Cauchy distributed pseudorandom numbers.
JavaScript
2
0
Apache License 2.0
This project provides functions for generating pseudorandom numbers that follow a Cauchy distribution, a continuous probability distribution. It's part of the stdlib library, a JavaScript standard library for numerical and scientific computing. The library is designed for developers who need to generate random numbers for statistical simulations, modeling, or other applications requiring Cauchy-distributed values.
Total donated
Undistributed
Share with your subscribers:
Recipients
How the donated funds are distributed
Support the dependencies of stdlib-js/random-base-cauchy
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 NaN.
Test if a value is a number.
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 tangent of a number.
Normally distributed pseudorandom numbers using the improved Ziggurat method.
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.
Create a zero-filled generic array.
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.
Support the repos that depend on stdlib-js/random-base-cauchy
Top contributors
stdlib-bot
50 contributions
Recent events
Kivach works on the Obyte network, and therefore you can track all donations.
No events yet