- [`dists`][@stdlib/stats/base/dists]: standard library probability distribution modules.

- [`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. - [`dcumax( N, x, strideX, y, strideY )`][@stdlib/stats/base/dcumax]: calculate the cumulative maximum of double-precision floating-point strided array elements. - [`dcumaxabs( N, x, strideX, y, strideY )`][@stdlib/stats/base/dcumaxabs]: calculate the cumulative maximum absolute value of double-precision floating-point strided array elements. - [`dcumin( N, x, strideX, y, strideY )`][@stdlib/stats/base/dcumin]: calculate the cumulative minimum of double-precision floating-point strided array elements. - [`dcuminabs( N, x, strideX, y, strideY )`][@stdlib/stats/base/dcuminabs]: calculate the cumulative minimum absolute value of double-precision floating-point strided array elements. - [`dmax( N, x, stride )`][@stdlib/stats/base/dmax]: calculate the maximum value of a double-precision floating-point strided array. - [`dmaxabs( N, x, stride )`][@stdlib/stats/base/dmaxabs]: calculate the maximum absolute value of a double-precision floating-point strided array. - [`dmaxabssorted( N, x, stride )`][@stdlib/stats/base/dmaxabssorted]: calculate the maximum absolute value of a sorted double-precision floating-point strided array. - [`dmaxsorted( N, x, stride )`][@stdlib/stats/base/dmaxsorted]: calculate the maximum value of a sorted double-precision floating-point strided array. - [`dmean( N, x, stride )`][@stdlib/stats/base/dmean]: calculate the arithmetic mean of a double-precision floating-point strided array. - [`dmeankbn( N, x, stride )`][@stdlib/stats/base/dmeankbn]: calculate the arithmetic mean of a double-precision floating-point strided array using an improved Kahan–Babuška algorithm. - [`dmeankbn2( N, x, stride )`][@stdlib/stats/base/dmeankbn2]: calculate the arithmetic mean of a double-precision floating-point strided array using a second-order iterative Kahan–Babuška algorithm. - [`dmeanli( N, x, stride )`][@stdlib/stats/base/dmeanli]: calculate the arithmetic mean of a double-precision floating-point strided array using a one-pass trial mean algorithm. - [`dmeanlipw( N, x, stride )`][@stdlib/stats/base/dmeanlipw]: calculate the arithmetic mean of a double-precision floating-point strided array using a one-pass trial mean algorithm with pairwise summation. - [`dmeanors( N, x, stride )`][@stdlib/stats/base/dmeanors]: calculate the arithmetic mean of a double-precision floating-point strided array using ordinary recursive summation. - [`dmeanpn( N, x, stride )`][@stdlib/stats/base/dmeanpn]: calculate the arithmetic mean of a double-precision floating-point strided array using a two-pass error correction algorithm. - [`dmeanpw( N, x, stride )`][@stdlib/stats/base/dmeanpw]: calculate the arithmetic mean of a double-precision floating-point strided array using pairwise summation. - [`dmeanstdev( N, correction, x, strideX, out, strideOut )`][@stdlib/stats/base/dmeanstdev]: calculate the mean and standard deviation of a double-precision floating-point strided array. - [`dmeanstdevpn( N, correction, x, strideX, out, strideOut )`][@stdlib/stats/base/dmeanstdevpn]: calculate the mean and standard deviation of a double-precision floating-point strided array using a two-pass algorithm. - [`dmeanvar( N, correction, x, strideX, out, strideOut )`][@stdlib/stats/base/dmeanvar]: calculate the mean and variance of a double-precision floating-point strided array. - [`dmeanvarpn( N, correction, x, strideX, out, strideOut )`][@stdlib/stats/base/dmeanvarpn]: calculate the mean and variance of a double-precision floating-point strided array using a two-pass algorithm. - [`dmeanwd( N, x, stride )`][@stdlib/stats/base/dmeanwd]: calculate the arithmetic mean of a double-precision floating-point strided array using Welford's algorithm. - [`dmediansorted( N, x, stride )`][@stdlib/stats/base/dmediansorted]: calculate the median value of a sorted double-precision floating-point strided array. - [`dmidrange( N, x, stride )`][@stdlib/stats/base/dmidrange]: calculate the mid-range of a double-precision floating-point strided array. - [`dmin( N, x, stride )`][@stdlib/stats/base/dmin]: calculate the minimum value of a double-precision floating-point strided array. - [`dminabs( N, x, stride )`][@stdlib/stats/base/dminabs]: calculate the minimum absolute value of a double-precision floating-point strided array. - [`dminsorted( N, x, stride )`][@stdlib/stats/base/dminsorted]: calculate the minimum value of a sorted double-precision floating-point strided array. - [`dmskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/dmskmax]: calculate the maximum value of a double-precision