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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 (2025-03-29)
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## Unreleased (2025-04-02)
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<sectionclass="features">
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### Features
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-[`41d0632`](https://github.com/stdlib-js/stdlib/commit/41d0632145a36b71711b27c0ee7add3c0434c7f5) - refactor and add protocol support to `stats/base/nanvariancewd`[(#6023)](https://github.com/stdlib-js/stdlib/pull/6023)
Copy file name to clipboardExpand all lines: README.md
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var nanvariancewd =require( '@stdlib/stats-base-nanvariancewd' );
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```
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#### nanvariancewd( N, correction, x, stride )
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#### nanvariancewd( N, correction, x, strideX )
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Computes the [variance][variance] of a strided array `x`ignoring `NaN` values and using Welford's algorithm.
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Computes the [variance][variance] of a strided array ignoring `NaN` values and using Welford's 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 [variance][variance] according to `n-c` where `c` corresponds to the provided degrees of freedom adjustment and `n` corresponds to the number of non-`NaN` indexed elements. When computing the [variance][variance] 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 unbiased sample [variance][variance], 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 [variance][variance] of every other element in `x`,
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The `N` and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the [variance][variance] 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 =nanvariancewd( N, 1, x, 2 );
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var v =nanvariancewd( 5, 1, x, 2 );
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// returns 6.25
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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 =nanvariancewd( N, 1, x1, 2 );
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var v =nanvariancewd( 5, 1, x1, 2 );
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// returns 6.25
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```
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#### nanvariancewd.ndarray( N, correction, x, stride, offset )
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#### nanvariancewd.ndarray( N, correction, x, strideX, offsetX )
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Computes the [variance][variance] of a strided array ignoring `NaN` values and using Welford's 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 [variance][variance] 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 [variance][variance] for every other element in the strided array 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 =nanvariancewd.ndarray( N, 1, x, 2, 1 );
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var v =nanvariancewd.ndarray( 5, 1, x, 2, 1 );
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// returns 6.25
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```
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## Notes
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- If `N <= 0`, both functions return `NaN`.
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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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- 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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- Depending on the environment, the typed versions ([`dnanvariancewd`][@stdlib/stats/strided/dnanvariancewd], [`snanvariancewd`][@stdlib/stats/base/snanvariancewd], etc.) are likely to be significantly more performant.
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<!-- eslint no-undef: "error" -->
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```javascript
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var randu =require( '@stdlib/random-base-randu' );
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var round =require( '@stdlib/math-base-special-round' );
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