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Copy file name to clipboardExpand all lines: CHANGELOG.md
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<sectionclass="release"id="unreleased">
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## Unreleased (2025-05-05)
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## Unreleased (2025-07-09)
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<sectionclass="features">
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### Features
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-[`5654424`](https://github.com/stdlib-js/stdlib/commit/5654424510f9b6d9273b5ce4c60f365761a53cca) - add support for accessor arrays and refactor `stats/base/nanstdevch`[(#7588)](https://github.com/stdlib-js/stdlib/pull/7588)
-[`5654424`](https://github.com/stdlib-js/stdlib/commit/5654424510f9b6d9273b5ce4c60f365761a53cca) - **feat:** add support for accessor arrays and refactor `stats/base/nanstdevch`[(#7588)](https://github.com/stdlib-js/stdlib/pull/7588)_(by Gururaj Gurram)_
Copy file name to clipboardExpand all lines: README.md
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var nanstdevch =require( '@stdlib/stats-base-nanstdevch' );
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```
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#### nanstdevch( N, correction, x, stride )
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#### nanstdevch( N, correction, x, strideX )
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Computes the [standard deviation][standard-deviation] of a strided array `x`ignoring `NaN` values and using a one-pass trial mean algorithm.
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Computes the [standard deviation][standard-deviation] of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm.
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```javascript
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var x = [ 1.0, -2.0, NaN, 2.0 ];
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-**N**: number of indexed elements.
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-**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).
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-**x**: input [`Array`][mdn-array] or [`typed array`][mdn-typed-array].
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-**stride**: index increment for `x`.
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-**strideX**: stride length for `x`.
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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`,
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```javascript
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var floor =require( '@stdlib/math-base-special-floor' );
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var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN ];
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varN=floor( x.length/2 );
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var v =nanstdevch( N, 1, x, 2 );
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var v =nanstdevch( 5, 1, x, 2 );
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// returns 2.5
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```
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Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.
var floor =require( '@stdlib/math-base-special-floor' );
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var x0 =newFloat64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
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var x0 =newFloat64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ] );
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var x1 =newFloat64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
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varN=floor( x0.length/2 );
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var v =nanstdevch( N, 1, x1, 2 );
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var v =nanstdevch( 5, 1, x1, 2 );
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// returns 2.5
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```
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#### nanstdevch.ndarray( N, correction, x, stride, offset )
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#### nanstdevch.ndarray( N, correction, x, strideX, offsetX )
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Computes the [standard deviation][standard-deviation] of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm and alternative indexing semantics.
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The function has the following additional parameters:
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-**offset**: starting index for `x`.
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-**offsetX**: starting index for `x`.
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While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying `buffer`, the `offset` parameter supports indexing semantics based on a starting index. For example, to calculate the [standard deviation][standard-deviation] for every other value in `x` starting from the second value
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While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameter supports indexing semantics based on a starting index. For example, to calculate the [standard deviation][standard-deviation] for every other element in `x` starting from the second element
var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
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varN=floor( x.length/2 );
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var v =nanstdevch.ndarray( N, 1, x, 2, 1 );
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var v =nanstdevch.ndarray( 5, 1, x, 2, 1 );
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// returns 2.5
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```
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- If `N <= 0`, both functions return `NaN`.
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- 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`.
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- 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).
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- Both functions support array-like objects having getter and setter accessors for array element access (e.g., [`@stdlib/array-base/accessor`][@stdlib/array/base/accessor]).
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- Depending on the environment, the typed versions ([`dnanstdevch`][@stdlib/stats/strided/dnanstdevch], [`snanstdevch`][@stdlib/stats/base/snanstdevch], etc.) are likely to be significantly more performant.
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</section>
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