docs: update namespace table of contents

PR-URL: #7354

Reviewed-by: Philipp Burckhardt <[email protected]>
Signed-off-by: stdlib-bot <[email protected]>

Author: stdlib-bot

lib/node_modules/@stdlib/stats/base/README.md

@@ -65,8 +65,8 @@ The namespace contains the following statistical functions:
 -   <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>
 -   <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>
 -   <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>
--   <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>
--   <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>
+-   <span class="signature">[`dvarm( N, mean, correction, x, strideX )`][@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>
+-   <span class="signature">[`dvarmpn( N, mean, correction, x, strideX )`][@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>
 -   <span class="signature">[`nanmean( N, x, strideX )`][@stdlib/stats/base/nanmean]</span><span class="delimiter">: </span><span class="description">calculate the arithmetic mean of a strided array, ignoring `NaN` values.</span>
 -   <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>
 -   <span class="signature">[`nanmeanpn( N, x, strideX )`][@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>
@@ -83,7 +83,7 @@ The namespace contains the following statistical functions:
 -   <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>
 -   <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>
 -   <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>
--   <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>
+-   <span class="signature">[`nanvariancech( N, correction, x, strideX )`][@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>
 -   <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>
 -   <span class="signature">[`nanvariancetk( N, correction, x, strideX )`][@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>
 -   <span class="signature">[`nanvariancewd( N, correction, x, strideX )`][@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>
@@ -111,17 +111,17 @@ The namespace contains the following statistical functions:
 -   <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>
 -   <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>
 -   <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>
--   <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>
--   <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>
--   <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>
+-   <span class="signature">[`stdev( N, correction, x, strideX )`][@stdlib/stats/base/stdev]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array.</span>
+-   <span class="signature">[`stdevch( N, correction, x, strideX )`][@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>
+-   <span class="signature">[`stdevpn( N, correction, x, strideX )`][@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>
 -   <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>
 -   <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>
 -   <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>
 -   <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>
 -   <span class="signature">[`svariancewd( N, correction, x, strideX )`][@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>
 -   <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>
--   <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>
--   <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>
+-   <span class="signature">[`variancech( N, correction, x, strideX )`][@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>
+-   <span class="signature">[`variancepn( N, correction, x, strideX )`][@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>
 -   <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>
 -   <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>
 -   <span class="signature">[`varianceyc( N, correction, x, strideX )`][@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>