README.md (83840B)
1 <!-- 2 3 @license Apache-2.0 4 5 Copyright (c) 2018 The Stdlib Authors. 6 7 Licensed under the Apache License, Version 2.0 (the "License"); 8 you may not use this file except in compliance with the License. 9 You may obtain a copy of the License at 10 11 http://www.apache.org/licenses/LICENSE-2.0 12 13 Unless required by applicable law or agreed to in writing, software 14 distributed under the License is distributed on an "AS IS" BASIS, 15 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 16 See the License for the specific language governing permissions and 17 limitations under the License. 18 19 --> 20 21 # Base Statistics 22 23 > Standard library base statistical functions. 24 25 <section class="usage"> 26 27 ## Usage 28 29 ```javascript 30 var stats = require( '@stdlib/stats/base' ); 31 ``` 32 33 #### stats 34 35 Standard library base statistical functions. 36 37 ```javascript 38 var ns = stats; 39 // returns {...} 40 ``` 41 42 The namespace contains the following sub-namespaces: 43 44 <!-- <toc pattern="dists"> --> 45 46 <div class="namespace-toc"> 47 48 - <span class="signature">[`dists`][@stdlib/stats/base/dists]</span><span class="delimiter">: </span><span class="description">standard library probability distribution modules.</span> 49 50 </div> 51 52 <!-- </toc> --> 53 54 The namespace contains the following statistical functions: 55 56 <!-- <toc pattern="*"> --> 57 58 <div class="namespace-toc"> 59 60 - <span class="signature">[`cumax( N, x, strideX, y, strideY )`][@stdlib/stats/base/cumax]</span><span class="delimiter">: </span><span class="description">calculate the cumulative maximum of a strided array.</span> 61 - <span class="signature">[`cumaxabs( N, x, strideX, y, strideY )`][@stdlib/stats/base/cumaxabs]</span><span class="delimiter">: </span><span class="description">calculate the cumulative maximum absolute value of a strided array.</span> 62 - <span class="signature">[`cumin( N, x, strideX, y, strideY )`][@stdlib/stats/base/cumin]</span><span class="delimiter">: </span><span class="description">calculate the cumulative minimum of a strided array.</span> 63 - <span class="signature">[`cuminabs( N, x, strideX, y, strideY )`][@stdlib/stats/base/cuminabs]</span><span class="delimiter">: </span><span class="description">calculate the cumulative minimum absolute value of a strided array.</span> 64 - <span class="signature">[`dcumax( N, x, strideX, y, strideY )`][@stdlib/stats/base/dcumax]</span><span class="delimiter">: </span><span class="description">calculate the cumulative maximum of double-precision floating-point strided array elements.</span> 65 - <span class="signature">[`dcumaxabs( N, x, strideX, y, strideY )`][@stdlib/stats/base/dcumaxabs]</span><span class="delimiter">: </span><span class="description">calculate the cumulative maximum absolute value of double-precision floating-point strided array elements.</span> 66 - <span class="signature">[`dcumin( N, x, strideX, y, strideY )`][@stdlib/stats/base/dcumin]</span><span class="delimiter">: </span><span class="description">calculate the cumulative minimum of double-precision floating-point strided array elements.</span> 67 - <span class="signature">[`dcuminabs( N, x, strideX, y, strideY )`][@stdlib/stats/base/dcuminabs]</span><span class="delimiter">: </span><span class="description">calculate the cumulative minimum absolute value of double-precision floating-point strided array elements.</span> 68 - <span class="signature">[`dmax( N, x, stride )`][@stdlib/stats/base/dmax]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a double-precision floating-point strided array.</span> 69 - <span class="signature">[`dmaxabs( N, x, stride )`][@stdlib/stats/base/dmaxabs]</span><span class="delimiter">: </span><span class="description">calculate the maximum absolute value of a double-precision floating-point strided array.</span> 70 - <span class="signature">[`dmaxabssorted( N, x, stride )`][@stdlib/stats/base/dmaxabssorted]</span><span class="delimiter">: </span><span class="description">calculate the maximum absolute value of a sorted double-precision floating-point strided array.</span> 71 - <span class="signature">[`dmaxsorted( N, x, stride )`][@stdlib/stats/base/dmaxsorted]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a sorted double-precision floating-point strided array.</span> 72 - <span class="signature">[`dmean( N, x, stride )`][@stdlib/stats/base/dmean]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a double-precision floating-point strided array.</span> 73 - <span class="signature">[`dmeankbn( N, x, stride )`][@stdlib/stats/base/dmeankbn]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a double-precision floating-point strided array using an improved Kahan–Babuška algorithm.</span> 74 - <span class="signature">[`dmeankbn2( N, x, stride )`][@stdlib/stats/base/dmeankbn2]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a double-precision floating-point strided array using a second-order iterative Kahan–Babuška algorithm.</span> 75 - <span class="signature">[`dmeanli( N, x, stride )`][@stdlib/stats/base/dmeanli]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a double-precision floating-point strided array using a one-pass trial mean algorithm.</span> 76 - <span class="signature">[`dmeanlipw( N, x, stride )`][@stdlib/stats/base/dmeanlipw]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a double-precision floating-point strided array using a one-pass trial mean algorithm with pairwise summation.</span> 77 - <span class="signature">[`dmeanors( N, x, stride )`][@stdlib/stats/base/dmeanors]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a double-precision floating-point strided array using ordinary recursive summation.</span> 78 - <span class="signature">[`dmeanpn( N, x, stride )`][@stdlib/stats/base/dmeanpn]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a double-precision floating-point strided array using a two-pass error correction algorithm.</span> 79 - <span class="signature">[`dmeanpw( N, x, stride )`][@stdlib/stats/base/dmeanpw]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a double-precision floating-point strided array using pairwise summation.</span> 80 - <span class="signature">[`dmeanstdev( N, correction, x, strideX, out, strideOut )`][@stdlib/stats/base/dmeanstdev]</span><span class="delimiter">: </span><span class="description">calculate the mean and standard deviation of a double-precision floating-point strided array.</span> 81 - <span class="signature">[`dmeanstdevpn( N, correction, x, strideX, out, strideOut )`][@stdlib/stats/base/dmeanstdevpn]</span><span class="delimiter">: </span><span class="description">calculate the mean and standard deviation of a double-precision floating-point strided array using a two-pass algorithm.</span> 82 - <span class="signature">[`dmeanvar( N, correction, x, strideX, out, strideOut )`][@stdlib/stats/base/dmeanvar]</span><span class="delimiter">: </span><span class="description">calculate the mean and variance of a double-precision floating-point strided array.</span> 83 - <span class="signature">[`dmeanvarpn( N, correction, x, strideX, out, strideOut )`][@stdlib/stats/base/dmeanvarpn]</span><span class="delimiter">: </span><span class="description">calculate the mean and variance of a double-precision floating-point strided array using a two-pass algorithm.</span> 84 - <span class="signature">[`dmeanwd( N, x, stride )`][@stdlib/stats/base/dmeanwd]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a double-precision floating-point strided array using Welford's algorithm.</span> 85 - <span class="signature">[`dmediansorted( N, x, stride )`][@stdlib/stats/base/dmediansorted]</span><span class="delimiter">: </span><span class="description">calculate the median value of a sorted double-precision floating-point strided array.