floating-point strided array according to a mask. - [`dmskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/dmskmin]: calculate the minimum value of a double-precision floating-point strided array according to a mask. - [`dmskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/dmskrange]: calculate the range of a double-precision floating-point strided array according to a mask. - [`dnanmax( N, x, stride )`][@stdlib/stats/base/dnanmax]: calculate the maximum value of a double-precision floating-point strided array, ignoring `NaN` values. - [`dnanmaxabs( N, x, stride )`][@stdlib/stats/base/dnanmaxabs]: calculate the maximum absolute value of a double-precision floating-point strided array, ignoring `NaN` values. - [`dnanmean( N, x, stride )`][@stdlib/stats/base/dnanmean]: calculate the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values. - [`dnanmeanors( N, x, stride )`][@stdlib/stats/base/dnanmeanors]: calculate the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using ordinary recursive summation. - [`dnanmeanpn( N, x, stride )`][@stdlib/stats/base/dnanmeanpn]: calculate the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using a two-pass error correction algorithm. - [`dnanmeanpw( N, x, stride )`][@stdlib/stats/base/dnanmeanpw]: calculate the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using pairwise summation. - [`dnanmeanwd( N, x, stride )`][@stdlib/stats/base/dnanmeanwd]: calculate the arithmetic mean of a double-precision floating-point strided array, using Welford's algorithm and ignoring `NaN` values. - [`dnanmin( N, x, stride )`][@stdlib/stats/base/dnanmin]: calculate the minimum value of a double-precision floating-point strided array, ignoring `NaN` values. - [`dnanminabs( N, x, stride )`][@stdlib/stats/base/dnanminabs]: calculate the minimum absolute value of a double-precision floating-point strided array, ignoring `NaN` values. - [`dnanmskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/dnanmskmax]: calculate the maximum value of a double-precision floating-point strided array according to a mask, ignoring `NaN` values. - [`dnanmskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/dnanmskmin]: calculate the minimum value of a double-precision floating-point strided array according to a mask, ignoring `NaN` values. - [`dnanmskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/dnanmskrange]: calculate the range of a double-precision floating-point strided array according to a mask, ignoring `NaN` values. - [`dnanrange( N, x, stride )`][@stdlib/stats/base/dnanrange]: calculate the range of a double-precision floating-point strided array, ignoring `NaN` values. - [`dnanstdev( N, correction, x, stride )`][@stdlib/stats/base/dnanstdev]: calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values. - [`dnanstdevch( N, correction, x, stride )`][@stdlib/stats/base/dnanstdevch]: calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass trial mean algorithm. - [`dnanstdevpn( N, correction, x, stride )`][@stdlib/stats/base/dnanstdevpn]: calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values and using a two-pass algorithm. - [`dnanstdevtk( N, correction, x, stride )`][@stdlib/stats/base/dnanstdevtk]: calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm. - [`dnanstdevwd( N, correction, x, stride )`][@stdlib/stats/base/dnanstdevwd]: calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values and using Welford's algorithm. - [`dnanstdevyc( N, correction, x, stride )`][@stdlib/stats/base/dnanstdevyc]: calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and Cramer. - [`dnanvariance( N, correction, x, stride )`][@stdlib/stats/base/dnanvariance]: calculate the variance of a double-precision floating-point strided array ignoring `NaN` values. - [`dnanvariancech( N, correction, x, stride )`][@stdlib/stats/base/dnanvariancech]: calculate the variance of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass trial mean algorithm. - [`dnanvariancepn( N, correction, x, stride )`][@stdlib/stats/base/dnanvariancepn]: calculate the variance of a double-precision floating-point strided array ignoring `NaN` values and using a two-pass algorithm. - [`dnanvariancetk( N, correction, x, stride )`][@stdlib/stats/base/dnanvariancetk]: calculate the variance of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm. - [`dnanvariancewd( N, correction, x, stride )`][@stdlib/stats/base/dnanvariancewd]: calculate the variance of a double-precision floating-point strided array ignoring `NaN` values and using Welford's algorithm. - [`dnanvarianceyc( N, correction, x, stride )`][@stdlib/stats/base/dnanvarianceyc]: calculate the variance