Struct rv::dist::NegBinomial

pub struct NegBinomial { /* fields omitted */ }

Negative Binomial distribution NBin(r, p).

Notes

This crate uses the parameterization found on Wolfram Mathworld, which is also the parameterization used in scipy.

Parameters

• r: The number of successes before the trials are stopped
• p: The success probability

Implementations

impl NegBinomial

pub fn new(r: f64, p: f64) -> Result<Self, NegBinomialError>

Create a new Negative Binomial distribution

pub fn new_unchecked(r: f64, p: f64) -> Self

Create a new Negative Binomial distribution without input validation.

pub fn r(&self) -> f64

Get the value of the r parameter

pub fn set_r(&mut self, r: f64) -> Result<(), NegBinomialError>

Change the value of the r parameter

Example

use rv::dist::NegBinomial;

let mut nbin = NegBinomial::new(4.0, 0.8).unwrap();

assert!((nbin.r() - 4.0).abs() < 1E-10);

nbin.set_r(2.5).unwrap();

assert!((nbin.r() - 2.5).abs() < 1E-10);

Will error for invalid values

assert!(nbin.set_r(2.0).is_ok());
assert!(nbin.set_r(1.0).is_ok());

// r must be >= 1.0
assert!(nbin.set_r(0.99).is_err());

assert!(nbin.set_r(std::f64::INFINITY).is_err());
assert!(nbin.set_r(std::f64::NEG_INFINITY).is_err());
assert!(nbin.set_r(std::f64::NAN).is_err());

pub fn set_r_unchecked(&mut self, r: f64)

Set the value of r without input validation

pub fn p(&self) -> f64

Get the value of the p parameter

pub fn set_p(&mut self, p: f64) -> Result<(), NegBinomialError>

Change the value of the p parameter

Example

use rv::dist::NegBinomial;

let mut nbin = NegBinomial::new(4.0, 0.8).unwrap();

assert!((nbin.p() - 0.8).abs() < 1E-10);

nbin.set_p(0.51).unwrap();

assert!((nbin.p() - 0.51).abs() < 1E-10);

Will error for invalid values

// OK values in [0, 1]
assert!(nbin.set_p(0.51).is_ok());
assert!(nbin.set_p(0.0).is_ok());
assert!(nbin.set_p(1.0).is_ok());

// Too low, not in [0, 1]
assert!(nbin.set_p(-0.1).is_err());

// Too high, not in [0, 1]
assert!(nbin.set_p(-1.1).is_err());

assert!(nbin.set_p(std::f64::INFINITY).is_err());
assert!(nbin.set_p(std::f64::NEG_INFINITY).is_err());
assert!(nbin.set_p(std::f64::NAN).is_err());

pub fn set_p_unchecked(&mut self, p: f64)

Set the value of p without input validation

Trait Implementations

impl Cdf<u16> for NegBinomial

fn cdf(&self, x: &u16) -> f64

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Cdf<u32> for NegBinomial

fn cdf(&self, x: &u32) -> f64

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Cdf<u8> for NegBinomial

fn cdf(&self, x: &u8) -> f64

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Clone for NegBinomial

fn clone(&self) -> Self

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for NegBinomial

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

Formats the value using the given formatter.

impl DiscreteDistr<u16> for NegBinomial

fn pmf(&self, x: &X) -> f64

Probability mass function (PMF) at x

fn ln_pmf(&self, x: &X) -> f64

Natural logarithm of the probability mass function (PMF)

impl DiscreteDistr<u32> for NegBinomial

fn pmf(&self, x: &X) -> f64

Probability mass function (PMF) at x

fn ln_pmf(&self, x: &X) -> f64

Natural logarithm of the probability mass function (PMF)

impl DiscreteDistr<u8> for NegBinomial

fn pmf(&self, x: &X) -> f64

Probability mass function (PMF) at x

fn ln_pmf(&self, x: &X) -> f64

Natural logarithm of the probability mass function (PMF)

impl Kurtosis for NegBinomial

fn kurtosis(&self) -> Option<f64>

impl Mean<f64> for NegBinomial

fn mean(&self) -> Option<f64>

Returns None if the mean is undefined

impl PartialEq<NegBinomial> for NegBinomial

fn eq(&self, other: &NegBinomial) -> bool

This method tests for self and other values to be equal, and is used by ==.

#[must_use]fn ne(&self, other: &Rhs) -> bool

This method tests for !=.

impl Rv<u16> for NegBinomial

fn ln_f(&self, x: &u16) -> f64

Probability function

fn draw<R: Rng>(&self, rng: &mut R) -> u16

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u16>

Multiple draws of the Rv

fn f(&self, x: &X) -> f64

Probability function

fn sample_stream<'r, R: Rng>(
    &'r self,
    rng: &'r mut R
) -> Box<dyn Iterator<Item = X> + 'r>

Create a never-ending iterator of samples

impl Rv<u32> for NegBinomial

fn ln_f(&self, x: &u32) -> f64

Probability function

fn draw<R: Rng>(&self, rng: &mut R) -> u32

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u32>

Multiple draws of the Rv

fn f(&self, x: &X) -> f64

Probability function

fn sample_stream<'r, R: Rng>(
    &'r self,
    rng: &'r mut R
) -> Box<dyn Iterator<Item = X> + 'r>

Create a never-ending iterator of samples

impl Rv<u8> for NegBinomial

fn ln_f(&self, x: &u8) -> f64

Probability function

fn draw<R: Rng>(&self, rng: &mut R) -> u8

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<u8>

Multiple draws of the Rv

fn f(&self, x: &X) -> f64

Probability function

fn sample_stream<'r, R: Rng>(
    &'r self,
    rng: &'r mut R
) -> Box<dyn Iterator<Item = X> + 'r>

Create a never-ending iterator of samples

impl Skewness for NegBinomial

fn skewness(&self) -> Option<f64>

impl Support<u16> for NegBinomial

fn supports(&self, _x: &u16) -> bool

Returns true if x is in the support of the Rv

impl Support<u32> for NegBinomial

fn supports(&self, _x: &u32) -> bool

Returns true if x is in the support of the Rv

impl Support<u8> for NegBinomial

fn supports(&self, _x: &u8) -> bool

Returns true if x is in the support of the Rv

impl Variance<f64> for NegBinomial

fn variance(&self) -> Option<f64>

Returns None if the variance is undefined