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feat: add stats/base/ndarray/sdsnanmeanors
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‎lib/node_modules/@stdlib/stats/base/ndarray/sdsnanmeanors/README.md‎

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<!--
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@license Apache-2.0
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Copyright (c) 2025 The Stdlib Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# sdsnanmeanors
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> Compute the [arithmetic mean][arithmetic-mean] of a one-dimensional single-precision floating-point ndarray, ignoring NaN values and using ordinary recursive summation with extended accumulation.
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<section class="intro">
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The [arithmetic mean][arithmetic-mean] is defined as
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<!-- <equation class="equation" label="eq:arithmetic_mean" align="center" raw="\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i" alt="Equation for the arithmetic mean."> -->
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```math
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\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i
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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:arithmetic_mean">
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<img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@42d8f64d805113ab899c79c7c39d6c6bac7fe25c/lib/node_modules/@stdlib/stats/base/ndarray/mean/docs/img/equation_arithmetic_mean.svg" alt="Equation for the arithmetic mean.">
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<br>
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</div> -->
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<!-- </equation> -->
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</section>
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<!-- /.intro -->
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<section class="usage">
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## Usage
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```javascript
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var sdsnanmeanors = require( '@stdlib/stats/base/ndarray/sdsnanmeanors' );
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```
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#### sdsnanmeanors( arrays )
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Computes the [arithmetic mean][arithmetic-mean] of a one-dimensional single-precision floating-point ndarray, ignoring NaN values and using ordinary recursive summation with extended accumulation.
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```javascript
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var Float32Array = require( '@stdlib/array/float32' );
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var ndarray = require( '@stdlib/ndarray/base/ctor' );
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var xbuf = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
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var x = new ndarray( 'float32', xbuf, [ 4 ], [ 1 ], 0, 'row-major' );
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var v = sdsnanmeanors( [ x ] );
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// returns ~0.3333
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```
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The function has the following parameters:
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- **arrays**: array-like object containing a one-dimensional input ndarray.
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</section>
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<!-- /.usage -->
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<section class="notes">
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## Notes
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- If provided an empty one-dimensional ndarray, the function returns `NaN`.
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- If every indexed element is `NaN`, the function returns `NaN`.
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- Accumulated intermediate values are stored as double-precision floating-point numbers.
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</section>
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<!-- /.notes -->
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<section class="examples">
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## Examples
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<!-- eslint no-undef: "error" -->
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```javascript
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var uniform = require( '@stdlib/random/base/uniform' );
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var filledarrayBy = require( '@stdlib/array/filled-by' );
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var bernoulli = require( '@stdlib/random/base/bernoulli' );
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var ndarray = require( '@stdlib/ndarray/base/ctor' );
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var ndarray2array = require( '@stdlib/ndarray/to-array' );
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var sdsnanmeanors = require( '@stdlib/stats/base/ndarray/sdsnanmeanors' );
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function rand() {
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if ( bernoulli( 0.8 ) < 1 ) {
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return NaN;
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}
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return uniform( -50.0, 50.0 );
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}
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var xbuf = filledarrayBy( 10, 'float32', rand );
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var x = new ndarray( 'float32', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
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console.log( ndarray2array( x ) );
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var v = sdsnanmeanors( [ x ] );
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console.log( v );
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```
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</section>
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<!-- /.examples -->
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<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
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<section class="related">
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</section>
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<!-- /.related -->
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<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="links">
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[arithmetic-mean]: https://en.wikipedia.org/wiki/Arithmetic_mean
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/stats/base/ndarray/sdsnanmeanors/benchmark/benchmark.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2025 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var bench = require( '@stdlib/bench' );
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var uniform = require( '@stdlib/random/array/uniform' );
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var bernoulli = require( '@stdlib/random/base/bernoulli' );
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var isnanf = require( '@stdlib/math/base/assert/is-nanf' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var ndarray = require( '@stdlib/ndarray/base/ctor' );
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var pkg = require( './../package.json' ).name;
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var sdsnanmeanors = require( './../lib' );
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// VARIABLES //
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var options = {
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'dtype': 'float32'
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};
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// FUNCTIONS //
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/**
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* Creates a benchmark function.
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*
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* @private
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* @param {PositiveInteger} len - array length
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* @returns {Function} benchmark function
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*/
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function createBenchmark( len ) {
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var xbuf;
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var x;
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var i;
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xbuf = uniform( len, -10.0, 10.0, options );
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for ( i = 0; i < len; i++ ) {
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if ( bernoulli( 0.8 ) < 1 ) {
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xbuf[ i ] = NaN;
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}
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}
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x = new ndarray( options.dtype, xbuf, [ len ], [ 1 ], 0, 'row-major' );
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return benchmark;
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function benchmark( b ) {
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var v;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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v = sdsnanmeanors( [ x ] );
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if ( isnanf( v ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnanf( v ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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}
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}
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// MAIN //
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/**
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* Main execution sequence.
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*
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* @private
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*/
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function main() {
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var len;
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var min;
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var max;
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var f;
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var i;
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min = 1; // 10^min
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max = 6; // 10^max
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for ( i = min; i <= max; i++ ) {
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len = pow( 10, i );
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f = createBenchmark( len );
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bench( pkg+':len='+len, f );
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}
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}
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main();

