Base

Base ndarray statistical functions.

Installation

npm install @stdlib/stats-base-ndarray

Usage

var ns = require( '@stdlib/stats-base-ndarray' );

ns

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.
• dcumaxabs( arrays ): compute the cumulative maximum absolute 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.
• dcuminabs( arrays ): compute the cumulative minimum absolute 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.
• dmaxabssorted( arrays ): compute the maximum absolute value of a sorted 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.
• dmeankbn( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using an improved Kahan–Babuška algorithm.
• dmeankbn2( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using a second-order iterative Kahan–Babuška algorithm.
• dmeanli( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm.
• dmeanlipw( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm with pairwise summation.
• dmeanors( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using ordinary recursive summation.
• dmeanpn( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using a two-pass error correction algorithm.
• dmeanpw( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using pairwise summation.
• dmeanstdev( arrays ): compute the arithmetic mean and standard deviation of a one-dimensional double-precision floating-point ndarray.
• dmeanwd( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using Welford's algorithm.
• dmediansorted( arrays ): compute the median value of a sorted one-dimensional double-precision floating-point ndarray.
• dmidrange( arrays ): compute the mid-range 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.
• dminsorted( arrays ): compute the minimum value of a sorted one-dimensional double-precision floating-point ndarray.
• dmskmax( arrays ): calculate the maximum value of a one-dimensional double-precision floating-point ndarray according to a mask.
• dmskmin( arrays ): calculate the minimum value of a one-dimensional double-precision floating-point ndarray according to a mask.
• dmskrange( arrays ): calculate the range of a one-dimensional double-precision floating-point ndarray according to a mask.
• dnanmax( arrays ): compute the maximum value of a one-dimensional double-precision floating-point ndarray, ignoring NaN values.
• dnanmaxabs( arrays ): compute the maximum absolute value of a one-dimensional double-precision floating-point ndarray, ignoring NaN values.
• dnanmean( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoring NaN values.
• dnanmeanors( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoring NaN values and using ordinary recursive summation.
• dnanmeanpn( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoring NaN values and using a two-pass error correction algorithm.
• dnanmeanpw( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoring NaN values and using pairwise summation.
• dnanmeanwd( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoring NaN values and using Welford's algorithm.
• dnanmidrange( arrays ): compute the mid-range of a one-dimensional double-precision floating-point ndarray, ignoring NaN values.
• dnanmin( arrays ): compute the minimum value of a one-dimensional double-precision floating-point ndarray, ignoring NaN values.
• dnanminabs( arrays ): compute the minimum absolute value of a one-dimensional double-precision floating-point ndarray, ignoring NaN values.
• dnanmskmax( arrays ): compute the maximum value of a double-precision floating-point ndarray according to a mask, ignoring NaN values.
• dnanmskmin( arrays ): compute the minimum value of a double-precision floating-point ndarray according to a mask, ignoring NaN values.
• dnanmskrange( arrays ): calculate the range of a one-dimensional double-precision floating-point ndarray according to a mask, ignoring NaN values.
• dnanrange( arrays ): compute the range of a one-dimensional double-precision floating-point ndarray, ignoring NaN values.
• drange( arrays ): compute the range of a one-dimensional double-precision floating-point ndarray.
• drangeabs( arrays ): compute the range of absolute values of a one-dimensional double-precision floating-point ndarray.
• dstdev( arrays ): calculate the standard deviation of a one-dimensional double-precision floating-point ndarray.
• dstdevch( arrays ): calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm.
• dstdevpn( arrays ): calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using a two-pass algorithm.
• dstdevtk( arrays ): calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using a one-pass textbook algorithm.
• dstdevwd( arrays ): calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using Welford's algorithm.
• dstdevyc( arrays ): calculate the standard deviation of a one-dimensional double-precision floating-point ndarray using a one-pass algorithm proposed by Youngs and Cramer.
• 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.
• meankbn( arrays ): compute the arithmetic mean of a one-dimensional ndarray using an improved Kahan–Babuška algorithm.
• meankbn2( arrays ): compute the arithmetic mean of a one-dimensional ndarray using a second-order iterative Kahan–Babuška algorithm.
