stdlib-js/random-base-mt19937

Return a JSON representation of a typed array.

Uint32Array.

Test if an object has a specified property.

Test if a value is a boolean.

Test if a value is a collection.

Test if a value is a plain object.

Test if a value is a number having a positive integer value.

Test if a value is a Uint32Array.

Copy values from x into y.

Maximum safe double-precision floating-point integer.

Maximum unsigned 32-bit integer.

Round a double-precision floating-point number toward negative infinity.

Return the maximum value.

Common APIs shared across base pseudorandom number generators.

Insert supplied variable values into a format string.

stdlib TypeScript type declarations.

Define a non-enumerable read-only accessor.

Define a non-enumerable read-only property.

Define a non-enumerable read-write accessor.

Load a manifest for compiling source files.

Format an error message for production.

Test if a double-precision floating-point numeric value is NaN.

Test if a finite double-precision floating-point number is a nonnegative integer.

Object containing the user environment.

A linear congruential pseudorandom number generator (LCG) whose output is shuffled.

One-sample Kolmogorov-Smirnov goodness-of-fit test.

tap-producing test harness for node and browsers

Yet another JS code coverage tool that computes statement, line, function and branch coverage with module loader hooks to transparently add coverage when running tests. Supports all JS coverage use cases including unit tests, server side functional tests and browser tests. Built for scale

Minimal TAP output formatter.

Benchmark harness.

Perform C-like multiplication of two unsigned 32-bit integers.

Normally distributed pseudorandom numbers using the improved Ziggurat method.

Gamma distributed pseudorandom numbers.

Uniformly distributed pseudorandom numbers.

Raised cosine distributed pseudorandom numbers.

Normally distributed pseudorandom numbers using the Box-Muller transform.

Beta distributed pseudorandom numbers.

Kumaraswamy's double bounded distributed pseudorandom numbers.

Bernoulli distributed pseudorandom numbers.

Binomial distributed pseudorandom numbers.

Exponentially distributed pseudorandom numbers.

Discrete uniform distributed pseudorandom numbers.

Poisson distributed random numbers.

Uniformly distributed pseudorandom numbers between 0 and 1.

Weibull distributed pseudorandom numbers.

Arcsine distributed pseudorandom numbers.

Geometric distributed pseudorandom numbers.

Laplace (double exponential) distributed pseudorandom numbers.

Rayleigh distributed pseudorandom numbers.

Fréchet distributed pseudorandom numbers.

Gumbel distributed pseudorandom numbers.

Hypergeometric distributed pseudorandom numbers.

Pareto (Type I) distributed pseudorandom numbers.

Lévy distributed pseudorandom numbers.

Triangular distributed pseudorandom numbers.

Erlang distributed pseudorandom numbers.

Logistic distributed pseudorandom numbers.

Pseudorandom numbers having integer values.

Sample elements from an array-like object.

Create a readable stream for a 32-bit Mersenne Twister pseudorandom number generator.

Incrementally partition data into `k` clusters.

Fill a strided array with pseudorandom numbers generated using a 32-bit Mersenne Twister pseudorandom number generator.

Create an iterator for a 32-bit Mersenne Twister pseudorandom number generator.

Create an array containing pseudorandom numbers generated using a 32-bit Mersenne Twister pseudorandom number generator.

Revive a JSON-serialized pseudorandom number generator (PRNG).

Base pseudorandom number generators.

Create a readable stream for a 32-bit Mersenne Twister pseudorandom number generator.