Normal distribution probability density function (PDF).
JavaScript
3
0
Apache License 2.0
This JavaScript package provides a function to calculate the probability density function (PDF) of a normal distribution, given a value x, mean (μ), and standard deviation (σ). It is part of the stdlib library, a collection of high-performance numerical and scientific computing modules for JavaScript and Node.js. The package is intended for developers working on statistical analysis, data science, or any application requiring normal distribution calculations in JavaScript.
Total donated
Undistributed
Share with your subscribers:
Recipients
How the donated funds are distributed
Support the dependencies of stdlib-js/stats-base-dists-normal-pdf
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 ternary numerical function.
Natural exponential function.
Exponential function.
Compute the principal square root of a double-precision floating-point number.
Degenerate distribution probability density function (PDF).
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 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.
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 cases including unit tests, server side functional tests and browser tests. Built for scale
Minimal TAP output formatter.
Benchmark harness.
Support the repos that depend on this repository
Top contributors
stdlib-bot
67 contributions
Recent events
Kivach works on the Obyte network, and therefore you can track all donations.
No events yet