• meanors( arrays ): compute the arithmetic mean of a one-dimensional ndarray using ordinary recursive summation.
• meanpn( arrays ): compute the arithmetic mean of a one-dimensional ndarray using a two-pass error correction algorithm.
• meanpw( arrays ): compute the arithmetic mean of a one-dimensional ndarray using pairwise summation.
• meanwd( arrays ): compute the arithmetic mean of a one-dimensional ndarray using Welford's algorithm.
• mediansorted( arrays ): compute the median value of a sorted one-dimensional ndarray.
• midrangeBy( arrays, clbk[, thisArg ] ): calculate the mid-range of a one-dimensional ndarray via a callback function.
• midrange( arrays ): compute the mid-range 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.
• minsorted( arrays ): compute the minimum value of a sorted one-dimensional ndarray.
• mskmax( arrays ): calculate the maximum value of a one-dimensional ndarray according to a mask.
• mskmaxabs( arrays ): calculate the maximum absolute value of a one-dimensional ndarray according to a mask.
• mskmidrange( arrays ): calculate the mid-range of a one-dimensional ndarray according to a mask.
• mskmin( arrays ): calculate the minimum value of a one-dimensional ndarray according to a mask.
• mskrange( arrays ): calculate the range of a one-dimensional ndarray according to a mask.
• nanmaxBy( arrays, clbk[, thisArg ] ): compute the maximum value of a one-dimensional ndarray via a callback function, ignoring NaN values.
• nanmax( arrays ): compute the maximum value of a one-dimensional ndarray, ignoring NaN values.
• nanmaxabs( arrays ): compute the maximum absolute value of a one-dimensional ndarray, ignoring NaN values.
• nanmean( arrays ): compute the arithmetic mean of a one-dimensional ndarray, ignoring NaN values.
• nanmeanors( arrays ): compute the arithmetic mean of a one-dimensional ndarray, ignoring NaN values and using ordinary recursive summation.
• nanmeanpn( arrays ): compute the arithmetic mean of a one-dimensional ndarray, ignoring NaN values and using a two-pass error correction algorithm.
• nanmeanwd( arrays ): compute the arithmetic mean of a one-dimensional ndarray, ignoring NaN values and using Welford's algorithm.
• nanmidrangeBy( arrays, clbk[, thisArg ] ): calculate the mid-range of a one-dimensional ndarray via a callback function, ignoring NaN values.
• nanmidrange( arrays ): compute the mid-range of a one-dimensional ndarray, ignoring NaN values.
• nanminBy( arrays, clbk[, thisArg ] ): compute the minimum value of a one-dimensional ndarray via a callback function, ignoring NaN values.
• nanmin( arrays ): compute the minimum value of a one-dimensional ndarray, ignoring NaN values.
• nanminabs( arrays ): compute the minimum absolute value of a one-dimensional ndarray, ignoring NaN values.
• nanmskmax( arrays ): calculate the maximum value of a one-dimensional ndarray according to a mask, ignoring NaN values.
• nanmskmin( arrays ): calculate the minimum value of a one-dimensional ndarray according to a mask, ignoring NaN values.
• nanmskrange( arrays ): calculate the range of a one-dimensional ndarray according to a mask, ignoring NaN values.
• nanrangeBy( arrays, clbk[, thisArg ] ): calculate the range of a one-dimensional ndarray via a callback function, ignoring NaN values.
• nanrange( arrays ): compute the range of a one-dimensional ndarray, ignoring NaN values.
• rangeBy( arrays, clbk[, thisArg ] ): calculate the range of a one-dimensional ndarray via a callback function.
• range( arrays ): compute the range of a one-dimensional ndarray.
• rangeabs( arrays ): compute the range of absolute values 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.
• scumaxabs( arrays ): compute the cumulative maximum absolute 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.
• scuminabs( arrays ): compute the cumulative minimum absolute value of a one-dimensional single-precision floating-point ndarray.
• sdsmean( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using extended accumulation.
• sdsmeanors( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using ordinary recursive summation with extended accumulation.
• sdsnanmeanors( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray, ignoring NaN values and using ordinary recursive summation with extended accumulation.
• 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.
• smaxabssorted( arrays ): compute the maximum absolute value of a sorted 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.
• smeankbn( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using an improved Kahan–Babuška algorithm.
• smeankbn2( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using a second-order iterative Kahan–Babuška algorithm.
• smeanli( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using a one-pass trial mean algorithm.
• smeanlipw( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using a one-pass trial mean algorithm with pairwise summation.
• smeanors( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using ordinary recursive summation.
• smeanpn( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using a two-pass error correction algorithm.
• smeanpw( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using pairwise summation.
• smeanwd( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray using Welford's algorithm.