</span> 86 - <span class="signature">[`dmidrange( N, x, stride )`][@stdlib/stats/base/dmidrange]</span><span class="delimiter">: </span><span class="description">calculate the mid-range of a double-precision floating-point strided array.</span> 87 - <span class="signature">[`dmin( N, x, stride )`][@stdlib/stats/base/dmin]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a double-precision floating-point strided array.</span> 88 - <span class="signature">[`dminabs( N, x, stride )`][@stdlib/stats/base/dminabs]</span><span class="delimiter">: </span><span class="description">calculate the minimum absolute value of a double-precision floating-point strided array.</span> 89 - <span class="signature">[`dminsorted( N, x, stride )`][@stdlib/stats/base/dminsorted]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a sorted double-precision floating-point strided array.</span> 90 - <span class="signature">[`dmskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/dmskmax]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a double-precision floating-point strided array according to a mask.</span> 91 - <span class="signature">[`dmskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/dmskmin]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a double-precision floating-point strided array according to a mask.</span> 92 - <span class="signature">[`dmskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/dmskrange]</span><span class="delimiter">: </span><span class="description">calculate the range of a double-precision floating-point strided array according to a mask.</span> 93 - <span class="signature">[`dnanmax( N, x, stride )`][@stdlib/stats/base/dnanmax]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a double-precision floating-point strided array, ignoring `NaN` values.</span> 94 - <span class="signature">[`dnanmaxabs( N, x, stride )`][@stdlib/stats/base/dnanmaxabs]</span><span class="delimiter">: </span><span class="description">calculate the maximum absolute value of a double-precision floating-point strided array, ignoring `NaN` values.</span> 95 - <span class="signature">[`dnanmean( N, x, stride )`][@stdlib/stats/base/dnanmean]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values.</span> 96 - <span class="signature">[`dnanmeanors( N, x, stride )`][@stdlib/stats/base/dnanmeanors]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using ordinary recursive summation.</span> 97 - <span class="signature">[`dnanmeanpn( N, x, stride )`][@stdlib/stats/base/dnanmeanpn]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using a two-pass error correction algorithm.</span> 98 - <span class="signature">[`dnanmeanpw( N, x, stride )`][@stdlib/stats/base/dnanmeanpw]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using pairwise summation.</span> 99 - <span class="signature">[`dnanmeanwd( N, x, stride )`][@stdlib/stats/base/dnanmeanwd]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a double-precision floating-point strided array, using Welford's algorithm and ignoring `NaN` values.</span> 100 - <span class="signature">[`dnanmin( N, x, stride )`][@stdlib/stats/base/dnanmin]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a double-precision floating-point strided array, ignoring `NaN` values.</span> 101 - <span class="signature">[`dnanminabs( N, x, stride )`][@stdlib/stats/base/dnanminabs]</span><span class="delimiter">: </span><span class="description">calculate the minimum absolute value of a double-precision floating-point strided array, ignoring `NaN` values.</span> 102 - <span class="signature">[`dnanmskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/dnanmskmax]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a double-precision floating-point strided array according to a mask, ignoring `NaN` values.</span> 103 - <span class="signature">[`dnanmskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/dnanmskmin]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a double-precision floating-point strided array according to a mask, ignoring `NaN` values.</span> 104 - <span class="signature">[`dnanmskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/dnanmskrange]</span><span class="delimiter">: </span><span class="description">calculate the range of a double-precision floating-point strided array according to a mask, ignoring `NaN` values.</span> 105 - <span class="signature">[`dnanrange( N, x, stride )`][@stdlib/stats/base/dnanrange]</span><span class="delimiter">: </span><span class="description">calculate the range of a double-precision floating-point strided array, ignoring `NaN` values.</span> 106 - <span class="signature">[`dnanstdev( N, correction, x, stride )`][@stdlib/stats/base/dnanstdev]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values.</span> 107 - <span class="signature">[`dnanstdevch( N, correction, x, stride )`][@stdlib/stats/base/dnanstdevch]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass trial mean algorithm.</span> 108 - <span class="signature">[`dnanstdevpn( N, correction, x, stride )`][@stdlib/stats/base/dnanstdevpn]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values and using a two-pass algorithm.</span> 109 - <span class="signature">[`dnanstdevtk( N, correction, x, stride )`][@stdlib/stats/base/dnanstdevtk]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm.</span> 110 - <span class="signature">[`dnanstdevwd( N, correction, x, stride )`][@stdlib/stats/base/dnanstdevwd]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a double-precision floating-point strided array ignoring `NaN` values and using Welford's algorithm.</span> 111 - <span class="signature">[`dnanstdevyc( N, correction, x, stride )`][@stdlib/stats/base/dnanstdevyc]</span><span class="delimiter">: </span><span class="description">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.</span> 112 - <span class="signature">[`dnanvariance( N, correction, x, stride )`][@stdlib/stats/base/dnanvariance]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array ignoring `NaN` values.</span> 113 - <span class="signature">[`dnanvariancech( N, correction, x, stride )`][@stdlib/stats/base/dnanvariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass trial mean algorithm.</span> 114 - <span class="signature">[`dnanvariancepn( N, correction, x, stride )`][@stdlib/stats/base/dnanvariancepn]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array ignoring `NaN` values and using a two-pass algorithm.</span> 115 - <span class="signature">[`dnanvariancetk( N, correction, x, stride )`][@stdlib/stats/base/dnanvariancetk]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm.</span> 116 - <span class="signature">[`dnanvariancewd( N, correction, x, stride )`][@stdlib/stats/base/dnanvariancewd]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array ignoring `NaN` values and using Welford's algorithm.</span> 117 - <span class="signature">[`dnanvarianceyc( N, correction, x, stride )`][@stdlib/stats/base/dnanvarianceyc]</span><span class="delimiter">: </span><span class="description">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.</span> 118 - <span class="signature">[`drange( N, x, stride )`][@stdlib/stats/base/drange]</span><span class="delimiter">: </span><span class="description">calculate the range of a double-precision floating-point strided array.</span> 119 - <span class="signature">[`dsem( N, correction, x, stride )`][@stdlib/stats/base/dsem]</span><span class="delimiter">: </span><span class="description">calculate the standard error of the mean of a double-precision floating-point strided array.</span> 120 - <span class="signature">[`dsemch( N, correction, x, stride )`][@stdlib/stats/base/dsemch]</span><span class="delimiter">: </span><span class="description">calculate the standard error of the mean of a double-precision floating-point strided array using a one-pass trial mean algorithm.