of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and Cramer. - [`drange( N, x, stride )`][@stdlib/stats/base/drange]: calculate the range of a double-precision floating-point strided array. - [`dsem( N, correction, x, stride )`][@stdlib/stats/base/dsem]: calculate the standard error of the mean of a double-precision floating-point strided array. - [`dsemch( N, correction, x, stride )`][@stdlib/stats/base/dsemch]: calculate the standard error of the mean of a double-precision floating-point strided array using a one-pass trial mean algorithm. - [`dsempn( N, correction, x, stride )`][@stdlib/stats/base/dsempn]: calculate the standard error of the mean of a double-precision floating-point strided array using a two-pass algorithm. - [`dsemtk( N, correction, x, stride )`][@stdlib/stats/base/dsemtk]: calculate the standard error of the mean of a double-precision floating-point strided array using a one-pass textbook algorithm. - [`dsemwd( N, correction, x, stride )`][@stdlib/stats/base/dsemwd]: calculate the standard error of the mean of a double-precision floating-point strided array using Welford's algorithm. - [`dsemyc( N, correction, x, stride )`][@stdlib/stats/base/dsemyc]: calculate the standard error of the mean of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer. - [`dsmean( N, x, stride )`][@stdlib/stats/base/dsmean]: calculate the arithmetic mean of a single-precision floating-point strided array using extended accumulation and returning an extended precision result. - [`dsmeanors( N, x, stride )`][@stdlib/stats/base/dsmeanors]: calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation with extended accumulation and returning an extended precision result. - [`dsmeanpn( N, x, stride )`][@stdlib/stats/base/dsmeanpn]: calculate the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm with extended accumulation and returning an extended precision result. - [`dsmeanpw( N, x, stride )`][@stdlib/stats/base/dsmeanpw]: calculate the arithmetic mean of a single-precision floating-point strided array using pairwise summation with extended accumulation and returning an extended precision result. - [`dsmeanwd( N, x, stride )`][@stdlib/stats/base/dsmeanwd]: calculate the arithmetic mean of a single-precision floating-point strided array using Welford's algorithm with extended accumulation and returning an extended precision result. - [`dsnanmean( N, x, stride )`][@stdlib/stats/base/dsnanmean]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values, using extended accumulation, and returning an extended precision result. - [`dsnanmeanors( N, x, stride )`][@stdlib/stats/base/dsnanmeanors]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values, using ordinary recursive summation with extended accumulation, and returning an extended precision result. - [`dsnanmeanpn( N, x, stride )`][@stdlib/stats/base/dsnanmeanpn]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values, using a two-pass error correction algorithm with extended accumulation, and returning an extended precision result. - [`dsnanmeanwd( N, x, stride )`][@stdlib/stats/base/dsnanmeanwd]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values, using Welford's algorithm with extended accumulation, and returning an extended precision result. - [`dstdev( N, correction, x, stride )`][@stdlib/stats/base/dstdev]: calculate the standard deviation of a double-precision floating-point strided array. - [`dstdevch( N, correction, x, stride )`][@stdlib/stats/base/dstdevch]: calculate the standard deviation of a double-precision floating-point strided array using a one-pass trial mean algorithm. - [`dstdevpn( N, correction, x, stride )`][@stdlib/stats/base/dstdevpn]: calculate the standard deviation of a double-precision floating-point strided array using a two-pass algorithm. - [`dstdevtk( N, correction, x, stride )`][@stdlib/stats/base/dstdevtk]: calculate the standard deviation of a double-precision floating-point strided array using a one-pass textbook algorithm. - [`dstdevwd( N, correction, x, stride )`][@stdlib/stats/base/dstdevwd]: calculate the standard deviation of a double-precision floating-point strided array using Welford's algorithm. - [`dstdevyc( N, correction, x, stride )`][@stdlib/stats/base/dstdevyc]: calculate the standard deviation of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer. - [`dsvariance( N, correction, x, stride )`][@stdlib/stats/base/dsvariance]: calculate the variance of a single-precision floating-point strided array using extended accumulation and returning an extended precision result. - [`dsvariancepn( N, correction, x, stride )`][@stdlib/stats/base/dsvariancepn]: calculate the variance of