• smediansorted( arrays ): compute the median value of a sorted one-dimensional single-precision floating-point ndarray.
• smidrange( arrays ): compute the mid-range 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.
• sminsorted( arrays ): compute the minimum value of a sorted one-dimensional single-precision floating-point ndarray.
• smskmax( arrays ): calculate the maximum value of a one-dimensional single-precision floating-point ndarray according to a mask.
• smskmaxabs( arrays ): calculate the maximum absolute value of a one-dimensional single-precision floating-point ndarray according to a mask.
• smskmidrange( arrays ): calculate the mid-range of a one-dimensional single-precision floating-point ndarray according to a mask.
• smskmin( arrays ): calculate the minimum value of a one-dimensional single-precision floating-point ndarray according to a mask.
• smskrange( arrays ): calculate the range of a one-dimensional single-precision floating-point ndarray according to a mask.
• snanmax( arrays ): compute the maximum value of a one-dimensional single-precision floating-point ndarray, ignoring NaN values.
• snanmaxabs( arrays ): compute the maximum absolute value of a one-dimensional single-precision floating-point ndarray, ignoring NaN values.
• snanmean( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray, ignoring NaN values.
• snanmeanors( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray, ignoring NaN values and using ordinary recursive summation.
• snanmeanpn( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray, ignoring NaN values and using a two-pass error correction algorithm.
• snanmeanwd( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray, ignoring NaN values and using Welford's algorithm.
• snanmidrange( arrays ): compute the mid-range of a one-dimensional single-precision floating-point ndarray, ignoring NaN values.
• snanmin( arrays ): compute the minimum value of a one-dimensional single-precision floating-point ndarray, ignoring NaN values.
• snanminabs( arrays ): compute the minimum absolute value of a one-dimensional single-precision floating-point ndarray, ignoring NaN values.
• snanmskmax( arrays ): calculate the maximum value of a one-dimensional single-precision floating-point ndarray according to a mask, ignoring NaN values.
• snanmskmin( arrays ): calculate the minimum value of a one-dimensional single-precision floating-point ndarray according to a mask, ignoring NaN values.
• snanmskrange( arrays ): calculate the range of a one-dimensional single-precision floating-point ndarray according to a mask, ignoring NaN values.
• snanrange( arrays ): compute the range of a one-dimensional single-precision floating-point ndarray, ignoring NaN values.
• srange( arrays ): compute the range of a one-dimensional single-precision floating-point ndarray.
• srangeabs( arrays ): compute the range of absolute values of a one-dimensional single-precision floating-point ndarray.
• sstdev( arrays ): calculate the standard deviation of a one-dimensional single-precision floating-point ndarray.
• sstdevch( arrays ): calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using a one-pass trial mean algorithm.
• sstdevpn( arrays ): calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using a two-pass algorithm.
• sstdevtk( arrays ): calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using a one-pass textbook algorithm.
• sstdevwd( arrays ): calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using Welford's algorithm.
• sstdevyc( arrays ): calculate the standard deviation of a one-dimensional single-precision floating-point ndarray using a one-pass algorithm proposed by Youngs and Cramer.
• stdev( arrays ): calculate the standard deviation of a one-dimensional ndarray.
• stdevch( arrays ): calculate the standard deviation of a one-dimensional ndarray using a one-pass trial mean algorithm.
• stdevpn( arrays ): calculate the standard deviation of a one-dimensional ndarray using a two-pass algorithm.
• stdevtk( arrays ): calculate the standard deviation of a one-dimensional ndarray using a one-pass textbook algorithm.
• stdevwd( arrays ): calculate the standard deviation of a one-dimensional ndarray using Welford's algorithm.
• stdevyc( arrays ): calculate the standard deviation of a one-dimensional ndarray using a one-pass algorithm proposed by Youngs and Cramer.
• 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.
• variance( arrays ): calculate the variance of a one-dimensional ndarray.
• variancech( arrays ): calculate the variance of a one-dimensional ndarray using a one-pass trial mean algorithm.
• variancewd( arrays ): calculate the variance of a one-dimensional ndarray using Welford's algorithm.
• [ztest( arrays )][@stdlib/stats/base/ndarray/ztest]: compute a one-sample Z-test for a one-dimensional ndarray.
• [ztest2( arrays )][@stdlib/stats/base/ndarray/ztest2]: compute a two-sample Z-test for two one-dimensional ndarrays.

Examples

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

console.log( objectKeys( ns ) );

Notice

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.

Community

License

See LICENSE.

Copyright

Copyright © 2016-2026. The Stdlib Authors.