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Base ndarray statistical functions.
npm install @stdlib/stats-base-ndarrayAlternatively,
- To load the package in a website via a
scripttag without installation and bundlers, use the ES Module available on theesmbranch (see README). - If you are using Deno, visit the
denobranch (see README for usage intructions). - For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the
umdbranch (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.
var ns = require( '@stdlib/stats-base-ndarray' );Namespace containing base ndarray statistical functions.
var o = ns;
// returns {...}The namespace exposes the following APIs:
covarmtk( arrays ): calculate the covariance of two one-dimensional ndarrays provided known means and using a one-pass textbook algorithm.cumax( arrays ): compute the cumulative maximum value of a one-dimensional ndarray.cumin( arrays ): compute the cumulative minimum value of a one-dimensional ndarray.dcovarmtk( arrays ): calculate the covariance of two one-dimensional double-precision floating-point ndarrays provided known means and using a one-pass textbook algorithm.dcumax( arrays ): compute the cumulative maximum value of a one-dimensional double-precision floating-point ndarray.dcumin( arrays ): compute the cumulative minimum value of a one-dimensional double-precision floating-point ndarray.dmax( arrays ): compute the maximum value of a one-dimensional double-precision floating-point ndarray.dmaxabs( arrays ): compute the maximum absolute value of a one-dimensional double-precision floating-point ndarray.dmaxsorted( arrays ): compute the maximum value of a sorted one-dimensional double-precision floating-point ndarray.dmean( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray.dmin( arrays ): compute the minimum value of a one-dimensional double-precision floating-point ndarray.dminabs( arrays ): compute the minimum absolute value of a one-dimensional double-precision floating-point ndarray.dnanmax( arrays ): compute the maximum value of a one-dimensional double-precision floating-point ndarray, ignoringNaNvalues.dnanmean( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoringNaNvalues.dnanmin( arrays ): compute the minimum value of a one-dimensional double-precision floating-point ndarray, ignoringNaNvalues.drange( arrays ): compute the range of a one-dimensional double-precision floating-point ndarray.dztest( arrays ): compute a one-sample Z-test for a one-dimensional double-precision floating-point ndarray.dztest2( arrays ): compute a two-sample Z-test for two one-dimensional double-precision floating-point ndarrays.maxBy( arrays, clbk[, thisArg ] ): compute the maximum value of a one-dimensional ndarray via a callback function.max( arrays ): compute the maximum value of a one-dimensional ndarray.maxabs( arrays ): compute the maximum absolute value of a one-dimensional ndarray.maxsorted( arrays ): compute the maximum value of a sorted one-dimensional ndarray.mean( arrays ): compute the arithmetic mean of a one-dimensional ndarray.minBy( arrays, clbk[, thisArg ] ): compute the minimum value of a one-dimensional ndarray via a callback function.min( arrays ): compute the minimum value of a one-dimensional ndarray.minabs( arrays ): compute the minimum absolute value of a one-dimensional ndarray.nanmax( arrays ): compute the maximum value of a one-dimensional ndarray, ignoringNaNvalues.nanmean( arrays ): compute the arithmetic mean of a one-dimensional ndarray, ignoringNaNvalues.nanmin( arrays ): compute the minimum value of a one-dimensional ndarray, ignoringNaNvalues.range( arrays ): compute the range of a one-dimensional ndarray.scovarmtk( arrays ): calculate the covariance of two one-dimensional single-precision floating-point ndarrays provided known means and using a one-pass textbook algorithm.scumax( arrays ): compute the cumulative maximum value of a one-dimensional single-precision floating-point ndarray.scumin( arrays ): compute the cumulative minimum value of a one-dimensional single-precision floating-point ndarray.smax( arrays ): compute the maximum value of a one-dimensional single-precision floating-point ndarray.smaxabs( arrays ): compute the maximum absolute value of a one-dimensional single-precision floating-point ndarray.smaxsorted( arrays ): compute the maximum value of a sorted one-dimensional single-precision floating-point ndarray.smean( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray.smin( arrays ): compute the minimum value of a one-dimensional single-precision floating-point ndarray.sminabs( arrays ): compute the minimum absolute value of a one-dimensional single-precision floating-point ndarray.snanmax( arrays ): compute the maximum value of a one-dimensional single-precision floating-point ndarray, ignoringNaNvalues.snanmean( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray, ignoringNaNvalues.snanmin( arrays ): compute the minimum value of a one-dimensional single-precision floating-point ndarray, ignoringNaNvalues.srange( arrays ): compute the range of a one-dimensional single-precision floating-point ndarray.sztest( arrays ): compute a one-sample Z-test for a one-dimensional single-precision floating-point ndarray.sztest2( arrays ): compute a two-sample Z-test for two one-dimensional single-precision floating-point ndarrays.ztest( arrays ): compute a one-sample Z-test for a one-dimensional ndarray.ztest2( arrays ): compute a two-sample Z-test for two one-dimensional ndarrays.
var objectKeys = require( '@stdlib/utils-keys' );
var ns = require( '@stdlib/stats-base-ndarray' );
console.log( objectKeys( ns ) );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.
See LICENSE.
Copyright © 2016-2025. The Stdlib Authors.
