This project provides a JavaScript function to calculate the probability mass function (PMF) of a Bernoulli distribution, which is used to determine the probability of success or failure in a single trial. It is designed for developers working on statistical analysis or probabilistic modeling in JavaScript, particularly those using the stdlib library for numerical and scientific computing.
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Support the dependencies of stdlib-js/stats-base-dists-bernoulli-pmf
Test if a double-precision floating-point numeric value is NaN.
C APIs for registering a Node-API module exporting an interface for invoking a binary numerical function.
Constant function.
Define a non-enumerable read-only property.
Load a manifest for compiling source files.
Insert array element values and the result of a callback function into a format string and print the result.
Difference between one and the smallest value greater than one that can be represented as a double-precision floating-point number.
Double-precision floating-point positive infinity.
Compute the absolute value of a double-precision floating-point number.
Round a numeric value to the nearest integer.
Create an array containing pseudorandom numbers drawn from a discrete uniform distribution.
Create an array containing pseudorandom numbers drawn from a continuous uniform distribution.
Insert supplied variable values into a format string.
Wrap `require` in a try/catch block.
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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