docs: update namespace table of contents

PR-URL: #7458

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

Author: stdlib-bot

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

@@ -66,23 +66,23 @@ The namespace contains the following statistical functions:
 -   <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">[`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">[`nanmeanors( N, x, strideX )`][@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>
--   <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>
+-   <span class="signature">[`nanmeanwd( N, x, strideX )`][@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>
 -   <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>
 -   <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>
 -   <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>
 -   <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>
 -   <span class="signature">[`nanrange( N, x, strideX )`][@stdlib/stats/base/nanrange]</span><span class="delimiter">: </span><span class="description">calculate the range of a strided array, ignoring `NaN` values.</span>
--   <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>
+-   <span class="signature">[`nanstdev( N, correction, x, strideX )`][@stdlib/stats/base/nanstdev]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array ignoring `NaN` values.</span>
 -   <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>
--   <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>
+-   <span class="signature">[`nanstdevpn( N, correction, x, strideX )`][@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>
 -   <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>
 -   <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">[`nanstdevyc( N, correction, x, strideX )`][@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, strideX )`][@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, 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">[`nanvariancepn( N, correction, x, strideX )`][@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>
 -   <span class="signature">[`nanvarianceyc( N, correction, x, strideX )`][@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>

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

@@ -48,10 +48,12 @@ The namespace exposes the following APIs:
 -   <span class="signature">[`cumax( arrays )`][@stdlib/stats/base/ndarray/cumax]</span><span class="delimiter">: </span><span class="description">compute the cumulative maximum value of a one-dimensional ndarray.</span>
 -   <span class="signature">[`dcumax( arrays )`][@stdlib/stats/base/ndarray/dcumax]</span><span class="delimiter">: </span><span class="description">compute the cumulative maximum value of a one-dimensional double-precision floating-point ndarray.</span>
 -   <span class="signature">[`dmax( arrays )`][@stdlib/stats/base/ndarray/dmax]</span><span class="delimiter">: </span><span class="description">compute the maximum value of a one-dimensional double-precision floating-point ndarray.</span>
+-   <span class="signature">[`dztest( arrays )`][@stdlib/stats/base/ndarray/dztest]</span><span class="delimiter">: </span><span class="description">compute a one-sample Z-test for a one-dimensional double-precision floating-point ndarray.</span>
 -   <span class="signature">[`maxBy( arrays, clbk[, thisArg ] )`][@stdlib/stats/base/ndarray/max-by]</span><span class="delimiter">: </span><span class="description">compute the maximum value of a one-dimensional ndarray via a callback function.</span>
 -   <span class="signature">[`max( arrays )`][@stdlib/stats/base/ndarray/max]</span><span class="delimiter">: </span><span class="description">compute the maximum value of a one-dimensional ndarray.</span>
 -   <span class="signature">[`scumax( arrays )`][@stdlib/stats/base/ndarray/scumax]</span><span class="delimiter">: </span><span class="description">compute the cumulative maximum value of a one-dimensional single-precision floating-point ndarray.</span>
 -   <span class="signature">[`smax( arrays )`][@stdlib/stats/base/ndarray/smax]</span><span class="delimiter">: </span><span class="description">compute the maximum value of a one-dimensional single-precision floating-point ndarray.</span>
+-   <span class="signature">[`sztest( arrays )`][@stdlib/stats/base/ndarray/sztest]</span><span class="delimiter">: </span><span class="description">compute a one-sample Z-test for a one-dimensional single-precision floating-point ndarray.</span>
 
 </div>
 
@@ -100,6 +102,8 @@ console.log( objectKeys( ns ) );
 
 [@stdlib/stats/base/ndarray/dmax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dmax
 
+[@stdlib/stats/base/ndarray/dztest]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/dztest
+
 [@stdlib/stats/base/ndarray/max-by]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/max-by
 
 [@stdlib/stats/base/ndarray/max]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/max
@@ -108,6 +112,8 @@ console.log( objectKeys( ns ) );
 
 [@stdlib/stats/base/ndarray/smax]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/smax
 
+[@stdlib/stats/base/ndarray/sztest]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/ndarray/sztest
+
 <!-- </toc-links> -->
 
 </section>