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refactor: update paths
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‎lib/node_modules/@stdlib/namespace/lib/namespace/base/strided/d.js

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@@ -1420,7 +1420,7 @@ ns.push({
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'related': [
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'@stdlib/stats/strided/dvariancech',
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'@stdlib/stats/strided/dnanvariance',
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'@stdlib/stats/base/nanvariancech',
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'@stdlib/stats/strided/nanvariancech',
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'@stdlib/stats/base/snanvariancech'
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]
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});

‎lib/node_modules/@stdlib/namespace/lib/namespace/base/strided/n.js

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@@ -254,7 +254,7 @@ ns.push({
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'type': 'Function',
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'related': [
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'@stdlib/stats/strided/dnanstdevch',
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'@stdlib/stats/base/nanvariancech',
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'@stdlib/stats/strided/nanvariancech',
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'@stdlib/stats/base/nanstdev',
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'@stdlib/stats/base/snanstdevch',
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'@stdlib/stats/strided/stdevch'
@@ -332,8 +332,8 @@ ns.push({
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ns.push({
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'alias': 'base.strided.nanvariancech',
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'path': '@stdlib/stats/base/nanvariancech',
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'value': require( '@stdlib/stats/base/nanvariancech' ),
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'path': '@stdlib/stats/strided/nanvariancech',
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'value': require( '@stdlib/stats/strided/nanvariancech' ),
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'type': 'Function',
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'related': [
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'@stdlib/stats/strided/dnanvariancech',

‎lib/node_modules/@stdlib/namespace/lib/namespace/base/strided/s.js

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@@ -1389,7 +1389,7 @@ ns.push({
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'type': 'Function',
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'related': [
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'@stdlib/stats/strided/dnanvariancech',
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'@stdlib/stats/base/nanvariancech',
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'@stdlib/stats/strided/nanvariancech',
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'@stdlib/stats/base/snanstdevch',
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'@stdlib/stats/base/snanvariance',
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'@stdlib/stats/strided/svariancech'

‎lib/node_modules/@stdlib/namespace/lib/namespace/base/strided/v.js

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@@ -47,7 +47,7 @@ ns.push({
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'type': 'Function',
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'related': [
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'@stdlib/stats/strided/dvariancech',
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'@stdlib/stats/base/nanvariancech',
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'@stdlib/stats/strided/nanvariancech',
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'@stdlib/stats/strided/stdevch',
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'@stdlib/stats/strided/variance'
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]

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

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@@ -33,7 +33,7 @@ The population [variance][variance] of a finite size population of size `N` is g
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```
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<!-- <div class="equation" align="center" data-raw-text="\sigma^2 = \frac{1}{N} \sum_{i=0}^{N-1} (x_i - \mu)^2" data-equation="eq:population_variance">
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<img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@a42764beff9494374912d93a140da3491f12e2cc/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/img/equation_population_variance.svg" alt="Equation for the population variance.">
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<img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@a42764beff9494374912d93a140da3491f12e2cc/lib/node_modules/@stdlib/stats/strided/nanvariancech/docs/img/equation_population_variance.svg" alt="Equation for the population variance.">
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<br>
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</div> -->
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@@ -48,7 +48,7 @@ where the population mean is given by
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```
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<!-- <div class="equation" align="center" data-raw-text="\mu = \frac{1}{N} \sum_{i=0}^{N-1} x_i" data-equation="eq:population_mean">
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<img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@a42764beff9494374912d93a140da3491f12e2cc/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/img/equation_population_mean.svg" alt="Equation for the population mean.">
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<img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@a42764beff9494374912d93a140da3491f12e2cc/lib/node_modules/@stdlib/stats/strided/nanvariancech/docs/img/equation_population_mean.svg" alt="Equation for the population mean.">
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<br>
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</div> -->
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@@ -63,7 +63,7 @@ s^2 = \frac{1}{n-1} \sum_{i=0}^{n-1} (x_i - \bar{x})^2
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```
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<!-- <div class="equation" align="center" data-raw-text="s^2 = \frac{1}{n-1} \sum_{i=0}^{n-1} (x_i - \bar{x})^2" data-equation="eq:unbiased_sample_variance">
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<img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@a42764beff9494374912d93a140da3491f12e2cc/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/img/equation_unbiased_sample_variance.svg" alt="Equation for computing an unbiased sample variance.">
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<img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@a42764beff9494374912d93a140da3491f12e2cc/lib/node_modules/@stdlib/stats/strided/nanvariancech/docs/img/equation_unbiased_sample_variance.svg" alt="Equation for computing an unbiased sample variance.">
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<br>
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</div> -->
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```
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<!-- <div class="equation" align="center" data-raw-text="\bar{x} = \frac{1}{n} \sum_{i=0}^{n-1} x_i" data-equation="eq:sample_mean">
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<img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@a42764beff9494374912d93a140da3491f12e2cc/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/img/equation_sample_mean.svg" alt="Equation for the sample mean.">
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<img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@a42764beff9494374912d93a140da3491f12e2cc/lib/node_modules/@stdlib/stats/strided/nanvariancech/docs/img/equation_sample_mean.svg" alt="Equation for the sample mean.">
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<br>
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</div> -->
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‎lib/node_modules/@stdlib/stats/array/nanvariancech/lib/main.js

