Module stats 

Statistical distributions: continuous and discrete.

Each distribution provides ContinuousDistribution or DiscreteDistribution trait implementations for a consistent API across all distributions.

Continuous distributions

Distribution	Parameters	Support
Normal	mean μ, std dev σ	(−∞, ∞)
Uniform	lower a, upper b	[a, b]
Exponential	rate λ	[0, ∞)
Gamma	shape α, rate β	(0, ∞)
Beta	shape α, shape β	[0, 1]
ChiSquared	degrees of freedom k	[0, ∞)
StudentT	degrees of freedom ν	(−∞, ∞)

Discrete distributions

Distribution	Parameters	Support
Bernoulli	probability p	{0, 1}
Binomial	trials n, probability p	{0, …, n}
Poisson	rate λ	{0, 1, 2, …}

Example

use numeris::stats::{Normal, ContinuousDistribution};

let n = Normal::new(0.0_f64, 1.0).unwrap();
assert!((n.cdf(0.0) - 0.5).abs() < 1e-14);
assert!((n.mean()).abs() < 1e-14);

Structs

Bernoulli

Bernoulli distribution with success probability p.

Beta

Beta distribution with shape parameters α and β on [0, 1].

Binomial

Binomial distribution B(n, p).

ChiSquared

Chi-squared distribution with k degrees of freedom.

Exponential

Exponential distribution with rate λ.

Gamma

Gamma distribution with shape α and rate β.

Normal

Normal (Gaussian) distribution N(μ, σ²).

Poisson

Poisson distribution with rate λ.

Rng

xoshiro256++ pseudo-random number generator.

StudentT

Student’s t-distribution with ν degrees of freedom.

Uniform

Continuous uniform distribution on [a, b].

Enums

StatsError

Errors from distribution construction.

Traits

ContinuousDistribution

Trait for continuous probability distributions.

DiscreteDistribution

Trait for discrete probability distributions.