</span> 121 - <span class="signature">[`dsempn( N, correction, x, stride )`][@stdlib/stats/base/dsempn]</span><span class="delimiter">: </span><span class="description">calculate the standard error of the mean of a double-precision floating-point strided array using a two-pass algorithm.</span> 122 - <span class="signature">[`dsemtk( N, correction, x, stride )`][@stdlib/stats/base/dsemtk]</span><span class="delimiter">: </span><span class="description">calculate the standard error of the mean of a double-precision floating-point strided array using a one-pass textbook algorithm.</span> 123 - <span class="signature">[`dsemwd( N, correction, x, stride )`][@stdlib/stats/base/dsemwd]</span><span class="delimiter">: </span><span class="description">calculate the standard error of the mean of a double-precision floating-point strided array using Welford's algorithm.</span> 124 - <span class="signature">[`dsemyc( N, correction, x, stride )`][@stdlib/stats/base/dsemyc]</span><span class="delimiter">: </span><span class="description">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.</span> 125 - <span class="signature">[`dsmean( N, x, stride )`][@stdlib/stats/base/dsmean]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array using extended accumulation and returning an extended precision result.</span> 126 - <span class="signature">[`dsmeanors( N, x, stride )`][@stdlib/stats/base/dsmeanors]</span><span class="delimiter">: </span><span class="description">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.</span> 127 - <span class="signature">[`dsmeanpn( N, x, stride )`][@stdlib/stats/base/dsmeanpn]</span><span class="delimiter">: </span><span class="description">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.</span> 128 - <span class="signature">[`dsmeanpw( N, x, stride )`][@stdlib/stats/base/dsmeanpw]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array using pairwise summation with extended accumulation and returning an extended precision result.</span> 129 - <span class="signature">[`dsmeanwd( N, x, stride )`][@stdlib/stats/base/dsmeanwd]</span><span class="delimiter">: </span><span class="description">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.</span> 130 - <span class="signature">[`dsnanmean( N, x, stride )`][@stdlib/stats/base/dsnanmean]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values, using extended accumulation, and returning an extended precision result.</span> 131 - <span class="signature">[`dsnanmeanors( N, x, stride )`][@stdlib/stats/base/dsnanmeanors]</span><span class="delimiter">: </span><span class="description">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.</span> 132 - <span class="signature">[`dsnanmeanpn( N, x, stride )`][@stdlib/stats/base/dsnanmeanpn]</span><span class="delimiter">: </span><span class="description">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.</span> 133 - <span class="signature">[`dsnanmeanwd( N, x, stride )`][@stdlib/stats/base/dsnanmeanwd]</span><span class="delimiter">: </span><span class="description">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.</span> 134 - <span class="signature">[`dstdev( N, correction, x, stride )`][@stdlib/stats/base/dstdev]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a double-precision floating-point strided array.</span> 135 - <span class="signature">[`dstdevch( N, correction, x, stride )`][@stdlib/stats/base/dstdevch]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a double-precision floating-point strided array using a one-pass trial mean algorithm.</span> 136 - <span class="signature">[`dstdevpn( N, correction, x, stride )`][@stdlib/stats/base/dstdevpn]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a double-precision floating-point strided array using a two-pass algorithm.</span> 137 - <span class="signature">[`dstdevtk( N, correction, x, stride )`][@stdlib/stats/base/dstdevtk]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a double-precision floating-point strided array using a one-pass textbook algorithm.</span> 138 - <span class="signature">[`dstdevwd( N, correction, x, stride )`][@stdlib/stats/base/dstdevwd]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a double-precision floating-point strided array using Welford's algorithm.</span> 139 - <span class="signature">[`dstdevyc( N, correction, x, stride )`][@stdlib/stats/base/dstdevyc]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.</span> 140 - <span class="signature">[`dsvariance( N, correction, x, stride )`][@stdlib/stats/base/dsvariance]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array using extended accumulation and returning an extended precision result.</span> 141 - <span class="signature">[`dsvariancepn( N, correction, x, stride )`][@stdlib/stats/base/dsvariancepn]</span><span class="delimiter">: </span><span class="description">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.</span> 142 - <span class="signature">[`dvariance( N, correction, x, stride )`][@stdlib/stats/base/dvariance]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array.</span> 143 - <span class="signature">[`dvariancech( N, correction, x, stride )`][@stdlib/stats/base/dvariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array using a one-pass trial mean algorithm.</span> 144 - <span class="signature">[`dvariancepn( N, correction, x, stride )`][@stdlib/stats/base/dvariancepn]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array using a two-pass algorithm.</span> 145 - <span class="signature">[`dvariancetk( N, correction, x, stride )`][@stdlib/stats/base/dvariancetk]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array using a one-pass textbook algorithm.</span> 146 - <span class="signature">[`dvariancewd( N, correction, x, stride )`][@stdlib/stats/base/dvariancewd]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array using Welford's algorithm.</span> 147 - <span class="signature">[`dvarianceyc( N, correction, x, stride )`][@stdlib/stats/base/dvarianceyc]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.</span> 148 - <span class="signature">[`dvarm( N, mean, correction, x, stride )`][@stdlib/stats/base/dvarm]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array provided a known mean.</span> 149 - <span class="signature">[`dvarmpn( N, mean, correction, x, stride )`][@stdlib/stats/base/dvarmpn]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array provided a known mean and using Neely's correction algorithm.</span> 150 - <span class="signature">[`dvarmtk( N, mean, correction, x, stride )`][@stdlib/stats/base/dvarmtk]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array provided a known mean and using a one-pass textbook algorithm.</span> 151 - <span class="signature">[`maxBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/base/max-by]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a strided array via a callback function.</span> 152 - <span class="signature">[`max( N, x, stride )`][@stdlib/stats/base/max]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a strided array.</span> 153 - <span class="signature">[`maxabs( N, x, stride )`][@stdlib/stats/base/maxabs]</span><span class="delimiter">: </span><span class="description">calculate the maximum absolute value of a strided array.</span> 154 - <span class="signature">[`maxsorted( N, x, stride )`][@stdlib/stats/base/maxsorted]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a sorted strided array.</span> 155 - <span class="signature">[`mean( N, x, stride )`][@stdlib/stats/base/mean]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a strided array.</span> 156 - <span class="signature">[`meankbn( N, x, stride )`][@stdlib/stats/base/meankbn]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a strided array using an improved Kahan–Babuška algorithm.