The **@stdlib/random-base-poisson** package provides a fast, high‑quality pseudorandom number generator for the Poisson distribution, usable directly (`poisson(lambda)`) or via a configurable factory that supports custom seeds, state sharing, and custom underlying uniform PRNGs. It is aimed at developers and researchers needing reliable Poisson‑distributed random variates in JavaScript/Node.js (and browser, Deno, Observable) as part of the stdlib numerical computing ecosystem.
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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.
Natural logarithm of the square root of 2π.
Test if a double-precision floating-point numeric value is NaN.
Compute the absolute value of a double-precision floating-point number.
Natural exponential function.
Evaluate the natural logarithm of the factorial function.
Round a double-precision floating-point number toward negative infinity.
Evaluate the natural logarithm of a double-precision floating-point number.
Evaluate the signum function for a double-precision floating-point number.
Compute the principal square root of a double-precision floating-point number.
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.
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.
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) based on Park and Miller.
A linear congruential pseudorandom number generator (LCG) whose output is shuffled.
Uniformly distributed pseudorandom numbers between 0 and 1.
Uniformly distributed pseudorandom numbers.
Perform a chi-square 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 ca
Minimal TAP output formatter.
Benchmark harness.
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