@stdlib/stats-base

Standard library base statistical functions.

https://github.com/stdlib-js/stats-base

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base javascript lib library node node-js nodejs standard statistics stats stdlib

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Standard library base statistical functions.

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Host: GitHub
Owner: stdlib-js
License: apache-2.0
Language: JavaScript
Default Branch: main
Homepage: https://github.com/stdlib-js/stdlib
Size: 29.4 MB

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base javascript lib library node node-js nodejs standard statistics stats stdlib

Created over 4 years ago · Last pushed 6 months ago

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Readme Changelog Contributing License Code of conduct Citation Security

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

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

## Installation ```bash npm install @stdlib/stats-base ``` Alternatively, - To load the package in a website via a `script` tag without installation and bundlers, use the [ES Module][es-module] available on the [`esm`][esm-url] branch (see [README][esm-readme]). - If you are using Deno, visit the [`deno`][deno-url] branch (see [README][deno-readme] for usage intructions). - For use in Observable, or in browser/node environments, use the [Universal Module Definition (UMD)][umd] build available on the [`umd`][umd-url] branch (see [README][umd-readme]). The [branches.md][branches-url] 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 ```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. - [`nanmskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/nanmskmax]: calculate the maximum value of a strided array according to a mask, ignoring `NaN` values. - [`nanmskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/nanmskmin]: calculate the minimum value of a strided array according to a mask, ignoring `NaN` values. - [`nanmskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/strided/nanmskrange]: calculate the range of a strided array according to a mask, ignoring `NaN` values. - [`nanrangeBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/strided/nanrange-by]: calculate the range of a strided array via a callback function, ignoring `NaN` values. - [`nanrange( N, x, strideX )`][@stdlib/stats/strided/nanrange]: calculate the range of a strided array, ignoring `NaN` values. - [`nanstdev( N, correction, x, strideX )`][@stdlib/stats/strided/nanstdev]: calculate the standard deviation of a strided array ignoring `NaN` values. - [`nanstdevch( N, correction, x, stride )`][@stdlib/stats/strided/nanstdevch]: calculate the standard deviation of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm. - [`nanstdevpn( N, correction, x, strideX )`][@stdlib/stats/strided/nanstdevpn]: calculate the standard deviation of a strided array ignoring `NaN` values and using a two-pass algorithm. - [`nanstdevtk( N, correction, x, stride )`][@stdlib/stats/strided/nanstdevtk]: calculate the standard deviation of a strided array ignoring `NaN` values and using a one-pass textbook algorithm. - [`nanstdevwd( N, correction, x, stride )`][@stdlib/stats/strided/nanstdevwd]: calculate the standard deviation of a strided array ignoring `NaN` values and using Welford's algorithm. - [`nanstdevyc( N, correction, x, strideX )`][@stdlib/stats/strided/nanstdevyc]: calculate the standard deviation of a strided array ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and Cramer. - [`nanvariance( N, correction, x, strideX )`][@stdlib/stats/strided/nanvariance]: calculate the variance of a strided array ignoring `NaN` values. - [`nanvariancech( N, correction, x, strideX )`][@stdlib/stats/strided/nanvariancech]: calculate the variance of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm. - [`nanvariancepn( N, correction, x, strideX )`][@stdlib/stats/strided/nanvariancepn]: calculate the variance of a strided array ignoring `NaN` values and using a two-pass algorithm. - [`nanvariancetk( N, correction, x, strideX )`][@stdlib/stats/strided/nanvariancetk]: calculate the variance of a strided array ignoring `NaN` values and using a one-pass textbook algorithm. - [`nanvariancewd( N, correction, x, strideX )`][@stdlib/stats/strided/nanvariancewd]: calculate the variance of a strided array ignoring `NaN` values and using Welford's algorithm. - [`nanvarianceyc( N, correction, x, strideX )`][@stdlib/stats/strided/nanvarianceyc]: calculate the variance of a strided array ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and