About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

incrcv

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]

> Compute the [coefficient of variation][coefficient-of-variation] (CV) incrementally.

The [corrected sample standard deviation][sample-stdev] is defined as

Equation for the corrected sample standard deviation.

-->

and the [arithmetic mean][arithmetic-mean] is defined as

The [coefficient of variation][coefficient-of-variation] (also known as relative standard deviation, RSD) is defined as

Installation

``bash npm install @stdlib/stats-incr-cv`

`Usage`

`javascript var incrcv = require( '@stdlib/stats-incr-cv' );`

#### incrcv( \[mean] )

Returns an accumulator function which incrementally computes the [coefficient of variation][coefficient-of-variation].

`javascript var accumulator = incrcv();`

If the mean is already known, provide a mean argument.

`javascript var accumulator = incrcv( 3.0 );`

#### accumulator( \[x] )

If provided an input value x, the accumulator function returns an updated accumulated value. If not provided an input value x, the accumulator function returns the current accumulated value.

`javascript var accumulator = incrcv();

var cv = accumulator( 2.0 ); // returns 0.0

cv = accumulator( 1.0 ); // => s^2 = ((2-1.5)^2+(1-1.5)^2) / (2-1) // returns ~0.47

cv = accumulator( 3.0 ); // => s^2 = ((2-2)^2+(1-2)^2+(3-2)^2) / (3-1) // returns 0.5

cv = accumulator(); // returns 0.5`

`Notes`

- Input values are not type checked. If provided NaN or a value which, when used in computations, results in NaN, the accumulated value is NaNfor all future invocations. If non-numeric inputs are possible, you are advised to type check and handle accordingly before passing the value to the accumulator function. - The [coefficient of variation][coefficient-of-variation] is typically computed on nonnegative values. The measure may lack meaning for data which can assume both positive and negative values. - For small and moderately sized samples, the accumulated value tends to be too low and is thus a biased estimator. Provided the generating distribution is known (e.g., a normal distribution), you may want to adjust the accumulated value or use an alternative implementation providing an unbiased estimator.

`Examples`

`javascript var randu = require( '@stdlib/random-base-randu' ); var incrcv = require( '@stdlib/stats-incr-cv' );

var accumulator; var v; var i;

// Initialize an accumulator: accumulator = incrcv();

// For each simulated datum, update the coefficient of variation... for ( i = 0; i < 100; i++ ) { v = randu() * 100.0; accumulator( v ); } console.log( accumulator() );`

*

`Notice`

This package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].

#### Community

[![Chat][chat-image]][chat-url]

---

`License`

See [LICENSE][stdlib-license].

`Copyright`

[npm-image]: http://img.shields.io/npm/v/@stdlib/stats-incr-cv.svg [npm-url]: https://npmjs.org/package/@stdlib/stats-incr-cv

[test-image]: https://github.com/stdlib-js/stats-incr-cv/actions/workflows/test.yml/badge.svg?branch=v0.2.3 [test-url]: https://github.com/stdlib-js/stats-incr-cv/actions/workflows/test.yml?query=branch:v0.2.3

[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-incr-cv/main.svg [coverage-url]: https://codecov.io/github/stdlib-js/stats-incr-cv?branch=main

[chat-image]: https://img.shields.io/badge/zulip-join_chat-brightgreen.svg [chat-url]: https://stdlib.zulipchat.com

[stdlib]: https://github.com/stdlib-js/stdlib

[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors

[umd]: https://github.com/umdjs/umd [es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules

[deno-url]: https://github.com/stdlib-js/stats-incr-cv/tree/deno [deno-readme]: https://github.com/stdlib-js/stats-incr-cv/blob/deno/README.md [umd-url]: https://github.com/stdlib-js/stats-incr-cv/tree/umd [umd-readme]: https://github.com/stdlib-js/stats-incr-cv/blob/umd/README.md [esm-url]: https://github.com/stdlib-js/stats-incr-cv/tree/esm [esm-readme]: https://github.com/stdlib-js/stats-incr-cv/blob/esm/README.md [branches-url]: https://github.com/stdlib-js/stats-incr-cv/blob/main/branches.md

[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-incr-cv/main/LICENSE

[coefficient-of-variation]: https://en.wikipedia.org/wiki/Coefficient_of_variation

[arithmetic-mean]: https://en.wikipedia.org/wiki/Arithmetic_mean

[sample-stdev]: https://en.wikipedia.org/wiki/Standard_deviation

[@stdlib/stats/incr/mean]: https://www.npmjs.com/package/@stdlib/stats-incr-mean

[@stdlib/stats/incr/mcv]: https://www.npmjs.com/package/@stdlib/stats-incr-mcv

[@stdlib/stats/incr/stdev]: https://www.npmjs.com/package/@stdlib/stats-incr-stdev

[@stdlib/stats/incr/vmr]: https://www.npmjs.com/package/@stdlib/stats-incr-vmr


  
    About stdlib...
  

  We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

  The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

  When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

  To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

`incrcv`

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]

> Compute the [coefficient of variation][coefficient-of-variation] (CV) incrementally.

The [corrected sample standard deviation][sample-stdev] is defined as

 -->
and the [arithmetic mean][arithmetic-mean] is defined as
The [coefficient of variation][coefficient-of-variation] (also known as relative standard deviation, RSD) is defined as

`Installation`

``bash npm install @stdlib/stats-incr-cv `

`Usage`

`javascript var incrcv = require( '@stdlib/stats-incr-cv' ); `

#### incrcv( \[mean] )

Returns an accumulator function which incrementally computes the [coefficient of variation][coefficient-of-variation].

`javascript var accumulator = incrcv(); `

If the mean is already known, provide a mean argument.

`javascript var accumulator = incrcv( 3.0 ); `

#### accumulator( \[x] )

If provided an input value x, the accumulator function returns an updated accumulated value. If not provided an input value x, the accumulator function returns the current accumulated value.

`javascript var accumulator = incrcv();

var cv = accumulator( 2.0 ); // returns 0.0

cv = accumulator( 1.0 ); // => s^2 = ((2-1.5)^2+(1-1.5)^2) / (2-1) // returns ~0.47

cv = accumulator( 3.0 ); // => s^2 = ((2-2)^2+(1-2)^2+(3-2)^2) / (3-1) // returns 0.5

cv = accumulator(); // returns 0.5 `

`Notes`

- Input values are not type checked. If provided NaN or a value which, when used in computations, results in NaN, the accumulated value is NaN for all future invocations. If non-numeric inputs are possible, you are advised to type check and handle accordingly before passing the value to the accumulator function. - The [coefficient of variation][coefficient-of-variation] is typically computed on nonnegative values. The measure may lack meaning for data which can assume both positive and negative values. - For small and moderately sized samples, the accumulated value tends to be too low and is thus a biased estimator. Provided the generating distribution is known (e.g., a normal distribution), you may want to adjust the accumulated value or use an alternative implementation providing an unbiased estimator.

`Examples`

`javascript var randu = require( '@stdlib/random-base-randu' ); var incrcv = require( '@stdlib/stats-incr-cv' );

var accumulator; var v; var i;

// Initialize an accumulator: accumulator = incrcv();

// For each simulated datum, update the coefficient of variation... for ( i = 0; i < 100; i++ ) { v = randu() * 100.0; accumulator( v ); } console.log( accumulator() ); `