docs: update namespace table of contents

PR-URL: #6010
Co-authored-by: Athan Reines <[email protected]>
Reviewed-by: Athan Reines <[email protected]> 
Signed-off-by: stdlib-bot <[email protected]>
Signed-off-by: Athan Reines <[email protected]>

Author: stdlib-bot

lib/node_modules/@stdlib/math/base/ops/README.md

@@ -45,7 +45,6 @@ The namespace contains the following functions:
 
 <div class="namespace-toc">
 
--   <span class="signature">[`cdiv( z1, z2 )`][@stdlib/complex/float64/base/div]</span><span class="delimiter">: </span><span class="description">divide two double-precision complex floating-point numbers.</span>
 -   <span class="signature">[`cneg( z )`][@stdlib/math/base/ops/cneg]</span><span class="delimiter">: </span><span class="description">negate a double-precision complex floating-point number.</span>
 -   <span class="signature">[`cnegf( z )`][@stdlib/math/base/ops/cnegf]</span><span class="delimiter">: </span><span class="description">negate a single-precision complex floating-point number.</span>
 -   <span class="signature">[`csub( z1, z2 )`][@stdlib/math/base/ops/csub]</span><span class="delimiter">: </span><span class="description">subtract two double-precision complex floating-point numbers.</span>
@@ -68,20 +67,9 @@ The namespace contains the following functions:
 <!-- eslint no-undef: "error" -->
 
 ```javascript
-var Complex128 = require( '@stdlib/complex/float64/ctor' );
 var ns = require( '@stdlib/math/base/ops' );
 
-console.log( ns.sub( 1.25, 0.45 ) );
-// => 0.8
-
-console.log( ns.divf( 1.2, 0.4 ) );
-// => 3.0
-
-// Operations for complex numbers:
-var z1 = new Complex128( 5.0, 3.0 );
-var z2 = new Complex128( -2.0, 1.0 );
-console.log( ns.cmul( z1, z2 ) ); // { 're': -13.0, 'im': -1.0 }
-// => <Complex128>
+console.log( ns );
 ```
 
 </section>
@@ -102,8 +90,6 @@ console.log( ns.cmul( z1, z2 ) ); // { 're': -13.0, 'im': -1.0 }
 
 <!-- <toc-links> -->
 
-[@stdlib/complex/float64/base/div]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/complex/float64/base/div
-
 [@stdlib/math/base/ops/cneg]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/base/ops/cneg
 
 [@stdlib/math/base/ops/cnegf]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/base/ops/cnegf

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

@@ -85,7 +85,7 @@ The namespace contains the following statistical functions:
 -   <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>
 -   <span class="signature">[`dnanstdevtk( N, correction, x, strideX )`][@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>
 -   <span class="signature">[`dnanstdevwd( N, correction, x, strideX )`][@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>
--   <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>
+-   <span class="signature">[`dnanstdevyc( N, correction, x, strideX )`][@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>
 -   <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>
 -   <span class="signature">[`dnanvariancech( N, correction, x, strideX )`][@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>
 -   <span class="signature">[`dnanvariancepn( N, correction, x, strideX )`][@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>
@@ -104,13 +104,13 @@ The namespace contains the following statistical functions:
 -   <span class="signature">[`dsmeanpn( N, x, strideX )`][@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>
 -   <span class="signature">[`dsmeanpw( N, x, strideX )`][@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>
 -   <span class="signature">[`dsmeanwd( N, x, strideX )`][@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>
--   <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>
+-   <span class="signature">[`dsnanmean( N, x, strideX )`][@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>
 -   <span class="signature">[`dsnanmeanors( N, x, strideX )`][@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>
 -   <span class="signature">[`dsnanmeanpn( N, x, strideX )`][@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>
 -   <span class="signature">[`dsnanmeanwd( N, x, strideX )`][@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>
 -   <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>
 -   <span class="signature">[`dstdevch( N, correction, x, strideX )`][@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>
--   <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>
+-   <span class="signature">[`dstdevpn( N, correction, x, strideX )`][@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>
 -   <span class="signature">[`dstdevtk( N, correction, x, strideX )`][@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>
 -   <span class="signature">[`dstdevwd( N, correction, x, strideX )`][@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>
 -   <span class="signature">[`dstdevyc( N, correction, x, strideX )`][@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>
@@ -241,7 +241,7 @@ The namespace contains the following statistical functions:
 -   <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">[`svariancech( N, correction, x, strideX )`][@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>
--   <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>
+-   <span class="signature">[`svariancepn( N, correction, x, strideX )`][@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>
 -   <span class="signature">[`svariancetk( N, correction, x, strideX )`][@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>
 -   <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>
 -   <span class="signature">[`svarianceyc( N, correction, x, strideX )`][@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>