stdlib-js/random-base-weibull

Return a JSON representation of a typed array.

Test if an object has a specified property.

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 double-precision floating-point numeric value is NaN.

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

Exponential function.

A 32-bit Mersenne Twister pseudorandom number generator.

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.

Uint32Array.

Test if a value is a Uint32Array.

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.

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

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.

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

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

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

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

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

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

Base pseudorandom number generators.