npm package: @stdlib/blas-ext-base-ndarray-dnansumpw
Description: Compute the sum of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using pairwise summation.
Author: The Stdlib Authors
Latest version: 0.1.1

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# dnansumpw

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

> Compute the sum of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using pairwise summation.

<section class="intro">

</section>

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<section class="installation">

## Installation

```bash
npm install @stdlib/blas-ext-base-ndarray-dnansumpw
```

</section>

<section class="usage">

## Usage

```javascript
var dnansumpw = require( '@stdlib/blas-ext-base-ndarray-dnansumpw' );
```

#### dnansumpw( arrays )

Computes the sum of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using pairwise summation.

```javascript
var Float64Array = require( '@stdlib/array-float64' );
var ndarray = require( '@stdlib/ndarray-base-ctor' );

var xbuf = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
var x = new ndarray( 'float64', xbuf, [ 4 ], [ 1 ], 0, 'row-major' );

var v = dnansumpw( [ x ] );
// returns 1.0
```

The function has the following parameters:

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

</section>

<!-- /.usage -->

<section class="notes">

## Notes

-   If provided an empty one-dimensional ndarray, the function returns `0.0`.
-   In general, pairwise summation is more numerically stable than ordinary recursive summation (i.e., "simple" summation), with slightly worse performance. While not the most numerically stable summation technique (e.g., compensated summation techniques such as the Kahan–Babuška-Neumaier algorithm are generally more numerically stable), pairwise summation strikes a reasonable balance between numerical stability and performance. If either numerical stability or performance is more desirable for your use case, consider alternative summation techniques.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var bernoulli = require( '@stdlib/random-base-bernoulli' );
var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var ndarray = require( '@stdlib/ndarray-base-ctor' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var dnansumpw = require( '@stdlib/blas-ext-base-ndarray-dnansumpw' );

function clbk() {
    if ( bernoulli( 0.7 ) > 0 ) {
        return discreteUniform( 0, 100 );
    }
    return NaN;
}

var xbuf = filledarrayBy( 10, 'float64', clbk );
var x = new ndarray( 'float64', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
console.log( ndarray2array( x ) );

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

</section>

<!-- /.examples -->

<section class="references">

## References

-   Higham, Nicholas J. 1993. "The Accuracy of Floating Point Summation." _SIAM Journal on Scientific Computing_ 14 (4): 783–99. doi:[10.1137/0914050][@higham:1993a].

</section>

<!-- /.references -->

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<section class="related">

</section>

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<section class="main-repo" >

* * *

## 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

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---

## License

See [LICENSE][stdlib-license].


## Copyright

Copyright &copy; 2016-2026. The Stdlib [Authors][stdlib-authors].

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[umd]: https://github.com/umdjs/umd
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[deno-url]: https://github.com/stdlib-js/blas-ext-base-ndarray-dnansumpw/tree/deno
[deno-readme]: https://github.com/stdlib-js/blas-ext-base-ndarray-dnansumpw/blob/deno/README.md
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[esm-url]: https://github.com/stdlib-js/blas-ext-base-ndarray-dnansumpw/tree/esm
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[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/blas-ext-base-ndarray-dnansumpw/main/LICENSE

[@higham:1993a]: https://doi.org/10.1137/0914050

</section>

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