Cramer. - [`ndarray`][@stdlib/stats/base/ndarray]: base ndarray statistical functions. - [`rangeBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/strided/range-by]: calculate the range of a strided array via a callback function. - [`range( N, x, strideX )`][@stdlib/stats/strided/range]: calculate the range of a strided array. - [`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. - [`sdsnanmeanors( N, x, stride )`][@stdlib/stats/base/sdsnanmeanors]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using ordinary recursive summation with extended accumulation. - [`smeankbn( N, x, stride )`][@stdlib/stats/strided/smeankbn]: calculate the arithmetic mean of a single-precision floating-point strided array using an improved Kahan–Babuška algorithm. - [`smeankbn2( N, x, stride )`][@stdlib/stats/strided/smeankbn2]: calculate the arithmetic mean of a single-precision floating-point strided array using a second-order iterative Kahan–Babuška algorithm. - [`smeanlipw( N, x, stride )`][@stdlib/stats/strided/smeanlipw]: calculate the arithmetic mean of a single-precision floating-point strided array using a one-pass trial mean algorithm with pairwise summation. - [`smeanors( N, x, stride )`][@stdlib/stats/strided/smeanors]: calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation. - [`snanmean( N, x, stride )`][@stdlib/stats/base/snanmean]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values. - [`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. - [`sstdevwd( N, correction, x, stride )`][@stdlib/stats/base/sstdevwd]: calculate the standard deviation of a single-precision floating-point strided array using Welford's algorithm. - [`stdev( N, correction, x, strideX )`][@stdlib/stats/strided/stdev]: calculate the standard deviation of a strided array. - [`stdevch( N, correction, x, strideX )`][@stdlib/stats/strided/stdevch]: calculate the standard deviation of a strided array using a one-pass trial mean algorithm. - [`stdevpn( N, correction, x, strideX )`][@stdlib/stats/strided/stdevpn]: calculate the standard deviation of a strided array using a two-pass algorithm. - [`stdevtk( N, correction, x, stride )`][@stdlib/stats/strided/stdevtk]: calculate the standard deviation of a strided array using a one-pass textbook algorithm. - [`stdevwd( N, correction, x, stride )`][@stdlib/stats/strided/stdevwd]: calculate the standard deviation of a strided array using Welford's algorithm. - [`stdevyc( N, correction, x, stride )`][@stdlib/stats/strided/stdevyc]: calculate the standard deviation of a strided array using a one-pass algorithm proposed by Youngs and Cramer. - [`variance( N, correction, x, strideX )`][@stdlib/stats/strided/variance]: calculate the variance of a strided array. - [`variancech( N, correction, x, strideX )`][@stdlib/stats/strided/variancech]: calculate the variance of a strided array using a one-pass trial mean algorithm. - [`variancepn( N, correction, x, strideX )`][@stdlib/stats/strided/variancepn]: calculate the variance of a strided array using a two-pass algorithm. - [`variancetk( N, correction, x, strideX )`][@stdlib/stats/strided/variancetk]: calculate the variance of a strided array using a one-pass textbook algorithm. - [`variancewd( N, correction, x, stride )`][@stdlib/stats/strided/variancewd]: calculate the variance of a strided array using Welford's algorithm. - [`varianceyc( N, correction, x, strideX )`][@stdlib/stats/strided/varianceyc]: calculate the variance of a strided array 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 Copyright © 2016-2025. The Stdlib [Authors][stdlib-authors].

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Standard library for JavaScript.

Citation (CITATION.cff)

cff-version: 1.2.0
title: stdlib
message: >-
  If you use this software, please cite it using the
  metadata from this file.

type: software

authors:
  - name: The Stdlib Authors
    url: https://github.com/stdlib-js/stdlib/graphs/contributors

repository-code: https://github.com/stdlib-js/stdlib
url: https://stdlib.io

abstract: |
  Standard library for JavaScript and Node.js.

keywords:
  - JavaScript
  - Node.js
  - TypeScript
  - standard library
  - scientific computing
  - numerical computing
  - statistical computing

license: Apache-2.0 AND BSL-1.0

date-released: 2016

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npmjs.org: @stdlib/stats-base

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

Homepage: https://stdlib.io
License: Apache-2.0
Latest release: 0.3.2
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@stdlib/stats-base

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