npm package: @stdlib/stats-base
Description: Base (i.e., lower-level) statistical functions.
Author: The Stdlib Authors
Latest version: 0.4.1

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<details>
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# Base Statistics

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> Base (i.e., lower-level) statistical functions.

<section class="installation">

## Installation

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

</section>

<section class="usage">

## Usage

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

#### stats

Namespace containing "base" (i.e., lower-level) statistical functions.

```javascript
var ns = stats;
// returns {...}
```

The namespace contains the following sub-namespaces:

<!-- <toc pattern="dists"> -->

<div class="namespace-toc">

-   <span class="signature">[`dists`][@stdlib/stats/base/dists]</span><span class="delimiter">: </span><span class="description">base (i.e., lower-level) probability distribution modules.</span>

</div>

<!-- </toc> -->

The namespace contains the following statistical functions:

<!-- <toc pattern="*"> -->

<div class="namespace-toc">

-   <span class="signature">[`cumax( N, x, strideX, y, strideY )`][@stdlib/stats/base/cumax]</span><span class="delimiter">: </span><span class="description">calculate the cumulative maximum of a strided array.</span>
-   <span class="signature">[`cumaxabs( N, x, strideX, y, strideY )`][@stdlib/stats/base/cumaxabs]</span><span class="delimiter">: </span><span class="description">calculate the cumulative maximum absolute value of a strided array.</span>
-   <span class="signature">[`cumin( N, x, strideX, y, strideY )`][@stdlib/stats/base/cumin]</span><span class="delimiter">: </span><span class="description">calculate the cumulative minimum of a strided array.</span>
-   <span class="signature">[`cuminabs( N, x, strideX, y, strideY )`][@stdlib/stats/base/cuminabs]</span><span class="delimiter">: </span><span class="description">calculate the cumulative minimum absolute value of a strided array.</span>
-   <span class="signature">[`ndarray`][@stdlib/stats/base/ndarray]</span><span class="delimiter">: </span><span class="description">base ndarray statistical functions.</span>
-   <span class="signature">[`sdsnanmean( N, x, stride )`][@stdlib/stats/base/sdsnanmean]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using extended accumulation.</span>
-   <span class="signature">[`snanstdev( N, correction, x, stride )`][@stdlib/stats/base/snanstdev]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array ignoring `NaN` values.</span>
-   <span class="signature">[`snanstdevch( N, correction, x, stride )`][@stdlib/stats/base/snanstdevch]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array ignoring `NaN` values and using a one-pass trial mean algorithm.</span>
-   <span class="signature">[`snanstdevpn( N, correction, x, stride )`][@stdlib/stats/base/snanstdevpn]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array ignoring `NaN` values and using a two-pass algorithm.</span>
-   <span class="signature">[`snanstdevtk( N, correction, x, stride )`][@stdlib/stats/base/snanstdevtk]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm.</span>
-   <span class="signature">[`snanstdevwd( N, correction, x, stride )`][@stdlib/stats/base/snanstdevwd]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array ignoring `NaN` values and using Welford's algorithm.</span>
-   <span class="signature">[`snanstdevyc( N, correction, x, stride )`][@stdlib/stats/base/snanstdevyc]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and Cramer.</span>
-   <span class="signature">[`snanvariance( N, correction, x, stride )`][@stdlib/stats/base/snanvariance]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array ignoring `NaN` values.</span>
-   <span class="signature">[`snanvariancech( N, correction, x, stride )`][@stdlib/stats/base/snanvariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array ignoring `NaN` values and using a one-pass trial mean algorithm.</span>
-   <span class="signature">[`snanvariancepn( N, correction, x, stride )`][@stdlib/stats/base/snanvariancepn]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array ignoring `NaN` values and using a two-pass algorithm.</span>
-   <span class="signature">[`snanvariancetk( N, correction, x, stride )`][@stdlib/stats/base/snanvariancetk]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm.</span>
-   <span class="signature">[`snanvariancewd( N, correction, x, stride )`][@stdlib/stats/base/snanvariancewd]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array ignoring `NaN` values and using Welford's algorithm.</span>
-   <span class="signature">[`snanvarianceyc( N, correction, x, stride )`][@stdlib/stats/base/snanvarianceyc]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and Cramer.</span>

</div>

<!-- </toc> -->

</section>

<!-- /.usage -->

<!-- Package notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->

<section class="notes">

</section>

<!-- /.notes -->

<section class="examples">

## Examples

<!-- TODO: better examples -->

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

```javascript
var objectKeys = require( '@stdlib/utils-keys' );
var ns = require( '@stdlib/stats-base' );

console.log( objectKeys( ns ) );
```

</section>

<!-- /.examples -->

<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->

<section class="related">

</section>

<!-- /.related -->

<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->


<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

[![Chat][chat-image]][chat-url]

---

## License

See [LICENSE][stdlib-license].


## Copyright

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

</section>

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

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

<!-- <toc-links> -->

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

<!-- </toc-links> -->

</section>

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