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!

Base Statistics

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

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

Installation

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

`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:

- [dists][@stdlib/stats/base/dists]: base (i.e., lower-level) probability distribution modules.

The namespace contains the following statistical functions:

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

`Examples`

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

console.log( objectKeys( ns ) );`

*

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

[test-image]: https://github.com/stdlib-js/stats-base/actions/workflows/test.yml/badge.svg?branch=v0.4.1 [test-url]: https://github.com/stdlib-js/stats-base/actions/workflows/test.yml?query=branch:v0.4.1

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

[chat-image]: https://img.shields.io/badge/zulip-join_chat-brightgreen.svg [chat-url]: https://stdlib.zulipchat.com

[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/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

[@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


  
    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!

`Base Statistics`

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

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

`Installation`

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

`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:

- [dists][@stdlib/stats/base/dists]: base (i.e., lower-level) probability distribution modules.

The namespace contains the following statistical functions:

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

`Examples`

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

console.log( objectKeys( ns ) ); `