This project provides a JavaScript implementation of the cumulative distribution function (CDF) for the exponential distribution, which is a fundamental concept in probability theory and statistics. It's designed for developers and data scientists working with statistical calculations in JavaScript or Node.js environments, particularly those who need to compute exponential distribution probabilities for applications like reliability analysis, queuing theory, or survival analysis.
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Support the dependencies of stdlib-js/stats-base-dists-exponential-cdf
Double-precision floating-point positive infinity.
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.
Natural exponential function.
Constant function.
Define a non-enumerable read-only property.
Load a manifest for compiling source files.
Float64Array.
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 negative infinity.
Compute the absolute value of a double-precision floating-point number.
Create an array containing pseudorandom numbers drawn from a continuous uniform distribution.
Uniformly distributed pseudorandom numbers.
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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