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@@ -26,7 +26,7 @@ var dtypes = require( '@stdlib/array/dtypes' );
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var dtype = require( '@stdlib/array/dtype' );
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var contains = require( '@stdlib/array/base/assert/contains' );
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var join = require( '@stdlib/array/base/join' );
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var strided = require( '@stdlib/stats/base/nanvariancech' ).ndarray;
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var strided = require( '@stdlib/stats/strided/nanvariancech' ).ndarray;
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var format = require( '@stdlib/string/format' );
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‎lib/node_modules/@stdlib/stats/base/README.md

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@@ -73,7 +73,7 @@ The namespace contains the following statistical functions:
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- <span class="signature">[`nanstdevwd( N, correction, x, stride )`][@stdlib/stats/base/nanstdevwd]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array ignoring `NaN` values and using Welford's algorithm.</span>
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- <span class="signature">[`nanstdevyc( N, correction, x, strideX )`][@stdlib/stats/base/nanstdevyc]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array ignoring `NaN` values and using a one-pass algorithm proposed by Youngs and Cramer.</span>
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- <span class="signature">[`nanvariance( N, correction, x, strideX )`][@stdlib/stats/base/nanvariance]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring `NaN` values.</span>
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- <span class="signature">[`nanvariancech( N, correction, x, strideX )`][@stdlib/stats/base/nanvariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm.</span>
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- <span class="signature">[`nanvariancech( N, correction, x, strideX )`][@stdlib/stats/strided/nanvariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm.</span>
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- <span class="signature">[`nanvariancepn( N, correction, x, strideX )`][@stdlib/stats/strided/nanvariancepn]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring `NaN` values and using a two-pass algorithm.</span>
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- <span class="signature">[`nanvariancetk( N, correction, x, strideX )`][@stdlib/stats/strided/nanvariancetk]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring `NaN` values and using a one-pass textbook algorithm.</span>
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- <span class="signature">[`nanvariancewd( N, correction, x, strideX )`][@stdlib/stats/strided/nanvariancewd]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring `NaN` values and using Welford's algorithm.</span>
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[@stdlib/stats/base/nanvariance]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/nanvariance
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[@stdlib/stats/base/nanvariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/nanvariancech
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[@stdlib/stats/strided/nanvariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/nanvariancech
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[@stdlib/stats/strided/nanvariancepn]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/nanvariancepn
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‎lib/node_modules/@stdlib/stats/base/nanstdevch/README.md

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## See Also
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- <span class="package-name">[`@stdlib/stats/strided/dnanstdevch`][@stdlib/stats/strided/dnanstdevch]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a double-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.</span>
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- <span class="package-name">[`@stdlib/stats/base/nanvariancech`][@stdlib/stats/base/nanvariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring NaN values and using a one-pass trial mean algorithm.</span>
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- <span class="package-name">[`@stdlib/stats/strided/nanvariancech`][@stdlib/stats/strided/nanvariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring NaN values and using a one-pass trial mean algorithm.</span>
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- <span class="package-name">[`@stdlib/stats/base/nanstdev`][@stdlib/stats/base/nanstdev]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array ignoring NaN values.</span>
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- <span class="package-name">[`@stdlib/stats/base/snanstdevch`][@stdlib/stats/base/snanstdevch]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.</span>
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- <span class="package-name">[`@stdlib/stats/strided/stdevch`][@stdlib/stats/strided/stdevch]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a strided array using a one-pass trial mean algorithm.</span>
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[@stdlib/stats/strided/dnanstdevch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanstdevch
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[@stdlib/stats/base/nanvariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/nanvariancech
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[@stdlib/stats/strided/nanvariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/nanvariancech
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[@stdlib/stats/base/nanstdev]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/nanstdev
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‎lib/node_modules/@stdlib/stats/base/nanstdevch/lib/ndarray.js

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// MODULES //
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var nanvariancech = require( '@stdlib/stats/base/nanvariancech' ).ndarray;
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var nanvariancech = require( '@stdlib/stats/strided/nanvariancech' ).ndarray;
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var sqrt = require( '@stdlib/math/base/special/sqrt' );
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‎lib/node_modules/@stdlib/stats/base/snanvariancech/README.md

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## See Also
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- <span class="package-name">[`@stdlib/stats/strided/dnanvariancech`][@stdlib/stats/strided/dnanvariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a double-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.</span>
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- <span class="package-name">[`@stdlib/stats/base/nanvariancech`][@stdlib/stats/base/nanvariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring NaN values and using a one-pass trial mean algorithm.</span>
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- <span class="package-name">[`@stdlib/stats/strided/nanvariancech`][@stdlib/stats/strided/nanvariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a strided array ignoring NaN values and using a one-pass trial mean algorithm.</span>
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- <span class="package-name">[`@stdlib/stats/base/snanstdevch`][@stdlib/stats/base/snanstdevch]</span><span class="delimiter">: </span><span class="description">calculate the standard deviation of a single-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.</span>
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- <span class="package-name">[`@stdlib/stats/base/snanvariance`][@stdlib/stats/base/snanvariance]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array ignoring NaN values.</span>
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- <span class="package-name">[`@stdlib/stats/strided/svariancech`][@stdlib/stats/strided/svariancech]</span><span class="delimiter">: </span><span class="description">calculate the variance of a single-precision floating-point strided array using a one-pass trial mean algorithm.</span>
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[@stdlib/stats/strided/dnanvariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanvariancech
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[@stdlib/stats/base/nanvariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/nanvariancech
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[@stdlib/stats/strided/nanvariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/nanvariancech
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[@stdlib/stats/base/snanstdevch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/snanstdevch
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