ::rand::distributions::Distribution<u64> for Binomial {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for Binomial {
+impl ::rand::distr::Distribution<f64> for Binomial {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         rng.sample::<u64, _>(self) as f64
     }

src/distribution/categorical.rs

@@ -127,23 +127,23 @@ impl core::fmt::Display for Categorical {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<usize> for Categorical {
+impl ::rand::distr::Distribution<usize> for Categorical {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> usize {
         sample_unchecked(rng, &self.cdf)
     }
 }
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<u64> for Categorical {
+impl ::rand::distr::Distribution<u64> for Categorical {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> u64 {
         sample_unchecked(rng, &self.cdf) as u64
     }
 }
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for Categorical {
+impl ::rand::distr::Distribution<f64> for Categorical {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         sample_unchecked(rng, &self.cdf) as f64
     }
@@ -344,7 +344,7 @@ impl Discrete<u64, f64> for Categorical {
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
 pub fn sample_unchecked<R: ::rand::Rng + ?Sized>(rng: &mut R, cdf: &[f64]) -> usize {
-    let draw = rng.gen::<f64>() * cdf.last().unwrap();
+    let draw = rng.random::<f64>() * cdf.last().unwrap();
     cdf.iter().position(|val| *val >= draw).unwrap()
 }
 

src/distribution/cauchy.rs

@@ -113,9 +113,9 @@ impl core::fmt::Display for Cauchy {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for Cauchy {
+impl ::rand::distr::Distribution<f64> for Cauchy {
     fn sample<R: ::rand::Rng + ?Sized>(&self, r: &mut R) -> f64 {
-        self.location + self.scale * (f64::consts::PI * (r.gen::<f64>() - 0.5)).tan()
+        self.location + self.scale * (f64::consts::PI * (r.random::<f64>() - 0.5)).tan()
     }
 }
 

src/distribution/chi.rs

@@ -95,7 +95,7 @@ impl core::fmt::Display for Chi {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for Chi {
+impl ::rand::distr::Distribution<f64> for Chi {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         (0..self.freedom())
             .fold(0.0, |acc, _| {

src/distribution/chi_squared.rs

@@ -102,9 +102,9 @@ impl core::fmt::Display for ChiSquared {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for ChiSquared {
+impl ::rand::distr::Distribution<f64> for ChiSquared {
     fn sample<R: ::rand::Rng + ?Sized>(&self, r: &mut R) -> f64 {
-        ::rand::distributions::Distribution::sample(&self.g, r)
+        ::rand::distr::Distribution::sample(&self.g, r)
     }
 }
 

src/distribution/dirac.rs

@@ -85,7 +85,7 @@ impl core::fmt::Display for Dirac {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for Dirac {
+impl ::rand::distr::Distribution<f64> for Dirac {
     fn sample<R: ::rand::Rng + ?Sized>(&self, _: &mut R) -> f64 {
         self.0
     }

src/distribution/dirichlet.rs

@@ -194,7 +194,7 @@ where
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl<D> ::rand::distributions::Distribution<OVector<f64, D>> for Dirichlet<D>
+impl<D> ::rand::distr::Distribution<OVector<f64, D>> for Dirichlet<D>
 where
     D: Dim,
     nalgebra::DefaultAllocator: nalgebra::allocator::Allocator<D>,

src/distribution/discrete_uniform.rs

@@ -115,15 +115,15 @@ impl core::fmt::Display for DiscreteUniform {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<i64> for DiscreteUniform {
+impl ::rand::distr::Distribution<i64> for DiscreteUniform {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> i64 {
-        rng.gen_range(self.min..=self.max)
+        rng.random_range(self.min..=self.max)
     }
 }
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for DiscreteUniform {
+impl ::rand::distr::Distribution<f64> for DiscreteUniform {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         rng.sample::<i64, _>(self) as f64
     }

src/distribution/empirical.rs

@@ -214,7 +214,7 @@ impl FromIterator<f64> for Empirical {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for Empirical {
+impl ::rand::distr::Distribution<f64> for Empirical {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         use crate::distribution::Uniform;
 

src/distribution/erlang.rs

@@ -84,9 +84,9 @@ impl core::fmt::Display for Erlang {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for Erlang {
+impl ::rand::distr::Distribution<f64> for Erlang {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        ::rand::distributions::Distribution::sample(&self.g, rng)
+        ::rand::distr::Distribution::sample(&self.g, rng)
     }
 }
 

src/distribution/exponential.rs

@@ -93,7 +93,7 @@ impl core::fmt::Display for Exp {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for Exp {
+impl ::rand::distr::Distribution<f64> for Exp {
     fn sample<R: ::rand::Rng + ?Sized>(&self, r: &mut R) -> f64 {
         use crate::distribution::ziggurat;
 

src/distribution/fisher_snedecor.rs

@@ -126,7 +126,7 @@ impl core::fmt::Display for FisherSnedecor {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for FisherSnedecor {
+impl ::rand::distr::Distribution<f64> for FisherSnedecor {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         (super::gamma::sample_unchecked(rng, self.freedom_1 / 2.0, 0.5) * self.freedom_2)
             / (super::gamma::sample_unchecked(rng, self.freedom_2 / 2.0, 0.5) * self.freedom_1)

src/distribution/gamma.rs

@@ -125,7 +125,7 @@ impl core::fmt::Display for Gamma {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for Gamma {
+impl ::rand::distr::Distribution<f64> for Gamma {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         sample_unchecked(rng, self.shape, self.rate)
     }
@@ -410,7 +410,7 @@ pub fn sample_unchecked<R: ::rand::Rng + ?Sized>(rng: &mut R, shape: f64, rate:
     let mut afix = 1.0;
     if shape < 1.0 {
         a = shape + 1.0;
-        afix = rng.gen::<f64>().powf(1.0 / shape);
+        afix = rng.random::<f64>().powf(1.0 / shape);
     }
 