‎lib/node_modules/@stdlib/stats/base/ndarray/sdsnanmeanors/docs/img/equation_arithmetic_mean.svg‎

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‎lib/node_modules/@stdlib/stats/base/ndarray/sdsnanmeanors/docs/repl.txt‎

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{{alias}}( arrays )
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Computes the arithmetic mean of a one-dimensional single-precision
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floating-point ndarray, ignoring NaN values and using ordinary recursive
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summation with extended accumulation.
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Accumulated intermediate values are stored as double-precision floating-
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point numbers.
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If provided an empty ndarray, the function returns `NaN`.
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If every indexed element is `NaN`, the function returns `NaN`.
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Parameters
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----------
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arrays: ArrayLikeObject<ndarray>
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Array-like object containing a one-dimensional input ndarray.
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Returns
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-------
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out: number
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Arithmetic mean.
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Examples
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--------
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> var xbuf = new {{alias:@stdlib/array/float32}}( [ 1.0, -2.0, NaN, 2.0 ] );
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> var dt = 'float32';
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> var sh = [ xbuf.length ];
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> var sx = [ 1 ];
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> var ox = 0;
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> var ord = 'row-major';
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> var x = new {{alias:@stdlib/ndarray/ctor}}( dt, xbuf, sh, sx, ox, ord );
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> {{alias}}( [ x ] )
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~0.3333
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See Also
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--------
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‎lib/node_modules/@stdlib/stats/base/ndarray/sdsnanmeanors/docs/types/index.d.ts‎

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/*
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* @license Apache-2.0
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*
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* Copyright (c) 2025 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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// TypeScript Version: 4.1
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/// <reference types="@stdlib/types"/>
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import { float32ndarray } from '@stdlib/types/ndarray';
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/**
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* Computes the arithmetic mean of a one-dimensional single-precision floating-point ndarray, ignoring NaN values and using ordinary recursive summation with extended accumulation.
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*
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* @param arrays - array-like object containing an input ndarray
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* @returns arithmetic mean
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*
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* @example
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* var Float32Array = require( '@stdlib/array/float32' );
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* var ndarray = require( '@stdlib/ndarray/base/ctor' );
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*
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* var xbuf = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
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* var x = new ndarray( 'float32', xbuf, [ 4 ], [ 1 ], 0, 'row-major' );
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*
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* var v = sdsnanmeanors( [ x ] );
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* // returns ~0.3333
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*/
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declare function sdsnanmeanors( arrays: [ float32ndarray ] ): number;
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// EXPORTS //
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export = sdsnanmeanors;

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