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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>
 
+-   [`adfa457`](https://github.com/stdlib-js/stdlib/commit/adfa4575e7715273bb374e76860236608ca4a165) - **refactor:** update paths _(by Aayush Khanna)_
 -   [`ab7cd64`](https://github.com/stdlib-js/stdlib/commit/ab7cd64f58fab5278c0160ad70d494fb545f6a44) - **refactor:** update paths _(by Aayush Khanna)_
 
 </details>

README.md

@@ -216,7 +216,7 @@ var v = variancech.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 variance 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 ([`dvariancech`][@stdlib/stats/strided/dvariancech], [`svariancech`][@stdlib/stats/base/svariancech], etc.) are likely to be significantly more performant.
+-   Depending on the environment, the typed versions ([`dvariancech`][@stdlib/stats/strided/dvariancech], [`svariancech`][@stdlib/stats/strided/svariancech], etc.) are likely to be significantly more performant.
 
 </section>
 
@@ -361,7 +361,7 @@ Copyright &copy; 2016-2025. The Stdlib [Authors][stdlib-authors].
 
 [mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
 
-[@stdlib/stats/base/svariancech]: https://github.com/stdlib-js/stats-base-svariancech
+[@stdlib/stats/strided/svariancech]: https://github.com/stdlib-js/stats-strided-svariancech
 
 [@neely:1966a]: https://doi.org/10.1145/365719.365958