a single-precision floating-point strided array using a two-pass algorithm with extended accumulation and returning an extended precision result. - [`dvariance( N, correction, x, stride )`][@stdlib/stats/base/dvariance]: calculate the variance of a double-precision floating-point strided array. - [`dvariancech( N, correction, x, stride )`][@stdlib/stats/base/dvariancech]: calculate the variance of a double-precision floating-point strided array using a one-pass trial mean algorithm. - [`dvariancepn( N, correction, x, stride )`][@stdlib/stats/base/dvariancepn]: calculate the variance of a double-precision floating-point strided array using a two-pass algorithm. - [`dvariancetk( N, correction, x, stride )`][@stdlib/stats/base/dvariancetk]: calculate the variance of a double-precision floating-point strided array using a one-pass textbook algorithm. - [`dvariancewd( N, correction, x, stride )`][@stdlib/stats/base/dvariancewd]: calculate the variance of a double-precision floating-point strided array using Welford's algorithm. - [`dvarianceyc( N, correction, x, stride )`][@stdlib/stats/base/dvarianceyc]: calculate the variance of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer. - [`dvarm( N, mean, correction, x, stride )`][@stdlib/stats/base/dvarm]: calculate the variance of a double-precision floating-point strided array provided a known mean. - [`dvarmpn( N, mean, correction, x, stride )`][@stdlib/stats/base/dvarmpn]: calculate the variance of a double-precision floating-point strided array provided a known mean and using Neely's correction algorithm. - [`dvarmtk( N, mean, correction, x, stride )`][@stdlib/stats/base/dvarmtk]: calculate the variance of a double-precision floating-point strided array provided a known mean and using a one-pass textbook algorithm. - [`maxBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/base/max-by]: calculate the maximum value of a strided array via a callback function. - [`max( N, x, stride )`][@stdlib/stats/base/max]: calculate the maximum value of a strided array. - [`maxabs( N, x, stride )`][@stdlib/stats/base/maxabs]: calculate the maximum absolute value of a strided array. - [`maxsorted( N, x, stride )`][@stdlib/stats/base/maxsorted]: calculate the maximum value of a sorted strided array. - [`mean( N, x, stride )`][@stdlib/stats/base/mean]: calculate the arithmetic mean of a strided array. - [`meankbn( N, x, stride )`][@stdlib/stats/base/meankbn]: calculate the arithmetic mean of a strided array using an improved Kahan–Babuška algorithm. - [`meankbn2( N, x, stride )`][@stdlib/stats/base/meankbn2]: calculate the arithmetic mean of a strided array using a second-order iterative Kahan–Babuška algorithm. - [`meanors( N, x, stride )`][@stdlib/stats/base/meanors]: calculate the arithmetic mean of a strided array using ordinary recursive summation. - [`meanpn( N, x, stride )`][@stdlib/stats/base/meanpn]: calculate the arithmetic mean of a strided array using a two-pass error correction algorithm. - [`meanpw( N, x, stride )`][@stdlib/stats/base/meanpw]: calculate the arithmetic mean of a strided array using pairwise summation. - [`meanwd( N, x, stride )`][@stdlib/stats/base/meanwd]: calculate the arithmetic mean of a strided array using Welford's algorithm. - [`mediansorted( N, x, stride )`][@stdlib/stats/base/mediansorted]: calculate the median value of a sorted strided array. - [`minBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/base/min-by]: calculate the minimum value of a strided array via a callback function. - [`min( N, x, stride )`][@stdlib/stats/base/min]: calculate the minimum value of a strided array. - [`minabs( N, x, stride )`][@stdlib/stats/base/minabs]: calculate the minimum absolute value of a strided array. - [`minsorted( N, x, stride )`][@stdlib/stats/base/minsorted]: calculate the minimum value of a sorted strided array. - [`mskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/mskmax]: calculate the maximum value of a strided array according to a mask. - [`mskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/mskmin]: calculate the minimum value of a strided array according to a mask. - [`mskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/mskrange]: calculate the range of a strided array according to a mask. - [`nanmaxBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/base/nanmax-by]: calculate the maximum value of a strided array via a callback function, ignoring `NaN` values. - [`nanmax( N, x, stride )`][@stdlib/stats/base/nanmax]: calculate the maximum value of a strided array, ignoring `NaN` values. - [`nanmaxabs( N, x, stride )`][@stdlib/stats/base/nanmaxabs]: calculate the maximum absolute value of