Merge branch 'master' into gumbel_issue189

.github/workflows/coverage.yml

@@ -7,15 +7,21 @@ on:
     branches: [ master ]
 jobs:
   coverage:
+    timeout-minutes: 20
+    strategy:
+      matrix:
+        toolchain: [nightly, nightly-2024-09-01]
+
     name: Coverage
     runs-on: ubuntu-latest
     env:
       RUSTFLAGS: -D warnings
       CARGO_TERM_COLOR: always
     steps:
       - uses: actions/checkout@v4
-      - uses: dtolnay/rust-toolchain@nightly
+      - uses: dtolnay/rust-toolchain@master
         with:
+          toolchain: ${{ matrix.toolchain }}
           components: llvm-tools-preview
 
       - uses: taiki-e/install-action@v2

.github/workflows/test.yml

@@ -20,12 +20,9 @@ jobs:
         with:
           components: clippy
 
-      - name: Run cargo clippy
+      - name: Run cargo clippy (default features)
         run: cargo clippy --all-targets
 
-      - name: Run cargo clippy without default features
-        run: cargo clippy --no-default-features --all-targets
-
   fmt:
     runs-on: ubuntu-latest
     steps:
@@ -65,3 +62,15 @@ jobs:
       - name: Test default features
         run: cargo test
 
+  features:
+    needs: [clippy, fmt]
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Install Rust stable
+        uses: dtolnay/rust-toolchain@stable
+      - name: Install cargo-hack
+        uses: taiki-e/install-action@cargo-hack
+      - uses: Swatinem/rust-cache@v2
+      - name: Check all possible feature sets
+        run: cargo hack check --feature-powerset --no-dev-deps

Cargo.toml

@@ -13,7 +13,7 @@ edition = "2021"
 include = ["CHANGELOG.md", "LICENSE.md", "src/", "tests/"]
 
 # When changing MSRV: Also update the README
-rust-version = "1.66.0"
+rust-version = "1.61.0"
 
 [lib]
 name = "statrs"
@@ -22,21 +22,26 @@ path = "src/lib.rs"
 [[bench]]
 name = "order_statistics"
 harness = false
+required-features = ["rand"]
 
 [features]
-default = ["nalgebra"]
+default = ["nalgebra", "rand"]
 nalgebra = ["dep:nalgebra"]
+rand = ["dep:rand", "nalgebra?/rand"]
 
 [dependencies]
-rand = "0.8"
 approx = "0.5.0"
 num-traits = "0.2.14"
 
+[dependencies.rand]
+version = "0.8"
+optional = true
+
 [dependencies.nalgebra]
 version = "0.32"
 optional = true
 default-features = false
-features = ["rand", "std"]
+features = ["std"]
 
 [dev-dependencies]
 criterion = "0.5"

README.md

@@ -50,7 +50,7 @@ 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 1.66.0 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 1.61.0 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).
 
 ## Contributing
 

benches/order_statistics.rs

@@ -1,4 +1,3 @@
-extern crate rand;
 extern crate statrs;
 use criterion::{black_box, criterion_group, criterion_main, BatchSize, Criterion};
 use rand::prelude::*;

src/distribution/bernoulli.rs

@@ -1,6 +1,5 @@
 use crate::distribution::{Binomial, BinomialError, Discrete, DiscreteCDF};
 use crate::statistics::*;
-use rand::Rng;
 
 /// Implements the
 /// [Bernoulli](https://en.wikipedia.org/wiki/Bernoulli_distribution)
@@ -85,8 +84,9 @@ impl std::fmt::Display for Bernoulli {
     }
 }
 
+#[cfg(feature = "rand")]
 impl ::rand::distributions::Distribution<f64> for Bernoulli {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+    fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         rng.gen_bool(self.p()) as u8 as f64
     }
 }

src/distribution/beta.rs

@@ -1,7 +1,6 @@
 use crate::distribution::{Continuous, ContinuousCDF};
 use crate::function::{beta, gamma};
 use crate::statistics::*;
-use rand::Rng;
 
