stdlib-js/random-base-gamma

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 function.

Test if a value is a plain object.

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

Test if a value is a Uint32Array.

Copy values from x into y.

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

Evaluate the natural logarithm of a double-precision floating-point number.

Exponential function.

Compute the principal square root of a double-precision floating-point number.

Copy enumerable own properties from one or more source objects to a target object.

Insert supplied variable values into a format string.

stdlib TypeScript type declarations.

Constant function.

Define a non-enumerable read-only accessor.

Define a non-enumerable read-only property.

Define a non-enumerable read-write accessor.

Function which does nothing.

Format an error message for production.

Difference between one and the smallest value greater than one that can be represented as a double-precision floating-point number.

Maximum unsigned 32-bit integer.

Object containing the user environment.

A linear congruential pseudorandom number generator (LCG) based on Park and Miller.

Uniformly distributed pseudorandom numbers between 0 and 1.

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

Time in seconds since the epoch.

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.

Normally distributed pseudorandom numbers using the improved Ziggurat method.

A 32-bit Mersenne Twister pseudorandom number generator.

Chi-square distributed pseudorandom numbers.

Inverse gamma distributed pseudorandom numbers.

Beta prime distributed pseudorandom numbers.

Negative binomial distributed pseudorandom numbers.

Create a readable stream for generating pseudorandom numbers drawn from a gamma distribution.

Create an iterator for generating pseudorandom numbers drawn from a gamma distribution.

Create an array containing pseudorandom numbers drawn from a gamma distribution.

Fill a strided array with pseudorandom numbers drawn from a gamma distribution.

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

Create a readable stream for generating pseudorandom numbers drawn from a gamma distribution.

Base pseudorandom number generators.