a strided array, ignoring `NaN` values. - [`nanmean( N, x, stride )`][@stdlib/stats/base/nanmean]: calculate the arithmetic mean of a strided array, ignoring `NaN` values. - [`nanmeanors( N, x, stride )`][@stdlib/stats/base/nanmeanors]: calculate the arithmetic mean of a strided array, ignoring `NaN` values and using ordinary recursive summation. - [`nanmeanpn( N, x, stride )`][@stdlib/stats/base/nanmeanpn]: calculate the arithmetic mean of a strided array, ignoring `NaN` values and using a two-pass error correction algorithm. - [`nanmeanwd( N, x, stride )`][@stdlib/stats/base/nanmeanwd]: calculate the arithmetic mean of a strided array, ignoring `NaN` values and using Welford's algorithm. - [`nanminBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/base/nanmin-by]: calculate the minimum value of a strided array via a callback function, ignoring `NaN` values. - [`nanmin( N, x, stride )`][@stdlib/stats/base/nanmin]: calculate the minimum value of a strided array, ignoring `NaN` values. - [`nanminabs( N, x, stride )`][@stdlib/stats/base/nanminabs]: calculate the minimum absolute value of a strided array, ignoring `NaN` values. - [`nanmskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/nanmskmax]: calculate the maximum value of a strided array according to a mask, ignoring `NaN` values. - [`nanmskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/nanmskmin]: calculate the minimum value of a strided array according to a mask, ignoring `NaN` values. - [`nanmskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/nanmskrange]: calculate the range of a strided array according to a mask, ignoring `NaN` values. - [`nanrangeBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/base/nanrange-by]: calculate the range of a strided array via a callback function, ignoring `NaN` values. - [`nanrange( N, x, stride )`][@stdlib/stats/base/nanrange]: calculate the range of a strided array, ignoring `NaN` values. - [`nanstdev( N, correction, x, stride )`][@stdlib/stats/base/nanstdev]: calculate the standard deviation of a strided array ignoring `NaN` values. - [`nanstdevch( N, correction, x, stride )`][@stdlib/stats/base/nanstdevch]: calculate the standard deviation of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm. - [`nanstdevpn( N, correction, x, stride )`][@stdlib/stats/base/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/base/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/base/nanstdevwd]: calculate the standard deviation of a strided array ignoring `NaN` values and using Welford's algorithm. - [`nanstdevyc( N, correction, x, stride )`][@stdlib/stats/base/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, stride )`][@stdlib/stats/base/nanvariance]: calculate the variance of a strided array ignoring `NaN` values. - [`nanvariancech( N, correction, x, stride )`][@stdlib/stats/base/nanvariancech]: calculate the variance of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm. - [`nanvariancepn( N, correction, x, stride )`][@stdlib/stats/base/nanvariancepn]: calculate the variance of a strided array ignoring `NaN` values and using a two-pass algorithm. - [`nanvariancetk( N, correction, x, stride )`][@stdlib/stats/base/nanvariancetk]: calculate the variance of a strided array ignoring `NaN` values and using a one-pass textbook algorithm. - [`nanvariancewd( N, correction, x, stride )`][@stdlib/stats/base/nanvariancewd]: calculate the variance of a strided array ignoring `NaN` values and using Welford's algorithm. - [`nanvarianceyc( N, correction, x, stride )`][@stdlib/stats/base/nanvarianceyc]: calculate the variance of a strided array ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and Cramer. - [`rangeBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/base/range-by]: calculate the range of a strided array via a callback function. - [`range( N, x, stride )`][@stdlib/stats/base/range]: calculate the range of a strided array. - [`scumax( N, x, strideX, y, strideY )`][@stdlib/stats/base/scumax]: calculate the cumulative maximum of single-precision floating-point strided array elements. - [`scumaxabs( N, x, strideX, y, strideY )`][@stdlib/stats/base/scumaxabs]: calculate the cumulative maximum absolute value of single-precision floating-point strided array elements. - [`scumin( N, x, strideX, y, strideY )`][@stdlib/stats/base/scumin]: calculate the cumulative minimum of single-precision floating-point strided array elements. - [`scuminabs( N, x, strideX, y, strideY )`][@stdlib/stats/base/scuminabs]: calculate the cumulative minimum absolute value of single-precision floating-point strided array elements. - [`sdsmean( N, x, stride )`][@stdlib/stats/base/sdsmean]: