# @stdlib/stats-base-meanpn

0.1.0 • Public • Published

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

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

The arithmetic mean is defined as

## Installation

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

## Usage

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

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

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

```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:

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

```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` views.

```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 of a strided array using a two-pass error correction algorithm and alternative indexing semantics.

```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` 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 for every other value in `x` starting from the second value

```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`, `smeanpn`, etc.) are likely to be significantly more performant.

## Examples

```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.
• 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.

## Notice

This package is part of 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, see the main project repository.

## Package Sidebar

### Install

`npm i @stdlib/stats-base-meanpn`

### Repository

github.com/stdlib-js/stats-base-meanpn

stdlib.io

12,622

0.1.0

Apache-2.0

49.3 kB

14