</span> 157 - <span class="signature">[`meankbn2( N, x, stride )`][@stdlib/stats/base/meankbn2]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a strided array using a second-order iterative Kahan–Babuška algorithm.</span> 158 - <span class="signature">[`meanors( N, x, stride )`][@stdlib/stats/base/meanors]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a strided array using ordinary recursive summation.</span> 159 - <span class="signature">[`meanpn( N, x, stride )`][@stdlib/stats/base/meanpn]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a strided array using a two-pass error correction algorithm.</span> 160 - <span class="signature">[`meanpw( N, x, stride )`][@stdlib/stats/base/meanpw]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a strided array using pairwise summation.</span> 161 - <span class="signature">[`meanwd( N, x, stride )`][@stdlib/stats/base/meanwd]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a strided array using Welford's algorithm.</span> 162 - <span class="signature">[`mediansorted( N, x, stride )`][@stdlib/stats/base/mediansorted]</span><span class="delimiter">: </span><span class="description">calculate the median value of a sorted strided array.</span> 163 - <span class="signature">[`minBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/base/min-by]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a strided array via a callback function.</span> 164 - <span class="signature">[`min( N, x, stride )`][@stdlib/stats/base/min]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a strided array.</span> 165 - <span class="signature">[`minabs( N, x, stride )`][@stdlib/stats/base/minabs]</span><span class="delimiter">: </span><span class="description">calculate the minimum absolute value of a strided array.</span> 166 - <span class="signature">[`minsorted( N, x, stride )`][@stdlib/stats/base/minsorted]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a sorted strided array.</span> 167 - <span class="signature">[`mskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/mskmax]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a strided array according to a mask.</span> 168 - <span class="signature">[`mskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/mskmin]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a strided array according to a mask.</span> 169 - <span class="signature">[`mskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/mskrange]</span><span class="delimiter">: </span><span class="description">calculate the range of a strided array according to a mask.</span> 170 - <span class="signature">[`nanmaxBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/base/nanmax-by]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a strided array via a callback function, ignoring `NaN` values.</span> 171 - <span class="signature">[`nanmax( N, x, stride )`][@stdlib/stats/base/nanmax]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a strided array, ignoring `NaN` values.</span> 172 - <span class="signature">[`nanmaxabs( N, x, stride )`][@stdlib/stats/base/nanmaxabs]</span><span class="delimiter">: </span><span class="description">calculate the maximum absolute value of a strided array, ignoring `NaN` values.</span> 173 - <span class="signature">[`nanmean( N, x, stride )`][@stdlib/stats/base/nanmean]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a strided array, ignoring `NaN` values.</span> 174 - <span class="signature">[`nanmeanors( N, x, stride )`][@stdlib/stats/base/nanmeanors]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a strided array, ignoring `NaN` values and using ordinary recursive summation.</span> 175 - <span class="signature">[`nanmeanpn( N, x, stride )`][@stdlib/stats/base/nanmeanpn]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a strided array, ignoring `NaN` values and using a two-pass error correction algorithm.</span> 176 - <span class="signature">[`nanmeanwd( N, x, stride )`][@stdlib/stats/base/nanmeanwd]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a strided array, ignoring `NaN` values and using Welford's algorithm.</span> 177 - <span class="signature">[`nanminBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/base/nanmin-by]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a strided array via a callback function, ignoring `NaN` values.</span> 178 - <span class="signature">[`nanmin( N, x, stride )`][@stdlib/stats/base/nanmin]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a strided array, ignoring `NaN` values.</span> 179 - <span class="signature">[`nanminabs( N, x, stride )`][@stdlib/stats/base/nanminabs]</span><span class="delimiter">: </span><span class="description">calculate the minimum absolute value of a strided array, ignoring `NaN` values.</span> 180 - <span class="signature">[`nanmskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/nanmskmax]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a strided array according to a mask, ignoring `NaN` values.</span> 181 - <span class="signature">[`nanmskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/nanmskmin]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a strided array according to a mask, ignoring `NaN` values.</span> 182 - <span class="signature">[`nanmskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/nanmskrange]</span><span class="delimiter">: </span><span class="description">calculate the range of a strided array according to a mask, ignoring `NaN` values.</span> 183 - <span class="signature">[`nanrangeBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/base/nanrange-by]</span><span class="delimiter">: </span><span class="description">calculate the range of a strided array via a callback function, ignoring `NaN` values.</span> 184 - <span class="signature">[`nanrange( N, x, stride )`][@stdlib/stats/base/nanrange]</span><span class="delimiter">: </span><span class="description">calculate the range of a strided array, ignoring `NaN` values.</span> 185 - <span class="signature">[`nanstdev( N, correction, x, stride )`][@stdlib/stats/base/nanstdev]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array ignoring `NaN` values.</span> 186 - <span class="signature">[`nanstdevch( N, correction, x, stride )`][@stdlib/stats/base/nanstdevch]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm.</span> 187 - <span class="signature">[`nanstdevpn( N, correction, x, stride )`][@stdlib/stats/base/nanstdevpn]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array ignoring `NaN` values and using a two-pass algorithm.</span> 188 - <span class="signature">[`nanstdevtk( N, correction, x, stride )`][@stdlib/stats/base/nanstdevtk]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array ignoring `NaN` values and using a one-pass textbook algorithm.</span> 189 - <span class="signature">[`nanstdevwd( N, correction, x, stride )`][@stdlib/stats/base/nanstdevwd]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array ignoring `NaN` values and using Welford's algorithm.</span> 190 - <span class="signature">[`nanstdevyc( N, correction, x, stride )`][@stdlib/stats/base/nanstdevyc]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and Cramer.</span> 191 - <span class="signature">[`nanvariance( N, correction, x, stride )`][@stdlib/stats/base/nanvariance]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring `NaN` values.</span> 192 - <span class="signature">[`nanvariancech( N, correction, x, stride )`][@stdlib/stats/base/nanvariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm.</span> 193 - <span class="signature">[`nanvariancepn( N, correction, x, stride )`][@stdlib/stats/base/nanvariancepn]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring `NaN` values and using a two-pass algorithm.