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!

incrstdev

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

> Compute a [corrected sample standard deviation][sample-stdev] incrementally.

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

Equation for the corrected sample standard deviation.

Installation

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

`Usage`

`javascript var incrstdev = require( '@stdlib/stats-incr-stdev' );`

#### incrstdev( \[mean] )

Returns an accumulator function which incrementally computes a [corrected sample standard deviation][sample-stdev].

`javascript var accumulator = incrstdev();`

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

`javascript var accumulator = incrstdev( 3.0 );`

#### accumulator( \[x] )

If provided an input value x, the accumulator function returns an updated [corrected sample standard deviation][sample-stdev]. If not provided an input value x, the accumulator function returns the current [corrected sample standard deviation][sample-stdev].

`javascript var accumulator = incrstdev();

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

s = accumulator( 1.0 ); // => sqrt(((2-1.5)^2+(1-1.5)^2) / (2-1)) // returns ~0.7071

s = accumulator( 3.0 ); // => sqrt(((2-2)^2+(1-2)^2+(3-2)^2) / (3-1)) // returns 1.0

s = accumulator(); // returns 1.0`

`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.

`Examples`

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

var accumulator; var v; var i;

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

// For each simulated datum, update the sample standard deviation... 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-stdev.svg [npm-url]: https://npmjs.org/package/@stdlib/stats-incr-stdev

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

[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-incr-stdev/main.svg [coverage-url]: https://codecov.io/github/stdlib-js/stats-incr-stdev?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-stdev/tree/deno [deno-readme]: https://github.com/stdlib-js/stats-incr-stdev/blob/deno/README.md [umd-url]: https://github.com/stdlib-js/stats-incr-stdev/tree/umd [umd-readme]: https://github.com/stdlib-js/stats-incr-stdev/blob/umd/README.md [esm-url]: https://github.com/stdlib-js/stats-incr-stdev/tree/esm [esm-readme]: https://github.com/stdlib-js/stats-incr-stdev/blob/esm/README.md [branches-url]: https://github.com/stdlib-js/stats-incr-stdev/blob/main/branches.md

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

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

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

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

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

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

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

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


  
    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!

`incrstdev`

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

> Compute a [corrected sample standard deviation][sample-stdev] incrementally.

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

`Installation`

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

`Usage`

`javascript var incrstdev = require( '@stdlib/stats-incr-stdev' ); `

#### incrstdev( \[mean] )

Returns an accumulator function which incrementally computes a [corrected sample standard deviation][sample-stdev].

`javascript var accumulator = incrstdev(); `

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

`javascript var accumulator = incrstdev( 3.0 ); `

#### accumulator( \[x] )

If provided an input value x, the accumulator function returns an updated [corrected sample standard deviation][sample-stdev]. If not provided an input value x, the accumulator function returns the current [corrected sample standard deviation][sample-stdev].

`javascript var accumulator = incrstdev();

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

s = accumulator( 1.0 ); // => sqrt(((2-1.5)^2+(1-1.5)^2) / (2-1)) // returns ~0.7071

s = accumulator( 3.0 ); // => sqrt(((2-2)^2+(1-2)^2+(3-2)^2) / (3-1)) // returns 1.0

s = accumulator(); // returns 1.0 `

`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.

`Examples`

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

var accumulator; var v; var i;

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

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