docs: add missing references and add missing notes

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Author: kgryte

lib/node_modules/@stdlib/stats/array/stdevch/README.md

@@ -162,6 +162,21 @@ console.log( v );
 
 <!-- /.examples -->
 
+* * *
+
+<section class="references">
+
+## References
+
+-   Neely, Peter M. 1966. "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients." _Communications of the ACM_ 9 (7). Association for Computing Machinery: 496–99. doi:[10.1145/365719.365958][@neely:1966a].
+-   Ling, Robert F. 1974. "Comparison of Several Algorithms for Computing Sample Means and Variances." _Journal of the American Statistical Association_ 69 (348). American Statistical Association, Taylor & Francis, Ltd.: 859–66. doi:[10.2307/2286154][@ling:1974a].
+-   Chan, Tony F., Gene H. Golub, and Randall J. LeVeque. 1983. "Algorithms for Computing the Sample Variance: Analysis and Recommendations." _The American Statistician_ 37 (3). American Statistical Association, Taylor & Francis, Ltd.: 242–47. doi:[10.1080/00031305.1983.10483115][@chan:1983a].
+-   Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In _Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. New York, NY, USA: Association for Computing Machinery. doi:[10.1145/3221269.3223036][@schubert:2018a].
+
+</section>
+
+<!-- /.references -->
+
 <!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
 
 <section class="related">
@@ -178,6 +193,14 @@ console.log( v );
 
 [@stdlib/array/base/accessor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/accessor
 
+[@neely:1966a]: https://doi.org/10.1145/365719.365958
+
+[@ling:1974a]: https://doi.org/10.2307/2286154
+
+[@chan:1983a]: https://doi.org/10.1080/00031305.1983.10483115
+
+[@schubert:2018a]: https://doi.org/10.1145/3221269.3223036
+
 </section>
 
 <!-- /.links -->

lib/node_modules/@stdlib/stats/array/stdevch/docs/types/index.d.ts

@@ -30,6 +30,10 @@ type InputArray = NumericArray | Collection<number> | AccessorArrayLike<number>;
 /**
 * Computes the standard deviation of an array using a one-pass trial mean algorithm.
 *
+* ## Notes
+*
+* -   Setting the correction parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the standard deviation according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the standard deviation of a population, setting the correction parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the corrected sample standard deviation, setting the correction parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction).
+*
 * @param x - input array
 * @param correction - degrees of freedom adjustment (default: 1.0)
 * @returns standard deviation

lib/node_modules/@stdlib/stats/array/stdevpn/docs/types/index.d.ts

@@ -30,6 +30,10 @@ type InputArray = NumericArray | Collection<number> | AccessorArrayLike<number>;
 /**
 * Computes the standard deviation of an array using a two-pass algorithm.
 *
+* ## Notes
+*
+* -   Setting the correction parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the standard deviation according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the standard deviation of a population, setting the correction parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the corrected sample standard deviation, setting the correction parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction).
+*
 * @param x - input array
 * @param correction - degrees of freedom adjustment (default: 1.0)
 * @returns standard deviation

lib/node_modules/@stdlib/stats/array/stdevtk/README.md

@@ -163,6 +163,18 @@ console.log( v );
 
 <!-- /.examples -->
 
+* * *
+
+<section class="references">
+
+## References
+
+-   Ling, Robert F. 1974. "Comparison of Several Algorithms for Computing Sample Means and Variances." _Journal of the American Statistical Association_ 69 (348). American Statistical Association, Taylor & Francis, Ltd.: 859–66. doi:[10.2307/2286154][@ling:1974a].
+
+</section>
+
+<!-- /.references -->
+
 <!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
 
 <section class="related">
@@ -179,6 +191,8 @@ console.log( v );
 
 [@stdlib/array/base/accessor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/accessor
 
