Pseudorandom Number Generator Iterators

Pseudorandom number generator (PRNG) iterators.

Usage

var ns = require( '@stdlib/random/iter' );

ns

Namespace containing pseudorandom number generator (PRNG) iterators.

var iterators = ns;
// returns {...}

The namespace contains the following functions for creating iterator protocol-compliant iterators:

• arcsine( a, b[, options] ): create an iterator for generating pseudorandom numbers drawn from an arcsine distribution.
• bernoulli( p[, options] ): create an iterator for generating pseudorandom numbers drawn from a Bernoulli distribution.
• beta( alpha, beta[, options] ): create an iterator for generating pseudorandom numbers drawn from a beta distribution.
• betaprime( alpha, beta[, options] ): create an iterator for generating pseudorandom numbers drawn from a beta prime distribution.
• binomial( n, p[, options] ): create an iterator for generating pseudorandom numbers drawn from a binomial distribution.
• boxMuller( [options] ): create an iterator for generating pseudorandom numbers drawn from a standard normal distribution using the Box-Muller transform.
• cauchy( x0, gamma[, options] ): create an iterator for generating pseudorandom numbers drawn from a Cauchy distribution.
• chi( k[, options] ): create an iterator for generating pseudorandom numbers drawn from a chi distribution.
• chisquare( k[, options] ): create an iterator for generating pseudorandom numbers drawn from a chi-square distribution.
• cosine( mu, s[, options] ): create an iterator for generating pseudorandom numbers drawn from a raised cosine distribution.
• discreteUniform( a, b[, options] ): create an iterator for generating pseudorandom numbers drawn from a discrete uniform distribution.
• erlang( k, lambda[, options] ): create an iterator for generating pseudorandom numbers drawn from an Erlang distribution.
• exponential( lambda[, options] ): create an iterator for generating pseudorandom numbers drawn from an exponential distribution.
• f( d1, d2[, options] ): create an iterator for generating pseudorandom numbers drawn from an F distribution.
• frechet( alpha, s, m[, options] ): create an iterator for generating pseudorandom numbers drawn from a Fréchet distribution.
• gamma( alpha, beta[, options] ): create an iterator for generating pseudorandom numbers drawn from a gamma distribution.
• geometric( p[, options] ): create an iterator for generating pseudorandom numbers drawn from a geometric distribution.
• gumbel( mu, beta[, options] ): create an iterator for generating pseudorandom numbers drawn from a Gumbel distribution.
• hypergeometric( N, K, n[, options] ): create an iterator for generating pseudorandom numbers drawn from a hypergeometric distribution.
• improvedZiggurat( [options] ): create an iterator for generating pseudorandom numbers drawn from a standard normal distribution using the Improved Ziggurat algorithm.
• invgamma( alpha, beta[, options] ): create an iterator for generating pseudorandom numbers drawn from an inverse gamma distribution.
• kumaraswamy( a, b[, options] ): create an iterator for generating pseudorandom numbers drawn from a Kumaraswamy's double bounded distribution.
• laplace( mu, b[, options] ): create an iterator for generating pseudorandom numbers drawn from a Laplace (double exponential) distribution.
• levy( mu, c[, options] ): create an iterator for generating pseudorandom numbers drawn from a Lévy distribution.
• logistic( mu, s[, options] ): create an iterator for generating pseudorandom numbers drawn from a logistic distribution.
• lognormal( mu, sigma[, options] ): create an iterator for generating pseudorandom numbers drawn from a lognormal distribution.
• minstdShuffle( [options] ): create an iterator for a linear congruential pseudorandom number generator (LCG) whose output is shuffled.
• minstd( [options] ): create an iterator for a linear congruential pseudorandom number generator (LCG) based on Park and Miller.
• mt19937( [options] ): create an iterator for a 32-bit Mersenne Twister pseudorandom number generator.
• negativeBinomial( r, p[, options] ): create an iterator for generating pseudorandom numbers drawn from a negative binomial distribution.
• normal( mu, sigma[, options] ): create an iterator for generating pseudorandom numbers drawn from a normal distribution.
• pareto1( alpha, beta[, options] ): create an iterator for generating pseudorandom numbers drawn from a Pareto (Type I) distribution.
• poisson( lambda[, options] ): create an iterator for generating pseudorandom numbers drawn from a Poisson distribution.
• randi( [options] ): create an iterator for generating pseudorandom numbers having integer values.
• randn( [options] ): create an iterator for generating pseudorandom numbers drawn from a standard normal distribution.
• randu( [options] ): create an iterator for generating uniformly distributed pseudorandom numbers between 0 and 1.
• rayleigh( sigma[, options] ): create an iterator for generating pseudorandom numbers drawn from a Rayleigh distribution.
• t( v[, options] ): create an iterator for generating pseudorandom numbers drawn from a Student's t distribution.
• triangular( a, b, c[, options] ): create an iterator for generating pseudorandom numbers drawn from a triangular distribution.
• uniform( a, b[, options] ): create an iterator for generating pseudorandom numbers drawn from a continuous uniform distribution.
• weibull( k, lambda[, options] ): create an iterator for generating pseudorandom numbers drawn from a Weibull distribution.

Examples

var objectKeys = require( '@stdlib/utils/keys' );
var ns = require( '@stdlib/random/iter' );

console.log( objectKeys( ns ) );