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!

meanpn

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

> Calculate the [arithmetic mean][arithmetic-mean] of a strided array using a two-pass error correction algorithm.

The [arithmetic mean][arithmetic-mean] is defined as

Installation

``bash npm install @stdlib/stats-base-meanpn`

`Usage`

`javascript var meanpn = require( '@stdlib/stats-base-meanpn' );`

#### meanpn( N, x, stride )

Computes the [arithmetic mean][arithmetic-mean] of a strided array x using a two-pass error correction algorithm.

`javascript var x = [ 1.0, -2.0, 2.0 ]; var N = x.length;

var v = meanpn( N, x, 1 ); // returns ~0.3333`

The function has the following parameters:

- N: number of indexed elements. - x: input [Array][mdn-array] or [typed array][mdn-typed-array]. - stride: index increment forx.

The N and stride parameters determine which elements in x are accessed at runtime. For example, to compute the [arithmetic mean][arithmetic-mean] of every other element in x,

`javascript var floor = require( '@stdlib/math-base-special-floor' );

var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ]; var N = floor( x.length / 2 );

var v = meanpn( N, x, 2 ); // returns 1.25`

Note that indexing is relative to the first index. To introduce an offset, use [typed array][mdn-typed-array] views.

`javascript var Float64Array = require( '@stdlib/array-float64' ); var floor = require( '@stdlib/math-base-special-floor' );

var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] ); var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var N = floor( x0.length / 2 );

var v = meanpn( N, x1, 2 ); // returns 1.25`

#### meanpn.ndarray( N, x, stride, offset )

Computes the [arithmetic mean][arithmetic-mean] of a strided array using a two-pass error correction algorithm and alternative indexing semantics.

`javascript var x = [ 1.0, -2.0, 2.0 ]; var N = x.length;

var v = meanpn.ndarray( N, x, 1, 0 ); // returns ~0.33333`

The function has the following additional parameters:

- offset: starting index for x.

While [typed array][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameter supports indexing semantics based on a starting index. For example, to calculate the [arithmetic mean][arithmetic-mean] for every other value in x starting from the second value

`javascript var floor = require( '@stdlib/math-base-special-floor' );

var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ]; var N = floor( x.length / 2 );

var v = meanpn.ndarray( N, x, 2, 1 ); // returns 1.25`

`Notes`

- If N <= 0, both functions return NaN. - Depending on the environment, the typed versions ([dmeanpn][@stdlib/stats/base/dmeanpn], [smeanpn][@stdlib/stats/base/smeanpn], etc.) are likely to be significantly more performant.

`Examples`

`javascript var randu = require( '@stdlib/random-base-randu' ); var round = require( '@stdlib/math-base-special-round' ); var Float64Array = require( '@stdlib/array-float64' ); var meanpn = require( '@stdlib/stats-base-meanpn' );

var x; var i;

x = new Float64Array( 10 ); for ( i = 0; i < x.length; i++ ) { x[ i ] = round( (randu()*100.0) - 50.0 ); } console.log( x );

var v = meanpn( x.length, x, 1 ); console.log( v );`

*

`References`

- Neely, Peter M. 1966. "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients." _Communications of the ACM_ 9 (7). Association for Computing Machinery: 496–99. doi:[10.1145/365719.365958][@neely:1966a]. - Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In _Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. New York, NY, USA: Association for Computing Machinery. doi:[10.1145/3221269.3223036][@schubert:2018a].

*

`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-base-meanpn.svg [npm-url]: https://npmjs.org/package/@stdlib/stats-base-meanpn

[test-image]: https://github.com/stdlib-js/stats-base-meanpn/actions/workflows/test.yml/badge.svg?branch=v0.2.2 [test-url]: https://github.com/stdlib-js/stats-base-meanpn/actions/workflows/test.yml?query=branch:v0.2.2

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

[chat-image]: https://img.shields.io/gitter/room/stdlib-js/stdlib.svg [chat-url]: https://app.gitter.im/#/room/#stdlib-js_stdlib:gitter.im

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

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

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

[mdn-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array

[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray

[@neely:1966a]: https://doi.org/10.1145/365719.365958

[@schubert:2018a]: https://doi.org/10.1145/3221269.3223036

[@stdlib/stats/base/dmeanpn]: https://www.npmjs.com/package/@stdlib/stats-base-dmeanpn

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

[@stdlib/stats/base/nanmeanpn]: https://www.npmjs.com/package/@stdlib/stats-base-nanmeanpn

[@stdlib/stats/base/smeanpn]: https://www.npmjs.com/package/@stdlib/stats-base-smeanpn


  
    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!