 /// Implements the [Beta](https://en.wikipedia.org/wiki/Beta_distribution)
 /// distribution
@@ -121,8 +120,9 @@ impl std::fmt::Display for Beta {
     }
 }
 
+#[cfg(feature = "rand")]
 impl ::rand::distributions::Distribution<f64> for Beta {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+    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);
         let y = super::gamma::sample_unchecked(rng, self.shape_b, 1.0);

src/distribution/binomial.rs

@@ -1,7 +1,6 @@
 use crate::distribution::{Discrete, DiscreteCDF};
 use crate::function::{beta, factorial};
 use crate::statistics::*;
-use rand::Rng;
 use std::f64;
 
 /// Implements the
@@ -110,8 +109,9 @@ impl std::fmt::Display for Binomial {
     }
 }
 
+#[cfg(feature = "rand")]
 impl ::rand::distributions::Distribution<f64> for Binomial {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+    fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         (0..self.n).fold(0.0, |acc, _| {
             let n: f64 = rng.gen();
             if n < self.p {

src/distribution/categorical.rs

@@ -1,6 +1,5 @@
 use crate::distribution::{Discrete, DiscreteCDF};
 use crate::statistics::*;
-use rand::Rng;
 use std::f64;
 
 /// Implements the
@@ -124,8 +123,9 @@ impl std::fmt::Display for Categorical {
     }
 }
 
+#[cfg(feature = "rand")]
 impl ::rand::distributions::Distribution<f64> for Categorical {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+    fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         sample_unchecked(rng, &self.cdf)
     }
 }
@@ -322,7 +322,8 @@ impl Discrete<u64, f64> for Categorical {
 
 /// Draws a sample from the categorical distribution described by `cdf`
 /// without doing any bounds checking
-pub fn sample_unchecked<R: Rng + ?Sized>(rng: &mut R, cdf: &[f64]) -> f64 {
+#[cfg(feature = "rand")]
+pub fn sample_unchecked<R: ::rand::Rng + ?Sized>(rng: &mut R, cdf: &[f64]) -> f64 {
     let draw = rng.gen::<f64>() * cdf.last().unwrap();
     cdf.iter()
         .enumerate()

src/distribution/cauchy.rs

@@ -1,6 +1,5 @@
 use crate::distribution::{Continuous, ContinuousCDF};
 use crate::statistics::*;
-use rand::Rng;
 use std::f64;
 
 /// Implements the [Cauchy](https://en.wikipedia.org/wiki/Cauchy_distribution)
@@ -111,8 +110,9 @@ impl std::fmt::Display for Cauchy {
     }
 }
 
+#[cfg(feature = "rand")]
 impl ::rand::distributions::Distribution<f64> for Cauchy {
-    fn sample<R: Rng + ?Sized>(&self, r: &mut R) -> f64 {
+    fn sample<R: ::rand::Rng + ?Sized>(&self, r: &mut R) -> f64 {
         self.location + self.scale * (f64::consts::PI * (r.gen::<f64>() - 0.5)).tan()
     }
 }

src/distribution/chi.rs

@@ -1,7 +1,6 @@
 use crate::distribution::{Continuous, ContinuousCDF};
 use crate::function::gamma;
 use crate::statistics::*;
-use rand::Rng;
 use std::f64;
 
 /// Implements the [Chi](https://en.wikipedia.org/wiki/Chi_distribution)
@@ -94,8 +93,9 @@ impl std::fmt::Display for Chi {
     }
 }
 