</span> 194 - <span class="signature">[`nanvariancetk( N, correction, x, stride )`][@stdlib/stats/base/nanvariancetk]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring `NaN` values and using a one-pass textbook algorithm.</span> 195 - <span class="signature">[`nanvariancewd( N, correction, x, stride )`][@stdlib/stats/base/nanvariancewd]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring `NaN` values and using Welford's algorithm.</span> 196 - <span class="signature">[`nanvarianceyc( N, correction, x, stride )`][@stdlib/stats/base/nanvarianceyc]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and Cramer.</span> 197 - <span class="signature">[`rangeBy( N, x, stride, clbk[, thisArg] )`][@stdlib/stats/base/range-by]</span><span class="delimiter">: </span><span class="description">calculate the range of a strided array via a callback function.</span> 198 - <span class="signature">[`range( N, x, stride )`][@stdlib/stats/base/range]</span><span class="delimiter">: </span><span class="description">calculate the range of a strided array.</span> 199 - <span class="signature">[`scumax( N, x, strideX, y, strideY )`][@stdlib/stats/base/scumax]</span><span class="delimiter">: </span><span class="description">calculate the cumulative maximum of single-precision floating-point strided array elements.</span> 200 - <span class="signature">[`scumaxabs( N, x, strideX, y, strideY )`][@stdlib/stats/base/scumaxabs]</span><span class="delimiter">: </span><span class="description">calculate the cumulative maximum absolute value of single-precision floating-point strided array elements.</span> 201 - <span class="signature">[`scumin( N, x, strideX, y, strideY )`][@stdlib/stats/base/scumin]</span><span class="delimiter">: </span><span class="description">calculate the cumulative minimum of single-precision floating-point strided array elements.</span> 202 - <span class="signature">[`scuminabs( N, x, strideX, y, strideY )`][@stdlib/stats/base/scuminabs]</span><span class="delimiter">: </span><span class="description">calculate the cumulative minimum absolute value of single-precision floating-point strided array elements.</span> 203 - <span class="signature">[`sdsmean( N, x, stride )`][@stdlib/stats/base/sdsmean]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array using extended accumulation.</span> 204 - <span class="signature">[`sdsmeanors( N, x, stride )`][@stdlib/stats/base/sdsmeanors]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation with extended accumulation.</span> 205 - <span class="signature">[`sdsnanmean( N, x, stride )`][@stdlib/stats/base/sdsnanmean]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using extended accumulation.</span> 206 - <span class="signature">[`sdsnanmeanors( N, x, stride )`][@stdlib/stats/base/sdsnanmeanors]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using ordinary recursive summation with extended accumulation.</span> 207 - <span class="signature">[`smax( N, x, stride )`][@stdlib/stats/base/smax]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a single-precision floating-point strided array.</span> 208 - <span class="signature">[`smaxabs( N, x, stride )`][@stdlib/stats/base/smaxabs]</span><span class="delimiter">: </span><span class="description">calculate the maximum absolute value of a single-precision floating-point strided array.</span> 209 - <span class="signature">[`smaxabssorted( N, x, stride )`][@stdlib/stats/base/smaxabssorted]</span><span class="delimiter">: </span><span class="description">calculate the maximum absolute value of a sorted single-precision floating-point strided array.</span> 210 - <span class="signature">[`smaxsorted( N, x, stride )`][@stdlib/stats/base/smaxsorted]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a sorted single-precision floating-point strided array.</span> 211 - <span class="signature">[`smean( N, x, stride )`][@stdlib/stats/base/smean]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array.</span> 212 - <span class="signature">[`smeankbn( N, x, stride )`][@stdlib/stats/base/smeankbn]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array using an improved Kahan–Babuška algorithm.</span> 213 - <span class="signature">[`smeankbn2( N, x, stride )`][@stdlib/stats/base/smeankbn2]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array using a second-order iterative Kahan–Babuška algorithm.</span> 214 - <span class="signature">[`smeanli( N, x, stride )`][@stdlib/stats/base/smeanli]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array using a one-pass trial mean algorithm.</span> 215 - <span class="signature">[`smeanlipw( N, x, stride )`][@stdlib/stats/base/smeanlipw]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array using a one-pass trial mean algorithm with pairwise summation.</span> 216 - <span class="signature">[`smeanors( N, x, stride )`][@stdlib/stats/base/smeanors]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation.</span> 217 - <span class="signature">[`smeanpn( N, x, stride )`][@stdlib/stats/base/smeanpn]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm.</span> 218 - <span class="signature">[`smeanpw( N, x, stride )`][@stdlib/stats/base/smeanpw]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array using pairwise summation.</span> 219 - <span class="signature">[`smeanwd( N, x, stride )`][@stdlib/stats/base/smeanwd]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array using Welford's algorithm.</span> 220 - <span class="signature">[`smediansorted( N, x, stride )`][@stdlib/stats/base/smediansorted]</span><span class="delimiter">: </span><span class="description">calculate the median value of a sorted single-precision floating-point strided array.</span> 221 - <span class="signature">[`smidrange( N, x, stride )`][@stdlib/stats/base/smidrange]</span><span class="delimiter">: </span><span class="description">calculate the mid-range of a single-precision floating-point strided array.</span> 222 - <span class="signature">[`smin( N, x, stride )`][@stdlib/stats/base/smin]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a single-precision floating-point strided array.</span> 223 - <span class="signature">[`sminabs( N, x, stride )`][@stdlib/stats/base/sminabs]</span><span class="delimiter">: </span><span class="description">calculate the minimum absolute value of a single-precision floating-point strided array.</span> 224 - <span class="signature">[`sminsorted( N, x, stride )`][@stdlib/stats/base/sminsorted]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a sorted single-precision floating-point strided array.</span> 225 - <span class="signature">[`smskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/smskmax]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a single-precision floating-point strided array according to a mask.</span> 226 - <span class="signature">[`smskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/smskmin]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a single-precision floating-point strided array according to a mask.</span> 227 - <span class="signature">[`smskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/smskrange]</span><span class="delimiter">: </span><span class="description">calculate the range of a single-precision floating-point strided array according to a mask.</span> 228 - <span class="signature">[`snanmax( N, x, stride )`][@stdlib/stats/base/snanmax]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a single-precision floating-point strided array, ignoring `NaN` values.</span> 229 - <span class="signature">[`snanmaxabs( N, x, stride )`][@stdlib/stats/base/snanmaxabs]</span><span class="delimiter">: </span><span class="description">calculate the maximum absolute value of a single-precision floating-point strided array, ignoring `NaN` values.