<section class="release" id="unreleased">

<section class="commits">

<details>

- [`cfd273d`](https://github.com/stdlib-js/stdlib/commit/cfd273df8e26730ad7a331e87430272cbf91c10a) - **refactor:** update paths _(by Aayush Khanna)_

</details>

- Aayush Khanna

</section>

Anshu Kumar <132515490+anxhukumar@users.noreply.github.com>

Anshu Kumar <contact.anshukumar@protonmail.com>

Anudeep Sanapala <anudeep0306@gmail.com>

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Athan Reines <kgryte@gmail.com>

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Chinmay Joshi <86140365+JawHawk@users.noreply.github.com>

Christopher Dambamuromo <chridam@gmail.com>

Dan Rose <danoftheroses@gmail.com>

Daniel Killenberger <daniel.killenberger@gmail.com>

Daniel Yu <40680511+Daniel777y@users.noreply.github.com>

Debashis Maharana <debashismaharana7854@gmail.com>

Golden Kumar <103646877+AuenKr@users.noreply.github.com>

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Haroon Rasheed <51189276+haroon26@users.noreply.github.com>

Harsh <harshyadav6078@gmail.com>

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Yernar Yergaziyev <yernar.yergaziyev@erg.kz>

Yugal Kaushik <yugalkaushik14@gmail.com>

Yuvi Mittal <128018763+yuvi-mittal@users.noreply.github.com>

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fadiothman22 <48636283+fadiothman22@users.noreply.github.com>

iraandrushko <71790513+iraandrushko@users.noreply.github.com>

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var nanstdevtk = require( '@stdlib/stats-base-nanstdevtk' );

var x = [ 1.0, -2.0, NaN, 2.0 ];

- **N**: number of indexed elements.

- **correction**: degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the [standard deviation][standard-deviation] according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the [standard deviation][standard-deviation] of a population, setting this 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][standard-deviation], setting this 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).

- **x**: input [`Array`][mdn-array] or [`typed array`][mdn-typed-array].

The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the [standard deviation][standard-deviation] of every other element in `x`,

var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN ];

Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.

var Float64Array = require( '@stdlib/array-float64' );

var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

Computes the [standard deviation][standard-deviation] of a strided array ignoring `NaN` values and using a one-pass textbook algorithm and alternative indexing semantics.

The function has the following additional parameters:

- 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 and `n` corresponds to the number of non-`NaN` indexed elements), both functions return `NaN`.

- 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 computing a negative variance due to floating-point rounding errors!). In short, no single "best" algorithm for computing the standard deviation 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.

- Depending on the environment, the typed versions ([`dnanstdevtk`][@stdlib/stats/strided/dnanstdevtk], [`snanstdevtk`][@stdlib/stats/base/snanstdevtk], etc.) are likely to be significantly more performant.

</section>

var nanstdevtk = require( '@stdlib/stats-base-nanstdevtk' );

}

console.log( x );

var v = nanstdevtk( x.length, 1, x, 1 );

[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray

[@ling:1974a]: https://doi.org/10.2307/2286154

var bench = require( '@stdlib/bench-harness' );

var isnan = require( '@stdlib/math-base-assert-is-nan' );

var pow = require( '@stdlib/math-base-special-pow' );

var pkg = require( './../package.json' ).name;

function createBenchmark( len ) {

return benchmark;

function benchmark( b ) {

var bench = require( '@stdlib/bench-harness' );

var isnan = require( '@stdlib/math-base-assert-is-nan' );

var pow = require( '@stdlib/math-base-special-pow' );

var pkg = require( './../package.json' ).name;

function createBenchmark( len ) {

return benchmark;

function benchmark( b ) {