Struct rand_distr::SkewNormal

pub struct SkewNormal<F> where
    F: Float,
    StandardNormal: Distribution<F>,  { /* private fields */ }

The skew normal distribution SN(location, scale, shape).

The skew normal distribution is a generalization of the Normal distribution to allow for non-zero skewness.

It has the density function, for scale > 0, f(x) = 2 / scale * phi((x - location) / scale) * Phi(alpha * (x - location) / scale) where phi and Phi are the density and distribution of a standard normal variable.

Example

use rand_distr::{SkewNormal, Distribution};

// location 2, scale 3, shape 1
let skew_normal = SkewNormal::new(2.0, 3.0, 1.0).unwrap();
let v = skew_normal.sample(&mut rand::thread_rng());
println!("{} is from a SN(2, 3, 1) distribution", v)

Implementation details

We are using the algorithm from A Method to Simulate the Skew Normal Distribution.

Implementations

impl<F> SkewNormal<F> where
    F: Float,
    StandardNormal: Distribution<F>, 

pub fn new(location: F, scale: F, shape: F) -> Result<SkewNormal<F>, Error>

Construct, from location, scale and shape.

Parameters:

• location (unrestricted)
• scale (must be finite and larger than zero)
• shape (must be finite)

pub fn location(&self) -> F

Returns the location of the distribution.

pub fn scale(&self) -> F

Returns the scale of the distribution.

pub fn shape(&self) -> F

Returns the shape of the distribution.

Trait Implementations

impl<F: Clone> Clone for SkewNormal<F> where
    F: Float,
    StandardNormal: Distribution<F>, 

fn clone(&self) -> SkewNormal<F>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<F: Debug> Debug for SkewNormal<F> where
    F: Float,
    StandardNormal: Distribution<F>, 

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<F> Distribution<F> for SkewNormal<F> where
    F: Float,
    StandardNormal: Distribution<F>, 

fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> F

Generate a random value of T, using rng as the source of randomness.

fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T> where
    R: Rng, 

Create an iterator that generates random values of T, using rng as the source of randomness.

fn map<F, S>(self, func: F) -> DistMap<Self, F, T, S> where
    F: Fn(T) -> S, 

Create a distribution of values of ‘S’ by mapping the output of Self through the closure F

impl<F: Copy> Copy for SkewNormal<F> where
    F: Float,
    StandardNormal: Distribution<F>,