Add clippy fixes

src/distribution/beta.rs

@@ -193,6 +193,29 @@ impl ContinuousCDF<f64, f64> for Beta {
             beta::inv_beta_reg(self.shape_a, self.shape_b, x)
         }
     }
+
+    /// Calculates the inverse cumulative distribution function for the beta
+    /// distribution at `x`.
+    ///
+    /// # Returns an error instead of a panic
+    ///
+    /// If x is not in `[0, 1]`.
+    ///
+    /// # Formula
+    ///
+    /// ```text
+    /// I^{-1}_x(α, β)
+    /// ```
+    ///
+    /// where `α` is shapeA, `β` is shapeB, and `I_x` is the inverse of the
+    /// regularized lower incomplete beta function.
+    fn try_inverse_cdf(&self, x: f64) -> Result<f64, Box<dyn std::error::Error>> {
+        if !(0.0..=1.0).contains(&x) {
+            Err("x must be in [0, 1]".into())
+        } else {
+            Ok(beta::inv_beta_reg(self.shape_a, self.shape_b, x))
+        }
+    }
 }
 
 impl Min<f64> for Beta {

src/distribution/inverse_gamma.rs

@@ -344,7 +344,7 @@ impl Continuous<f64, f64> for InverseGamma {
 mod tests {
     use super::*;
     use crate::distribution::internal::density_util;
-    use crate::distribution::internal::testing_boiler;
+
 
     testing_boiler!(shape: f64, rate: f64; InverseGamma; InverseGammaError);
 

src/distribution/laplace.rs

@@ -323,8 +323,6 @@ mod tests {
     use super::*;
     use crate::prec;
 
-    use crate::distribution::internal::testing_boiler;
-
     testing_boiler!(location: f64, scale: f64; Laplace; LaplaceError);
 
     // A wrapper for the `assert_relative_eq!` macro from the approx crate.

src/distribution/log_normal.rs

@@ -364,7 +364,6 @@ impl Continuous<f64, f64> for LogNormal {
 mod tests {
     use super::*;
     use crate::distribution::internal::density_util;
-    use crate::distribution::internal::testing_boiler;
 
     testing_boiler!(location: f64, scale: f64; LogNormal; LogNormalError);
 

src/distribution/mod.rs

@@ -166,6 +166,18 @@ pub trait ContinuousCDF<K: Float, T: Float>: Min<K> + Max<K> {
         }
         (high + low) / two
     }
+
+    /// Due to issues with rounding and floating-point accuracy the default
+    /// implementation may be ill-behaved.
+    /// Specialized inverse cdfs should be used whenever possible.
+    /// Performs a binary search on the domain of `cdf` to obtain an approximation
+    /// of `F^-1(p) := inf { x | F(x) >= p }`. Needless to say, performance may
+    /// may be lacking.
+    #[doc(alias = "quantile function")]
+    #[doc(alias = "quantile")]
+    fn try_inverse_cdf(&self, p: T) -> Result<K, Box<dyn std::error::Error>> {
+        Ok(self.inverse_cdf(p))
+    }
 }
 
 /// The `DiscreteCDF` trait is used to specify an interface for univariate

src/distribution/multivariate_normal.rs

@@ -177,11 +177,12 @@ where
 ///
 /// # Panics
 /// If both the `cov` and `cholesky` arguments are `None`; at least one must be `Some(_)`.
+type MeanCovarainceCholeskyReturn<D> = (OVector<f64, D>, OMatrix<f64, D, D>, Cholesky<f64, D>);
 fn normalize_constructor_arguments<D>(
     mean: OVector<f64, D>,
     covariance: Option<OMatrix<f64, D, D>>,
     cholesky: Option<Cholesky<f64, D>>,
-) -> Result<(OVector<f64, D>, OMatrix<f64, D, D>, Cholesky<f64, D>), MultivariateNormalError>
+) -> Result<MeanCovarainceCholeskyReturn<D>, MultivariateNormalError>
 where
     D: DimMin<D, Output = D>,
     nalgebra::DefaultAllocator: nalgebra::allocator::Allocator<D>

src/distribution/negative_binomial.rs

@@ -323,7 +323,6 @@ impl Discrete<u64, f64> for NegativeBinomial {
 mod tests {
     use super::*;
     use crate::distribution::internal::density_util;
-    use crate::distribution::internal::testing_boiler;
 
     testing_boiler!(r: f64, p: f64; NegativeBinomial; NegativeBinomialError);
 

src/distribution/poisson.rs

@@ -342,7 +342,6 @@ pub fn sample_unchecked<R: ::rand::Rng + ?Sized>(rng: &mut R, lambda: f64) -> f6
 mod tests {
     use super::*;
     use crate::distribution::internal::density_util;
-    use crate::distribution::internal::testing_boiler;
     testing_boiler!(lambda: f64; Poisson; PoissonError);
 
     #[test]

src/distribution/triangular.rs

@@ -438,7 +438,6 @@ fn sample_unchecked<R: ::rand::Rng + ?Sized>(rng: &mut R, min: f64, max: f64, mo
 mod tests {
     use super::*;
     use crate::distribution::internal::density_util;
-    use crate::distribution::internal::testing_boiler;
 
     testing_boiler!(min: f64, max: f64, mode: f64; Triangular; TriangularError);
 

src/distribution/uniform.rs

@@ -348,7 +348,6 @@ mod tests {
     use super::*;
     use crate::prec;
     use crate::distribution::internal::density_util;
-    use crate::distribution::internal::testing_boiler;
 
     testing_boiler!(min: f64, max: f64; Uniform; UniformError);
 

src/distribution/weibull.rs

@@ -381,7 +381,6 @@ impl Continuous<f64, f64> for Weibull {
 mod tests {
     use super::*;
     use crate::distribution::internal::density_util;
-    use crate::distribution::internal::testing_boiler;
 
     testing_boiler!(shape: f64, scale: f64; Weibull; WeibullError);
 

src/function/evaluate.rs

@@ -11,10 +11,7 @@
 ///
 /// Returns 0 for a 0 length coefficient slice
 pub fn polynomial(z: f64, coeff: &[f64]) -> f64 {
-    coeff
-        .into_iter()
-        .rev()
-        .fold(0_f64, |acc, val| acc * z + val)
+    coeff.iter().rev().fold(0_f64, |acc, val| acc * z + val)
 }
 
 #[rustfmt::skip]