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Author: stdlib-bot

CHANGELOG.md

@@ -4,14 +4,15 @@
 
 <section class="release" id="unreleased">
 
-## Unreleased (2025-03-29)
+## Unreleased (2025-03-30)
 
 <section class="commits">
 
 ### Commits
 
 <details>
 
+-   [`4894ba9`](https://github.com/stdlib-js/stdlib/commit/4894ba93c712a1b42242c1c597afa9cdc811ad8c) - **refactor:** update paths _(by Aayush Khanna)_
 -   [`17c4e41`](https://github.com/stdlib-js/stdlib/commit/17c4e41ad480a465b085afa0de85e34957861710) - **refactor:** update paths _(by Aayush Khanna)_
 
 </details>

README.md

@@ -216,7 +216,7 @@ var v = stdevch.ndarray( N, 1, x, 2, 1 );
 -   If `N <= 0`, both functions return `NaN`.
 -   If `N - c` is less than or equal to `0` (where `c` corresponds to the provided degrees of freedom adjustment), both functions return `NaN`.
 -   The underlying algorithm is a specialized case of Neely's two-pass algorithm. As the standard deviation is invariant with respect to changes in the location parameter, the underlying algorithm uses the first strided array element as a trial mean to shift subsequent data values and thus mitigate catastrophic cancellation. Accordingly, the algorithm's accuracy is best when data is **unordered** (i.e., the data is **not** sorted in either ascending or descending order such that the first value is an "extreme" value).
--   Depending on the environment, the typed versions ([`dstdevch`][@stdlib/stats/strided/dstdevch], [`sstdevch`][@stdlib/stats/base/sstdevch], etc.) are likely to be significantly more performant.
+-   Depending on the environment, the typed versions ([`dstdevch`][@stdlib/stats/strided/dstdevch], [`sstdevch`][@stdlib/stats/strided/sstdevch], etc.) are likely to be significantly more performant.
 
 </section>
 
@@ -276,7 +276,7 @@ console.log( v );
 
 -   <span class="package-name">[`@stdlib/stats-strided/dstdevch`][@stdlib/stats/strided/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="package-name">[`@stdlib/stats-base/nanstdevch`][@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="package-name">[`@stdlib/stats-base/sstdevch`][@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>
+-   <span class="package-name">[`@stdlib/stats-strided/sstdevch`][@stdlib/stats/strided/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>
 -   <span class="package-name">[`@stdlib/stats-base/stdev`][@stdlib/stats/base/stdev]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array.</span>
 -   <span class="package-name">[`@stdlib/stats-base/variancech`][@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>
 
@@ -376,7 +376,7 @@ Copyright &copy; 2016-2025. The Stdlib [Authors][stdlib-authors].
 
 [@stdlib/stats/base/nanstdevch]: https://github.com/stdlib-js/stats-base-nanstdevch
 
-[@stdlib/stats/base/sstdevch]: https://github.com/stdlib-js/stats-base-sstdevch
+[@stdlib/stats/strided/sstdevch]: https://github.com/stdlib-js/stats-strided-sstdevch
 
 [@stdlib/stats/base/stdev]: https://github.com/stdlib-js/stats-base-stdev