stdlib-js/random-array-kumaraswamy

Create an array containing pseudorandom numbers drawn from Kumaraswamy's double bounded distribution.

JavaScript

Apache License 2.0

This package generates arrays of pseudorandom numbers following Kumaraswamy's double bounded distribution, a probability distribution useful in statistical modeling. It's designed for developers and data scientists working with JavaScript who need to generate random samples from this specific distribution for simulations, statistical analysis, or testing. The library is part of stdlib, a comprehensive numerical computing library for JavaScript and Node.js.

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Support the dependencies of stdlib-js/random-array-kumaraswamy

stdlib-js/array-defaults

Default array settings.

stdlib-js/array-dtypes

List of array data types.

stdlib-js/random-array-tools-binary-factory

Create a factory function for generating pseudorandom values drawn from a binary PRNG.

stdlib-js/random-base-kumaraswamy

Kumaraswamy's double bounded distributed pseudorandom numbers.

stdlib-js/types

stdlib TypeScript type declarations.

stdlib-js/utils-define-nonenumerable-read-only-property

Define a non-enumerable read-only property.

stdlib-js/array-float32

Float32Array.

stdlib-js/array-float64

Float64Array.

stdlib-js/array-nans

Create an array filled with NaNs and having a specified length.

stdlib-js/array-uint32

Uint32Array.

stdlib-js/array-zeros

Create a zero-filled array having a specified length.

stdlib-js/assert-is-uint32array

Test if a value is a Uint32Array.

stdlib-js/console-log-each

Insert array element values into a format string and print the result.

stdlib-js/constants-uint32-max

Maximum unsigned 32-bit integer.

stdlib-js/math-base-assert-is-nan

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

stdlib-js/math-base-assert-is-nanf

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

stdlib-js/math-base-special-pow

Exponential function.

stdlib-js/random-base-minstd

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

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