`meanpn`

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

> Calculate the [arithmetic mean][arithmetic-mean] of a strided array using a two-pass error correction algorithm.

The [arithmetic mean][arithmetic-mean] is defined as

`Installation`

``bash npm install @stdlib/stats-base-meanpn `

`Usage`

`javascript var meanpn = require( '@stdlib/stats-base-meanpn' ); `

#### meanpn( N, x, stride )

Computes the [arithmetic mean][arithmetic-mean] of a strided array x using a two-pass error correction algorithm.

`javascript var x = [ 1.0, -2.0, 2.0 ]; var N = x.length;

var v = meanpn( N, x, 1 ); // returns ~0.3333 `

The function has the following parameters:

- N: number of indexed elements. - x: input [Array][mdn-array] or [typed array][mdn-typed-array]. - stride: index increment for x.

The N and stride parameters determine which elements in x are accessed at runtime. For example, to compute the [arithmetic mean][arithmetic-mean] of every other element in x,

`javascript var floor = require( '@stdlib/math-base-special-floor' );

var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ]; var N = floor( x.length / 2 );

var v = meanpn( N, x, 2 ); // returns 1.25 `

Note that indexing is relative to the first index. To introduce an offset, use [typed array][mdn-typed-array] views.

`javascript var Float64Array = require( '@stdlib/array-float64' ); var floor = require( '@stdlib/math-base-special-floor' );

var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] ); var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var N = floor( x0.length / 2 );

var v = meanpn( N, x1, 2 ); // returns 1.25 `

#### meanpn.ndarray( N, x, stride, offset )

Computes the [arithmetic mean][arithmetic-mean] of a strided array using a two-pass error correction algorithm and alternative indexing semantics.

`javascript var x = [ 1.0, -2.0, 2.0 ]; var N = x.length;

var v = meanpn.ndarray( N, x, 1, 0 ); // returns ~0.33333 `

The function has the following additional parameters:

- offset: starting index for x.

While [typed array][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameter supports indexing semantics based on a starting index. For example, to calculate the [arithmetic mean][arithmetic-mean] for every other value in x starting from the second value

`javascript var floor = require( '@stdlib/math-base-special-floor' );

var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ]; var N = floor( x.length / 2 );

var v = meanpn.ndarray( N, x, 2, 1 ); // returns 1.25 `

`Notes`

- If N <= 0, both functions return NaN. - Depending on the environment, the typed versions ([dmeanpn][@stdlib/stats/base/dmeanpn], [smeanpn][@stdlib/stats/base/smeanpn], etc.) are likely to be significantly more performant.

`Examples`

`javascript var randu = require( '@stdlib/random-base-randu' ); var round = require( '@stdlib/math-base-special-round' ); var Float64Array = require( '@stdlib/array-float64' ); var meanpn = require( '@stdlib/stats-base-meanpn' );

var x; var i;

x = new Float64Array( 10 ); for ( i = 0; i < x.length; i++ ) { x[ i ] = round( (randu()*100.0) - 50.0 ); } console.log( x );

var v = meanpn( x.length, x, 1 ); console.log( v ); `

*

`References`

*

`Notice`

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-base-meanpn.svg [npm-url]: https://npmjs.org/package/@stdlib/stats-base-meanpn

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

[chat-image]: https://img.shields.io/gitter/room/stdlib-js/stdlib.svg [chat-url]: https://app.gitter.im/#/room/#stdlib-js_stdlib:gitter.im

[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

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

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

[mdn-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array

[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray

[@neely:1966a]: https://doi.org/10.1145/365719.365958

[@schubert:2018a]: https://doi.org/10.1145/3221269.3223036

[@stdlib/stats/base/dmeanpn]: https://www.npmjs.com/package/@stdlib/stats-base-dmeanpn

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

[@stdlib/stats/base/nanmeanpn]: https://www.npmjs.com/package/@stdlib/stats-base-nanmeanpn

[@stdlib/stats/base/smeanpn]: https://www.npmjs.com/package/@stdlib/stats-base-smeanpn

@stdlib/stats-base-meanpn

meanpn

Installation

Usage

Notes

Examples

References

See Also

Notice

License

Copyright

@stdlib/stats-base-meanpn

meanpn

Installation

Usage

Notes

Examples

References

See Also

Notice

License

Copyright

`Usage`

`Notes`

`Examples`

`References`

`See Also`

`Notice`

`License`

`Copyright`

`@stdlib/stats-base-meanpn`

`meanpn`

`Installation`

`Usage`

`Notes`

`Examples`

`References`

`See Also`

`Notice`

`License`

`Copyright`