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nanmeanpn

Compute the arithmetic mean of a one-dimensional ndarray, ignoring NaN values and using a two-pass error correction algorithm.

The arithmetic mean is defined as

$$\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i$$

Installation

npm install @stdlib/stats-base-ndarray-nanmeanpn

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

var nanmeanpn = require( '@stdlib/stats-base-ndarray-nanmeanpn' );

nanmeanpn( arrays )

Computes the arithmetic mean of a one-dimensional ndarray, ignoring NaN values and using a two-pass error correction algorithm.

var ndarray = require( '@stdlib/ndarray-base-ctor' );

var xbuf = [ 1.0, 3.0, NaN, 2.0 ];
var x = new ndarray( 'generic', xbuf, [ 4 ], [ 1 ], 0, 'row-major' );

var v = nanmeanpn( [ x ] );
// returns 2.0

The function has the following parameters:

• arrays: array-like object containing a one-dimensional input ndarray.

Notes

• If provided an empty one-dimensional ndarray, the function returns NaN.

Examples

var uniform = require( '@stdlib/random-base-uniform' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var bernoulli = require( '@stdlib/random-base-bernoulli' );
var ndarray = require( '@stdlib/ndarray-base-ctor' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var nanmeanpn = require( '@stdlib/stats-base-ndarray-nanmeanpn' );

function rand() {
    if ( bernoulli( 0.8 ) < 1 ) {
        return NaN;
    }
    return uniform( -50.0, 50.0 );
}

var xbuf = filledarrayBy( 10, 'generic', rand );
var x = new ndarray( 'generic', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
console.log( ndarray2array( x ) );

var v = nanmeanpn( [ x ] );
console.log( v );

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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.

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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.

Community

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License

See LICENSE.

Copyright

Copyright © 2016-2025. The Stdlib Authors.