calculate the arithmetic mean of a single-precision floating-point strided array using extended accumulation. - [`sdsmeanors( N, x, stride )`][@stdlib/stats/base/sdsmeanors]: calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation with extended accumulation. - [`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. - [`smax( N, x, stride )`][@stdlib/stats/base/smax]: calculate the maximum value of a single-precision floating-point strided array. - [`smaxabs( N, x, stride )`][@stdlib/stats/base/smaxabs]: calculate the maximum absolute value of a single-precision floating-point strided array. - [`smaxabssorted( N, x, stride )`][@stdlib/stats/base/smaxabssorted]: calculate the maximum absolute value of a sorted single-precision floating-point strided array. - [`smaxsorted( N, x, stride )`][@stdlib/stats/base/smaxsorted]: calculate the maximum value of a sorted single-precision floating-point strided array. - [`smean( N, x, stride )`][@stdlib/stats/base/smean]: calculate the arithmetic mean of a single-precision floating-point strided array. - [`smeankbn( N, x, stride )`][@stdlib/stats/base/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/base/smeankbn2]: calculate the arithmetic mean of a single-precision floating-point strided array using a second-order iterative Kahan–Babuška algorithm. - [`smeanli( N, x, stride )`][@stdlib/stats/base/smeanli]: calculate the arithmetic mean of a single-precision floating-point strided array using a one-pass trial mean algorithm. - [`smeanlipw( N, x, stride )`][@stdlib/stats/base/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/base/smeanors]: calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation. - [`smeanpn( N, x, stride )`][@stdlib/stats/base/smeanpn]: calculate the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm. - [`smeanpw( N, x, stride )`][@stdlib/stats/base/smeanpw]: calculate the arithmetic mean of a single-precision floating-point strided array using pairwise summation. - [`smeanwd( N, x, stride )`][@stdlib/stats/base/smeanwd]: calculate the arithmetic mean of a single-precision floating-point strided array using Welford's algorithm. - [`smediansorted( N, x, stride )`][@stdlib/stats/base/smediansorted]: calculate the median value of a sorted single-precision floating-point strided array. - [`smidrange( N, x, stride )`][@stdlib/stats/base/smidrange]: calculate the mid-range of a single-precision floating-point strided array. - [`smin( N, x, stride )`][@stdlib/stats/base/smin]: calculate the minimum value of a single-precision floating-point strided array. - [`sminabs( N, x, stride )`][@stdlib/stats/base/sminabs]: calculate the minimum absolute value of a single-precision floating-point strided array. - [`sminsorted( N, x, stride )`][@stdlib/stats/base/sminsorted]: calculate the minimum value of a sorted single-precision floating-point strided array. - [`smskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/smskmax]: calculate the maximum value of a single-precision floating-point strided array according to a mask. - [`smskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/smskmin]: calculate the minimum value of a single-precision floating-point strided array according to a mask. - [`smskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/smskrange]: calculate the range of a single-precision floating-point strided array according to a mask. - [`snanmax( N, x, stride )`][@stdlib/stats/base/snanmax]: calculate the maximum value of a single-precision floating-point strided array, ignoring `NaN` values. - [`snanmaxabs( N, x, stride )`][@stdlib/stats/base/snanmaxabs]: calculate the maximum absolute value of a single-precision floating-point strided array, ignoring `NaN` values. - [`snanmean( N, x, stride )`][@stdlib/stats/base/snanmean]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values. - [`snanmeanors( N, x, stride )`][@stdlib/stats/base/snanmeanors]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using ordinary recursive summation. - [`snanmeanpn( N, x, stride )`][@stdlib/stats/base/snanmeanpn]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using a two-pass error correction algorithm. - [`snanmeanwd( N, x, stride )`][@stdlib/stats/base/snanmeanwd]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using Welford's algorithm. - [`snanmin( N, x, stride )`][@stdlib/stats/base/snanmin]: calculate the minimum value of a single-precision floating-point strided array, ignoring `NaN` values. - [`snanminabs( N, x, stride )`][@stdlib/stats/base/snanminabs]: calculate the minimum absolute value of a single-precision floating-point strided array, ignoring `NaN` values. - [`snanmskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/snanmskmax]: calculate the maximum value of a single-precision floating-point strided array according to a mask, ignoring `NaN` values. - [`snanmskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/snanmskmin]: calculate the minimum value of a single-precision floating-point strided array according to a mask, ignoring `NaN` values. - [`snanmskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/snanmskrange]: calculate the range of a single-precision floating-point strided array according to a mask, ignoring `NaN` values. - [`snanrange( N, x, stride )`][@stdlib/stats/base/snanrange]: calculate the range 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. - [`srange( N, x, stride )`][@stdlib/stats/base/srange]: calculate the range of a single-precision floating-point strided array. - [`sstdev( N, correction, x, stride )`][@stdlib/stats/base/sstdev]: calculate the standard deviation of a single-precision floating-point strided array. - [`sstdevch( N, correction, x, stride )`][@stdlib/stats/base/sstdevch]: calculate the standard deviation of a single-precision floating-point strided array using a one-pass trial mean algorithm. - [`sstdevpn( N, correction, x, stride )`][@stdlib/stats/base/sstdevpn]: calculate the standard deviation of a single-precision floating-point strided array using a two-pass algorithm. - [`sstdevtk( N, correction, x, stride )`][@stdlib/stats/base/sstdevtk]: calculate the standard deviation of a single-precision floating-point strided array using a one-pass textbook algorithm. - [`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. - [`sstdevyc( N, correction, x, stride )`][@stdlib/stats/base/sstdevyc]: calculate the standard deviation of a single-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer. - [`stdev( N, correction, x, stride )`][@stdlib/stats/base/stdev]: calculate the standard deviation of a strided array. - [`stdevch( N, correction, x, stride )`][@stdlib/stats/base/stdevch]: calculate the standard deviation of a strided array using a one-pass trial mean algorithm. - [`stdevpn( N, correction, x, stride )`][@stdlib/stats/base/stdevpn]: calculate the standard deviation of a strided array using a two-pass algorithm. - [`stdevtk( N, correction, x, stride )`][@stdlib/stats/base/stdevtk]: calculate the standard deviation of a strided array using a one-pass textbook algorithm. - [`stdevwd( N, correction, x, stride )`][@stdlib/stats/base/stdevwd]: calculate the standard deviation of a strided array using Welford's algorithm. - [`stdevyc( N, correction, x, stride )`][@stdlib/stats/base/stdevyc]: calculate the standard deviation of a strided array using a one-pass algorithm proposed by Youngs and Cramer. - [`svariance( N, correction, x, stride )`][@stdlib/stats/base/svariance]: calculate the variance of a single-precision floating-point strided array. - [`svariancech( N, correction, x, stride )`][@stdlib/stats/base/svariancech]: calculate the variance of a single-precision floating-point strided array using a one-pass trial mean algorithm. - [`svariancepn( N, correction, x, stride )`][@stdlib/stats/base/svariancepn]: calculate the variance of a single-precision floating-point strided array using a two-pass algorithm. - [`svariancetk( N, correction, x, stride )`][@stdlib/stats/base/svariancetk]: calculate the variance of a single-precision floating-point strided array using a one-pass textbook algorithm. - [`svariancewd( N, correction, x, stride )`][@stdlib/stats/base/svariancewd]: calculate the variance of a single-precision floating-point strided array using Welford's algorithm. - [`svarianceyc( N, correction, x, stride )`][@stdlib/stats/base/svarianceyc]: calculate the variance of a single-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer. - [`variance( N, correction, x, stride )`][@stdlib/stats/base/variance]: calculate the variance of a strided array. - [`variancech( N, correction, x, stride )`][@stdlib/stats/base/variancech]: calculate the variance of a strided array using a one-pass trial mean algorithm. - [`variancepn( N, correction, x, stride )`][@stdlib/stats/base/variancepn]: calculate the variance of a strided array using a two-pass algorithm. - [`variancetk( N, correction, x, stride )`][@stdlib/stats/base/variancetk]: calculate the variance of a strided array using a one-pass textbook algorithm. - [`variancewd( N, correction, x, stride )`][@stdlib/stats/base/variancewd]: calculate the variance of a strided array using Welford's algorithm. - [`varianceyc( N, correction, x, stride )`][@stdlib/stats/base/varianceyc]: calculate the variance of a strided array using a one-pass algorithm proposed by Youngs and Cramer.