Base ndarray statistical functions.
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.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.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.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.dnanmax( arrays ): compute the maximum value of a one-dimensional double-precision floating-point ndarray, ignoringNaNvalues.dnanmaxabs( arrays ): compute the maximum absolute 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.dnanmeanors( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoringNaNvalues and using ordinary recursive summation.dnanmeanpn( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoringNaNvalues and using a two-pass error correction algorithm.dnanmeanpw( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoringNaNvalues and using pairwise summation.dnanmeanwd( arrays ): compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoringNaNvalues and using Welford's algorithm.dnanmin( arrays ): compute the minimum value of a one-dimensional double-precision floating-point ndarray, ignoringNaNvalues.dnanminabs( arrays ): compute the minimum absolute 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.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.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.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.nanmax( arrays ): compute the maximum value of a one-dimensional ndarray, ignoringNaNvalues.nanmaxabs( arrays ): compute the maximum absolute value of a one-dimensional ndarray, ignoringNaNvalues.nanmean( arrays ): compute the arithmetic mean of a one-dimensional ndarray, ignoringNaNvalues.nanmeanpn( arrays ): compute the arithmetic mean of a one-dimensional ndarray, ignoringNaNvalues and using a two-pass error correction algorithm.nanmeanwd( arrays ): compute the arithmetic mean of a one-dimensional ndarray, ignoringNaNvalues and using Welford's algorithm.nanmin( arrays ): compute the minimum value of a one-dimensional ndarray, ignoringNaNvalues.nanminabs( arrays ): compute the minimum absolute value of a one-dimensional ndarray, ignoringNaNvalues.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.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.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.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.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.snanmax( arrays ): compute the maximum value of a one-dimensional single-precision floating-point ndarray, ignoringNaNvalues.snanmaxabs( arrays ): compute the maximum absolute 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.snanmeanors( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray, ignoringNaNvalues and using ordinary recursive summation.snanmeanpn( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray, ignoringNaNvalues and using a two-pass error correction algorithm.snanmeanwd( arrays ): compute the arithmetic mean of a one-dimensional single-precision floating-point ndarray, ignoringNaNvalues and using Welford's algorithm.snanmin( arrays ): compute the minimum value of a one-dimensional single-precision floating-point ndarray, ignoringNaNvalues.snanminabs( arrays ): compute the minimum absolute 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 ) );