fix: clippy warnings

src/distribution/beta.rs

@@ -291,7 +291,7 @@ impl Continuous<f64, f64> for Beta {
  ///
  /// where `α` is shapeA, `β` is shapeB, and `Γ` is the gamma function
  fn pdf(&self, x: f64) -> f64 {
- if x < 0.0 || x > 1.0 {
+ if !(0.0..=1.0).contains(&x) {
  0.0
  } else if self.shape_a.is_infinite() {
  if ulps_eq!(x, 1.0) {
@@ -329,7 +329,7 @@ impl Continuous<f64, f64> for Beta {
  ///
  /// where `α` is shapeA, `β` is shapeB, and `Γ` is the gamma function
  fn ln_pdf(&self, x: f64) -> f64 {
- if x < 0.0 || x > 1.0 {
+ if !(0.0..=1.0).contains(&x) {
  -INF
  } else if self.shape_a.is_infinite() {
  if ulps_eq!(x, 1.0) {

src/distribution/categorical.rs

@@ -282,7 +282,7 @@ pub fn prob_mass_to_cdf(prob_mass: &[f64]) -> Vec<f64> {
 fn binary_index(search: &[f64], val: f64) -> usize {
  use std::cmp;
 
- let mut low = 0 as isize;
+ let mut low = 0_isize;
  let mut high = search.len() as isize - 1;
  while low <= high {
  let mid = low + ((high - low) / 2);

src/distribution/chi.rs

@@ -145,7 +145,7 @@ impl Distribution<f64> for Chi {
  /// where `k` is degrees of freedom and `Γ` is the gamma function
  fn mean(&self) -> Option<f64> {
  if self.freedom.is_infinite() {
- return None;
+ None
  } else if self.freedom > 300.0 {
  // Large n approximation based on the Stirling series approximation to the Gamma function
  // This avoids call the Gamma function with large arguments and returning NaN

src/distribution/negative_binomial.rs

@@ -51,7 +51,7 @@ impl NegativeBinomial {
  if p.is_nan() || p < 0.0 || p > 1.0 || r.is_nan() || r < 0.0 {
  Err(StatsError::BadParams)
  } else {
- Ok(NegativeBinomial { p, r })
+ Ok(NegativeBinomial { r, p })
  }
  }
 

src/distribution/normal.rs

@@ -90,7 +90,7 @@ impl ContinuousCDF<f64, f64> for Normal {
  /// where `μ` is the mean, `σ` is the standard deviation and `erfc_inv` is
  /// the inverse of the complementary error function
  fn inverse_cdf(&self, x: f64) -> f64 {
- if x < 0.0 || x > 1.0 {
+ if !(0.0..=1.0).contains(&x) {
  panic!("x must be in [0, 1]");
  } else {
  self.mean - (self.std_dev * f64::consts::SQRT_2 * erf::erfc_inv(2.0 * x))

src/distribution/students_t.rs

@@ -155,7 +155,7 @@ impl ContinuousCDF<f64, f64> for StudentsT {
  /// Student's T-distribution at `x`
  fn inverse_cdf(&self, x: f64) -> f64 {
  // first calculate inverse_cdf for normal Student's T
- assert!(x >= 0.0 && x <= 1.0);
+ assert!((0.0..=1.0).contains(&x));
  let x = 2. * x.min(1. - x);
  let a = 0.5 * self.freedom;
  let b = 0.5;

src/function/beta.rs

@@ -115,7 +115,7 @@ pub fn checked_beta_reg(a: f64, b: f64, x: f64) -> Result<f64> {
  Err(StatsError::ArgMustBePositive("a"))
  } else if b <= 0.0 {
  Err(StatsError::ArgMustBePositive("b"))
- } else if x < 0.0 || x > 1.0 {
+ } else if !(0.0..=1.0).contains(&x) {
  Err(StatsError::ArgIntervalIncl("x", 0.0, 1.0))
  } else {
  let bt = if is_zero(x) || ulps_eq!(x, 1.0) {
@@ -275,7 +275,7 @@ pub fn inv_beta_reg(mut a: f64, mut b: f64, mut x: f64) -> f64 {
  const SAE: i32 = -30;
  const FPU: f64 = 1e-30; // 10^SAE
 
- debug_assert!(x >= 0.0 && x <= 1.0 && a > 0.0 && b > 0.0);
+ debug_assert!((0.0..=1.0).contains(&x) && a > 0.0 && b > 0.0);
 
  if x == 0.0 {
  return 0.0;

src/function/logistic.rs

@@ -24,7 +24,7 @@ pub fn logit(p: f64) -> f64 {
 ///
 /// If `p < 0.0` or `p > 1.0`
 pub fn checked_logit(p: f64) -> Result<f64> {
- if p < 0.0 || p > 1.0 {
+ if !(0.0..=1.0).contains(&p) {
  Err(StatsError::ArgIntervalIncl("p", 0.0, 1.0))
  } else {
  Ok((p / (1.0 - p)).ln())

src/statistics/slice_statistics.rs

@@ -129,7 +129,7 @@ impl<D: AsMut<[f64]> + AsRef<[f64]>> OrderStatistics<f64> for Data<D> {
  }
 
  fn quantile(&mut self, tau: f64) -> f64 {
- if tau < 0.0 || tau > 1.0 || self.is_empty() {
+ if !(0.0..=1.0).contains(&tau) || self.is_empty() {
  return f64::NAN;
  }