     let d = a - 1.0 / 3.0;
@@ -428,7 +428,7 @@ pub fn sample_unchecked<R: ::rand::Rng + ?Sized>(rng: &mut R, shape: f64, rate:
 
         v = v * v * v;
         x = x * x;
-        let u: f64 = rng.gen();
+        let u: f64 = rng.random();
         if u < 1.0 - 0.0331 * x * x || u.ln() < 0.5 * x + d * (1.0 - v + v.ln()) {
             return afix * d * v / rate;
         }

src/distribution/geometric.rs

@@ -94,12 +94,12 @@ impl core::fmt::Display for Geometric {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<u64> for Geometric {
+impl ::rand::distr::Distribution<u64> for Geometric {
     fn sample<R: ::rand::Rng + ?Sized>(&self, r: &mut R) -> u64 {
         if prec::ulps_eq!(self.p, 1.0) {
             1
         } else {
-            let x: f64 = r.sample(::rand::distributions::OpenClosed01);
+            let x: f64 = r.sample(::rand::distr::OpenClosed01);
             // This cast is safe, because the largest finite value this expression can take is when
             // `x = 1.4e-45` and `1.0 - self.p = 0.9999999999999999`, in which case we get
             // `930262250532780300`, which when casted to a `u64` is `930262250532780288`.
@@ -110,7 +110,7 @@ impl ::rand::distributions::Distribution<u64> for Geometric {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for Geometric {
+impl ::rand::distr::Distribution<f64> for Geometric {
     fn sample<R: ::rand::Rng + ?Sized>(&self, r: &mut R) -> f64 {
         r.sample::<u64, _>(self) as f64
     }

src/distribution/gumbel.rs

@@ -115,9 +115,9 @@ impl core::fmt::Display for Gumbel {
 }
 
 #[cfg(feature = "rand")]
-impl ::rand::distributions::Distribution<f64> for Gumbel {
+impl ::rand::distr::Distribution<f64> for Gumbel {
     fn sample<R: rand::Rng + ?Sized>(&self, r: &mut R) -> f64 {
-        self.location - self.scale * ((-(r.gen::<f64>())).ln()).ln()
+        self.location - self.scale * ((-(r.random::<f64>())).ln()).ln()
     }
 }
 
@@ -385,7 +385,7 @@ mod tests {
         test_exact(0.0, 2.0, 2.270362845461478, entropy);
         test_exact(0.1, 4.0, 2.9635100260214235, entropy);
         test_exact(1.0, 10.0, 3.8798007578955787, entropy);
-        test_exact(10.0, 11.0, 3.9751109376999034, entropy); 
+        test_exact(10.0, 11.0, 3.9751109376999034, entropy);
     }
 
     #[test]

src/distribution/hypergeometric.rs

@@ -160,15 +160,15 @@ impl core::fmt::Display for Hypergeometric {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<u64> for Hypergeometric {
+impl ::rand::distr::Distribution<u64> for Hypergeometric {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> u64 {
         let mut population = self.population as f64;
         let mut successes = self.successes as f64;
         let mut draws = self.draws;
         let mut x = 0;
         loop {
             let p = successes / population;
-            let next: f64 = rng.gen();
+            let next: f64 = rng.random();
             if next < p {
                 x += 1;
                 successes -= 1.0;
@@ -185,7 +185,7 @@ impl ::rand::distributions::Distribution<u64> for Hypergeometric {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for Hypergeometric {
+impl ::rand::distr::Distribution<f64> for Hypergeometric {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         rng.sample::<u64, _>(self) as f64
     }

src/distribution/inverse_gamma.rs

@@ -122,7 +122,7 @@ impl core::fmt::Display for InverseGamma {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for InverseGamma {
+impl ::rand::distr::Distribution<f64> for InverseGamma {
     fn sample<R: ::rand::Rng + ?Sized>(&self, r: &mut R) -> f64 {
         1.0 / super::gamma::sample_unchecked(r, self.shape, self.rate)
     }

src/distribution/laplace.rs

@@ -113,9 +113,9 @@ impl core::fmt::Display for Laplace {
 
 #[cfg(feature = "rand")]
 #[cfg_attr(docsrs, doc(cfg(feature = "rand")))]
-impl ::rand::distributions::Distribution<f64> for Laplace {
+impl ::rand::distr::Distribution<f64> for Laplace {
     fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        let x: f64 = rng.gen_range(-0.5..0.5);
+        let x: f64 = rng.random_range(-0.5..0.5);
         self.location - self.scale * x.signum() * (1. - 2. * x.abs()).ln()
     }
 }
@@ -557,17 +557,16 @@ mod tests {
     #[cfg(feature = "rand")]
     #[test]
     fn test_sample() {
-        use ::rand::distributions::Distribution;
-        use ::rand::thread_rng;
+        use ::rand::distr::Distribution;
 
         let l = create_ok(0.1, 0.5);
-        l.sample(&mut thread_rng());
+        l.sample(&mut ::rand::rng());
     }
 
     #[cfg(feature = "rand")]
     #[test]
     fn test_sample_distribution() {
-        use ::rand::distributions::Distribution;
+        use ::rand::distr::Distribution;
         use ::rand::rngs::StdRng;
         use ::rand::SeedableRng;