+#[cfg(feature = "rand")]
 impl ::rand::distributions::Distribution<f64> for Chi {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+    fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         (0..self.freedom as i64)
             .fold(0.0, |acc, _| {
                 acc + super::normal::sample_unchecked(rng, 0.0, 1.0).powf(2.0)
@@ -381,7 +381,7 @@ mod tests {
     fn test_large_dof_mean_not_nan() {
         for i in 1..1000 {
             let mean = Chi::new(i as f64).unwrap().mean().unwrap();
-            assert!(!mean.is_nan(), "Chi mean for {} dof was {}", i, mean);
+            assert!(!mean.is_nan(), "Chi mean for {i} dof was {mean}");
         }
     }
 

src/distribution/chi_squared.rs

@@ -1,6 +1,5 @@
 use crate::distribution::{Continuous, ContinuousCDF, Gamma, GammaError};
 use crate::statistics::*;
-use rand::Rng;
 use std::f64;
 
 /// Implements the
@@ -101,8 +100,9 @@ impl std::fmt::Display for ChiSquared {
     }
 }
 
+#[cfg(feature = "rand")]
 impl ::rand::distributions::Distribution<f64> for ChiSquared {
-    fn sample<R: Rng + ?Sized>(&self, r: &mut R) -> f64 {
+    fn sample<R: ::rand::Rng + ?Sized>(&self, r: &mut R) -> f64 {
         ::rand::distributions::Distribution::sample(&self.g, r)
     }
 }

src/distribution/dirac.rs

@@ -1,6 +1,5 @@
 use crate::distribution::ContinuousCDF;
 use crate::statistics::*;
-use rand::Rng;
 
 /// Implements the [Dirac Delta](https://en.wikipedia.org/wiki/Dirac_delta_function#As_a_distribution)
 /// distribution
@@ -69,8 +68,9 @@ impl std::fmt::Display for Dirac {
     }
 }
 
+#[cfg(feature = "rand")]
 impl ::rand::distributions::Distribution<f64> for Dirac {
-    fn sample<R: Rng + ?Sized>(&self, _: &mut R) -> f64 {
+    fn sample<R: ::rand::Rng + ?Sized>(&self, _: &mut R) -> f64 {
         self.0
     }
 }

src/distribution/dirichlet.rs

@@ -2,8 +2,7 @@ use crate::distribution::Continuous;
 use crate::function::gamma;
 use crate::prec;
 use crate::statistics::*;
-use nalgebra::{Const, Dim, Dyn, OMatrix, OVector};
-use rand::Rng;
+use nalgebra::{Dim, Dyn, OMatrix, OVector};
 use std::f64;
 
 /// Implements the
@@ -192,16 +191,17 @@ where
     }
 }
 
+#[cfg(feature = "rand")]
 impl<D> ::rand::distributions::Distribution<OVector<f64, D>> for Dirichlet<D>
 where
     D: Dim,
     nalgebra::DefaultAllocator: nalgebra::allocator::Allocator<f64, D>,
 {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> OVector<f64, D> {
+    fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> OVector<f64, D> {
         let mut sum = 0.0;
         OVector::from_iterator_generic(
             self.alpha.shape_generic().0,
-            Const::<1>,
+            nalgebra::Const::<1>,
             self.alpha.iter().map(|&a| {
                 let sample = super::gamma::sample_unchecked(rng, a, 1.0);
                 sum += sample;

src/distribution/discrete_uniform.rs

@@ -1,6 +1,5 @@
 use crate::distribution::{Discrete, DiscreteCDF};
 use crate::statistics::*;
-use rand::Rng;
 
 /// Implements the [Discrete
 /// Uniform](https://en.wikipedia.org/wiki/Discrete_uniform_distribution)
@@ -75,8 +74,9 @@ impl std::fmt::Display for DiscreteUniform {
     }
 }
 
+#[cfg(feature = "rand")]
 impl ::rand::distributions::Distribution<f64> for DiscreteUniform {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+    fn sample<R: ::rand::Rng + ?Sized>(&self, rng: &mut R) -> f64 {
         rng.gen_range(self.min..=self.max) as f64
     }
 }