+[@ling:1974a]: https://doi.org/10.2307/2286154
+
 </section>
 
 <!-- /.links -->

lib/node_modules/@stdlib/stats/array/stdevtk/docs/types/index.d.ts

@@ -30,6 +30,10 @@ type InputArray = NumericArray | Collection<number> | AccessorArrayLike<number>;
 /**
 * Computes the standard deviation of an array using a one-pass textbook algorithm.
 *
+* ## Notes
+*
+* -   Setting the correction parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the standard deviation according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the standard deviation of a population, setting the correction parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the corrected sample standard deviation, setting the correction parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction).
+*
 * @param x - input array
 * @param correction - degrees of freedom adjustment (default: 1.0)
 * @returns standard deviation

lib/node_modules/@stdlib/stats/array/stdevwd/docs/types/index.d.ts

@@ -30,6 +30,10 @@ type InputArray = NumericArray | Collection<number> | AccessorArrayLike<number>;
 /**
 * Computes the standard deviation of an array using Welford's algorithm.
 *
+* ## Notes
+*
+* -   Setting the correction parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the standard deviation according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the standard deviation of a population, setting the correction parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the corrected sample standard deviation, setting the correction parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction).
+*
 * @param x - input array
 * @param correction - degrees of freedom adjustment (default: 1.0)
 * @returns standard deviation

lib/node_modules/@stdlib/stats/array/stdevyc/docs/types/index.d.ts

@@ -30,6 +30,10 @@ type InputArray = NumericArray | Collection<number> | AccessorArrayLike<number>;
 /**
 * Computes the standard deviation of an array using a one-pass algorithm proposed by Youngs and Cramer.
 *
+* ## Notes
+*
+* -   Setting the correction parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the standard deviation according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the standard deviation of a population, setting the correction parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the corrected sample standard deviation, setting the correction parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction).
+*
 * @param x - input array
 * @param correction - degrees of freedom adjustment (default: 1.0)
 * @returns standard deviation

lib/node_modules/@stdlib/stats/array/variancech/README.md

@@ -146,17 +146,17 @@ console.log( v );
 
 <section class="references">
 
-</section>
-
-<!-- /.references -->
-
 ## References
 
 -   Neely, Peter M. 1966. "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients." _Communications of the ACM_ 9 (7). Association for Computing Machinery: 496–99. doi:[10.1145/365719.365958][@neely:1966a].
 -   Ling, Robert F. 1974. "Comparison of Several Algorithms for Computing Sample Means and Variances." _Journal of the American Statistical Association_ 69 (348). American Statistical Association, Taylor & Francis, Ltd.: 859–66. doi:[10.2307/2286154][@ling:1974a].
 -   Chan, Tony F., Gene H. Golub, and Randall J. LeVeque. 1983. "Algorithms for Computing the Sample Variance: Analysis and Recommendations." _The American Statistician_ 37 (3). American Statistical Association, Taylor & Francis, Ltd.: 242–47. doi:[10.1080/00031305.1983.10483115][@chan:1983a].
 -   Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In _Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. New York, NY, USA: Association for Computing Machinery. doi:[10.1145/3221269.3223036][@schubert:2018a].
 
+</section>
+
+<!-- /.references -->
+
 <!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
 
 <section class="related">

lib/node_modules/@stdlib/stats/array/variancetk/README.md

@@ -114,6 +114,7 @@ var v = variancetk( x, 0.0 );
 -   If provided an empty array, the function returns `NaN`.
 -   If provided a `correction` argument which is greater than or equal to the number of elements in a provided input array, the function returns `NaN`.
 -   The function supports array-like objects having getter and setter accessors for array element access (e.g., [`@stdlib/array/base/accessor`][@stdlib/array/base/accessor]).
+-   Some caution should be exercised when using the one-pass textbook algorithm. Literature overwhelmingly discourages the algorithm's use for two reasons: 1) the lack of safeguards against underflow and overflow and 2) the risk of catastrophic cancellation when subtracting the two sums if the sums are large and the variance small. These concerns have merit; however, the one-pass textbook algorithm should not be dismissed outright. For data distributions with a moderately large standard deviation to mean ratio (i.e., **coefficient of variation**), the one-pass textbook algorithm may be acceptable, especially when performance is paramount and some precision loss is acceptable (including a risk of returning a negative variance due to floating-point rounding errors!). In short, no single "best" algorithm for computing the variance exists. The "best" algorithm depends on the underlying data distribution, your performance requirements, and your minimum precision requirements. When evaluating which algorithm to use, consider the relative pros and cons, and choose the algorithm which best serves your needs.
 
 </section>