</span> 230 - <span class="signature">[`snanmean( N, x, stride )`][@stdlib/stats/base/snanmean]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values.</span> 231 - <span class="signature">[`snanmeanors( N, x, stride )`][@stdlib/stats/base/snanmeanors]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using ordinary recursive summation.</span> 232 - <span class="signature">[`snanmeanpn( N, x, stride )`][@stdlib/stats/base/snanmeanpn]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using a two-pass error correction algorithm.</span> 233 - <span class="signature">[`snanmeanwd( N, x, stride )`][@stdlib/stats/base/snanmeanwd]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using Welford's algorithm.</span> 234 - <span class="signature">[`snanmin( N, x, stride )`][@stdlib/stats/base/snanmin]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a single-precision floating-point strided array, ignoring `NaN` values.</span> 235 - <span class="signature">[`snanminabs( N, x, stride )`][@stdlib/stats/base/snanminabs]</span><span class="delimiter">: </span><span class="description">calculate the minimum absolute value of a single-precision floating-point strided array, ignoring `NaN` values.</span> 236 - <span class="signature">[`snanmskmax( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/snanmskmax]</span><span class="delimiter">: </span><span class="description">calculate the maximum value of a single-precision floating-point strided array according to a mask, ignoring `NaN` values.</span> 237 - <span class="signature">[`snanmskmin( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/snanmskmin]</span><span class="delimiter">: </span><span class="description">calculate the minimum value of a single-precision floating-point strided array according to a mask, ignoring `NaN` values.</span> 238 - <span class="signature">[`snanmskrange( N, x, strideX, mask, strideMask )`][@stdlib/stats/base/snanmskrange]</span><span class="delimiter">: </span><span class="description">calculate the range of a single-precision floating-point strided array according to a mask, ignoring `NaN` values.</span> 239 - <span class="signature">[`snanrange( N, x, stride )`][@stdlib/stats/base/snanrange]</span><span class="delimiter">: </span><span class="description">calculate the range of a single-precision floating-point strided array, ignoring `NaN` values.</span> 240 - <span class="signature">[`snanstdev( N, correction, x, stride )`][@stdlib/stats/base/snanstdev]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array ignoring `NaN` values.</span> 241 - <span class="signature">[`snanstdevch( N, correction, x, stride )`][@stdlib/stats/base/snanstdevch]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array ignoring `NaN` values and using a one-pass trial mean algorithm.</span> 242 - <span class="signature">[`snanstdevpn( N, correction, x, stride )`][@stdlib/stats/base/snanstdevpn]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array ignoring `NaN` values and using a two-pass algorithm.</span> 243 - <span class="signature">[`snanstdevtk( N, correction, x, stride )`][@stdlib/stats/base/snanstdevtk]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm.</span> 244 - <span class="signature">[`snanstdevwd( N, correction, x, stride )`][@stdlib/stats/base/snanstdevwd]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array ignoring `NaN` values and using Welford's algorithm.</span> 245 - <span class="signature">[`snanstdevyc( N, correction, x, stride )`][@stdlib/stats/base/snanstdevyc]</span><span class="delimiter">: </span><span class="description">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.</span> 246 - <span class="signature">[`snanvariance( N, correction, x, stride )`][@stdlib/stats/base/snanvariance]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array ignoring `NaN` values.</span> 247 - <span class="signature">[`snanvariancech( N, correction, x, stride )`][@stdlib/stats/base/snanvariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array ignoring `NaN` values and using a one-pass trial mean algorithm.</span> 248 - <span class="signature">[`snanvariancepn( N, correction, x, stride )`][@stdlib/stats/base/snanvariancepn]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array ignoring `NaN` values and using a two-pass algorithm.</span> 249 - <span class="signature">[`snanvariancetk( N, correction, x, stride )`][@stdlib/stats/base/snanvariancetk]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array ignoring `NaN` values and using a one-pass textbook algorithm.</span> 250 - <span class="signature">[`snanvariancewd( N, correction, x, stride )`][@stdlib/stats/base/snanvariancewd]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array ignoring `NaN` values and using Welford's algorithm.</span> 251 - <span class="signature">[`snanvarianceyc( N, correction, x, stride )`][@stdlib/stats/base/snanvarianceyc]</span><span class="delimiter">: </span><span class="description">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.</span> 252 - <span class="signature">[`srange( N, x, stride )`][@stdlib/stats/base/srange]</span><span class="delimiter">: </span><span class="description">calculate the range of a single-precision floating-point strided array.</span> 253 - <span class="signature">[`sstdev( N, correction, x, stride )`][@stdlib/stats/base/sstdev]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array.</span> 254 - <span class="signature">[`sstdevch( N, correction, x, stride )`][@stdlib/stats/base/sstdevch]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array using a one-pass trial mean algorithm.</span> 255 - <span class="signature">[`sstdevpn( N, correction, x, stride )`][@stdlib/stats/base/sstdevpn]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array using a two-pass algorithm.</span> 256 - <span class="signature">[`sstdevtk( N, correction, x, stride )`][@stdlib/stats/base/sstdevtk]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array using a one-pass textbook algorithm.</span> 257 - <span class="signature">[`sstdevwd( N, correction, x, stride )`][@stdlib/stats/base/sstdevwd]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array using Welford's algorithm.</span> 258 - <span class="signature">[`sstdevyc( N, correction, x, stride )`][@stdlib/stats/base/sstdevyc]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.</span> 259 - <span class="signature">[`stdev( N, correction, x, stride )`][@stdlib/stats/base/stdev]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array.</span> 260 - <span class="signature">[`stdevch( N, correction, x, stride )`][@stdlib/stats/base/stdevch]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array using a one-pass trial mean algorithm.</span> 261 - <span class="signature">[`stdevpn( N, correction, x, stride )`][@stdlib/stats/base/stdevpn]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array using a two-pass algorithm.</span> 262 - <span class="signature">[`stdevtk( N, correction, x, stride )`][@stdlib/stats/base/stdevtk]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array using a one-pass textbook algorithm.</span> 263 - <span class="signature">[`stdevwd( N, correction, x, stride )`][@stdlib/stats/base/stdevwd]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array using Welford's algorithm.</span> 264 - <span class="signature">[`stdevyc( N, correction, x, stride )`][@stdlib/stats/base/stdevyc]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array using a one-pass algorithm proposed by Youngs and Cramer.</span> 265 - <span class="signature">[`svariance( N, correction, x, stride )`][@stdlib/stats/base/svariance]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array.</span> 266 - <span class="signature">[`svariancech( N, correction, x, stride )`][@stdlib/stats/base/svariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array using a one-pass trial mean algorithm.