The `@stdlib/random-base-binomial` package provides functions for generating pseudorandom numbers from a binomial distribution, allowing users to specify the number of trials and success probability. It is designed for developers needing statistical simulations, probabilistic modeling, or random sampling in JavaScript applications, particularly those working in scientific computing or data analysis.
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Support the dependencies of stdlib-js/random-base-binomial
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 integer value.
Test if a value is a probability.
Test if a double-precision floating-point numeric value is NaN.
Test if a finite double-precision floating-point number is a positive integer.
Test if a double-precision floating-point number is a probability.
Compute the absolute value of a double-precision floating-point number.
Round a double-precision floating-point number toward negative infinity.
Evaluate the natural logarithm of a double-precision floating-point number.
Exponential function.
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.
Maximum unsigned 32-bit integer.
Test if a finite double-precision floating-point number is a nonnegative integer.
Round a double-precision floating-point number toward positive infinity.
Round a numeric value to the nearest 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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