</span> 267 - <span class="signature">[`svariancepn( N, correction, x, stride )`][@stdlib/stats/base/svariancepn]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array using a two-pass algorithm.</span> 268 - <span class="signature">[`svariancetk( N, correction, x, stride )`][@stdlib/stats/base/svariancetk]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array using a one-pass textbook algorithm.</span> 269 - <span class="signature">[`svariancewd( N, correction, x, stride )`][@stdlib/stats/base/svariancewd]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array using Welford's algorithm.</span> 270 - <span class="signature">[`svarianceyc( N, correction, x, stride )`][@stdlib/stats/base/svarianceyc]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.</span> 271 - <span class="signature">[`variance( N, correction, x, stride )`][@stdlib/stats/base/variance]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array.</span> 272 - <span class="signature">[`variancech( N, correction, x, stride )`][@stdlib/stats/base/variancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array using a one-pass trial mean algorithm.</span> 273 - <span class="signature">[`variancepn( N, correction, x, stride )`][@stdlib/stats/base/variancepn]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array using a two-pass algorithm.</span> 274 - <span class="signature">[`variancetk( N, correction, x, stride )`][@stdlib/stats/base/variancetk]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array using a one-pass textbook algorithm.</span> 275 - <span class="signature">[`variancewd( N, correction, x, stride )`][@stdlib/stats/base/variancewd]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array using Welford's algorithm.</span> 276 - <span class="signature">[`varianceyc( N, correction, x, stride )`][@stdlib/stats/base/varianceyc]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array using a one-pass algorithm proposed by Youngs and Cramer.</span> 277 278 </div> 279 280 <!-- </toc> --> 281 282 </section> 283 284 <!-- /.usage --> 285 286 <!-- Package notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. --> 287 288 <section class="notes"> 289 290 </section> 291 292 <!-- /.notes --> 293 294 <section class="examples"> 295 296 ## Examples 297 298 <!-- TODO: better examples --> 299 300 <!-- eslint no-undef: "error" --> 301 302 ```javascript 303 var objectKeys = require( '@stdlib/utils/keys' ); 304 var ns = require( '@stdlib/stats/base' ); 305 306 console.log( objectKeys( ns ) ); 307 ``` 308 309 </section> 310 311 <!-- /.examples --> 312 313 <section class="links"> 314 315 <!-- <toc-links> --> 316 317 [@stdlib/stats/base/cumax]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/cumax 318 319 [@stdlib/stats/base/cumaxabs]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/cumaxabs 320 321 [@stdlib/stats/base/cumin]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/cumin 322 323 [@stdlib/stats/base/cuminabs]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/cuminabs 324 325 [@stdlib/stats/base/dcumax]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dcumax 326 327 [@stdlib/stats/base/dcumaxabs]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dcumaxabs 328 329 [@stdlib/stats/base/dcumin]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dcumin 330 331 [@stdlib/stats/base/dcuminabs]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dcuminabs 332 333 [@stdlib/stats/base/dmax]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmax 334 335 [@stdlib/stats/base/dmaxabs]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmaxabs 336 337 [@stdlib/stats/base/dmaxabssorted]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmaxabssorted 338 339 [@stdlib/stats/base/dmaxsorted]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmaxsorted 340 341 [@stdlib/stats/base/dmean]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmean 342 343 [@stdlib/stats/base/dmeankbn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmeankbn 344 345 [@stdlib/stats/base/dmeankbn2]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmeankbn2 346 347 [@stdlib/stats/base/dmeanli]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmeanli 348 349 [@stdlib/stats/base/dmeanlipw]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmeanlipw 350 351 [@stdlib/stats/base/dmeanors]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmeanors 352 353 [@stdlib/stats/base/dmeanpn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmeanpn 354 355 [@stdlib/stats/base/dmeanpw]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmeanpw 356 357 [@stdlib/stats/base/dmeanstdev]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmeanstdev 358 359 [@stdlib/stats/base/dmeanstdevpn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmeanstdevpn 360 361 [@stdlib/stats/base/dmeanvar]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmeanvar 362 363 [@stdlib/stats/base/dmeanvarpn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmeanvarpn 364 365 [@stdlib/stats/base/dmeanwd]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmeanwd 366 367 [@stdlib/stats/base/dmediansorted]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmediansorted 368 369 [@stdlib/stats/base/dmidrange]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmidrange 370 371 [@stdlib/stats/base/dmin]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmin 372 373 [@stdlib/stats/base/dminabs]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dminabs 374 375 [@stdlib/stats/base/dminsorted]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dminsorted 376 377 [@stdlib/stats/base/dmskmax]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmskmax 378 379 [@stdlib/stats/base/dmskmin]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmskmin 380 381 [@stdlib/stats/base/dmskrange]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dmskrange 382 383 [@stdlib/stats/base/dnanmax]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanmax 384 385 [@stdlib/stats/base/dnanmaxabs]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanmaxabs 386 387 [@stdlib/stats/base/dnanmean]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanmean 388 389 [@stdlib/stats/base/dnanmeanors]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanmeanors 390 391 [@stdlib/stats/base/dnanmeanpn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanmeanpn 392 393 [@stdlib/stats/base/dnanmeanpw]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanmeanpw 394 395 [@stdlib/stats/base/dnanmeanwd]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanmeanwd 396 397 [@stdlib/stats/base/dnanmin]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanmin 398 399 [@stdlib/stats/base/dnanminabs]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanminabs 400 401 [@stdlib/stats/base/dnanmskmax]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanmskmax 402 403 [@stdlib/stats/base/dnanmskmin]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanmskmin 404 405 [@stdlib/stats/base/dnanmskrange]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanmskrange 406 407 [@stdlib/stats/base/dnanrange]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanrange 408 409 [@stdlib/stats/base/dnanstdev]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanstdev 410 411 [@stdlib/stats/base/dnanstdevch]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanstdevch 412 413 [@stdlib/stats/base/dnanstdevpn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanstdevpn 414 415 [@stdlib/stats/base/dnanstdevtk]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanstdevtk 416 417 [@stdlib/stats/base/dnanstdevwd]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanstdevwd 418 419 [@stdlib/stats/base/dnanstdevyc]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanstdevyc 420 421 [@stdlib/stats/base/dnanvariance]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanvariance 422 423 [@stdlib/stats/base/dnanvariancech]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanvariancech 424 425 [@stdlib/stats/base/dnanvariancepn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanvariancepn 426 427 [@stdlib/stats/base/dnanvariancetk]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanvariancetk 428 429 [@stdlib/stats/base/dnanvariancewd]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanvariancewd 430 431 [@stdlib/stats/base/dnanvarianceyc]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dnanvarianceyc 432 433 [@stdlib/stats/base/drange]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/drange 434 435 [@stdlib/stats/base/dsem]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsem 436 437 [@stdlib/stats/base/dsemch]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsemch 438 439 [@stdlib/stats/base/dsempn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsempn 440 441 [@stdlib/stats/base/dsemtk]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsemtk 442 443 [@stdlib/stats/base/dsemwd]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsemwd 444 445 [@stdlib/stats/base/dsemyc]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsemyc 446 447 [@stdlib/stats/base/dsmean]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsmean 448 449 [@stdlib/stats/base/dsmeanors]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsmeanors 450 451 [@stdlib/stats/base/dsmeanpn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsmeanpn 452 453 [@stdlib/stats/base/dsmeanpw]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsmeanpw 454 455 [@stdlib/stats/base/dsmeanwd]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsmeanwd 456 457 [@stdlib/stats/base/dsnanmean]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsnanmean 458 459 [@stdlib/stats/base/dsnanmeanors]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsnanmeanors 460 461 [@stdlib/stats/base/dsnanmeanpn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsnanmeanpn 462 463 [@stdlib/stats/base/dsnanmeanwd]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsnanmeanwd 464 465 [@stdlib/stats/base/dstdev]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dstdev 466 467 [@stdlib/stats/base/dstdevch]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dstdevch 468 469 [@stdlib/stats/base/dstdevpn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dstdevpn 470 471 [@stdlib/stats/base/dstdevtk]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dstdevtk 472 473 [@stdlib/stats/base/dstdevwd]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dstdevwd 474 475 [@stdlib/stats/base/dstdevyc]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dstdevyc 476 477 [@stdlib/stats/base/dsvariance]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsvariance 478 479 [@stdlib/stats/base/dsvariancepn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dsvariancepn 480 481 [@stdlib/stats/base/dvariance]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dvariance 482 483 [@stdlib/stats/base/dvariancech]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dvariancech 484 485 [@stdlib/stats/base/dvariancepn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dvariancepn 486 487 [@stdlib/stats/base/dvariancetk]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dvariancetk 488 489 [@stdlib/stats/base/dvariancewd]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dvariancewd 490 491 [@stdlib/stats/base/dvarianceyc]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dvarianceyc 492 493 [@stdlib/stats/base/dvarm]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dvarm 494 495 [@stdlib/stats/base/dvarmpn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dvarmpn 496 497 [@stdlib/stats/base/dvarmtk]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/dvarmtk 498 499 [@stdlib/stats/base/max-by]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/max-by 500 501 [@stdlib/stats/base/max]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/max 502 503 [@stdlib/stats/base/maxabs]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/maxabs 504 505 [@stdlib/stats/base/maxsorted]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/maxsorted 506 507 [@stdlib/stats/base/mean]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/mean 508 509 [@stdlib/stats/base/meankbn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/meankbn 510 511 [@stdlib/stats/base/meankbn2]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/meankbn2 512 513 [@stdlib/stats/base/meanors]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/meanors 514 515 [@stdlib/stats/base/meanpn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/meanpn 516 517 [@stdlib/stats/base/meanpw]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/meanpw 518 519 [@stdlib/stats/base/meanwd]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/meanwd 520 521 [@stdlib/stats/base/mediansorted]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/mediansorted 522 523 [@stdlib/stats/base/min-by]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/min-by 524 525 [@stdlib/stats/base/min]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/min 526 527 [@stdlib/stats/base/minabs]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/minabs 528 529 [@stdlib/stats/base/minsorted]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/minsorted 530 531 [@stdlib/stats/base/mskmax]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/mskmax 532 533 [@stdlib/stats/base/mskmin]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/mskmin 534 535 [@stdlib/stats/base/mskrange]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/mskrange 536 537 [@stdlib/stats/base/nanmax-by]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanmax-by 538 539 [@stdlib/stats/base/nanmax]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanmax 540 541 [@stdlib/stats/base/nanmaxabs]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanmaxabs 542 543 [@stdlib/stats/base/nanmean]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanmean 544 545 [@stdlib/stats/base/nanmeanors]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanmeanors 546 547 [@stdlib/stats/base/nanmeanpn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanmeanpn 548 549 [@stdlib/stats/base/nanmeanwd]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanmeanwd 550 551 [@stdlib/stats/base/nanmin-by]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanmin-by 552 553 [@stdlib/stats/base/nanmin]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanmin 554 555 [@stdlib/stats/base/nanminabs]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanminabs 556 557 [@stdlib/stats/base/nanmskmax]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanmskmax 558 559 [@stdlib/stats/base/nanmskmin]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanmskmin 560 561 [@stdlib/stats/base/nanmskrange]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanmskrange 562 563 [@stdlib/stats/base/nanrange-by]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanrange-by 564 565 [@stdlib/stats/base/nanrange]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanrange 566 567 [@stdlib/stats/base/nanstdev]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanstdev 568 569 [@stdlib/stats/base/nanstdevch]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanstdevch 570 571 [@stdlib/stats/base/nanstdevpn]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanstdevpn 572 573 [@stdlib/stats/base/nanstdevtk]: https://www.npmjs.com/package/@stdlib/stats/tree/main/base/nanstdevtk 574 575 [@stdlib/stats/base/nanstdevwd]: 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