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Merge branch 'develop' of https://github.com/stdlib-js/stdlib into develop
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‎lib/node_modules/@stdlib/math/base/tools/README.md‎

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// returns {...}
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```
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<!-- <toc pattern="*"> -->
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<div class="namespace-toc">
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- <span class="signature">[`continuedFraction( generator[, options ] )`][@stdlib/math/base/tools/continued-fraction]</span><span class="delimiter">: </span><span class="description">continued fraction approximation.</span>
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- <span class="signature">[`evalpoly( c, x )`][@stdlib/math/base/tools/evalpoly]</span><span class="delimiter">: </span><span class="description">evaluate a polynomial.</span>
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- <span class="signature">[`evalrational( P, Q, x )`][@stdlib/math/base/tools/evalrational]</span><span class="delimiter">: </span><span class="description">evaluate a rational function.</span>
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- <span class="signature">[`fibpoly( n, x )`][@stdlib/math/base/tools/fibpoly]</span><span class="delimiter">: </span><span class="description">evaluate a Fibonacci polynomial.</span>
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- <span class="signature">[`lucaspoly( n, x )`][@stdlib/math/base/tools/lucaspoly]</span><span class="delimiter">: </span><span class="description">evaluate a Lucas polynomial.</span>
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- <span class="signature">[`sumSeries( generator[, options ] )`][@stdlib/math/base/tools/sum-series]</span><span class="delimiter">: </span><span class="description">compute the sum of an infinite series.</span>
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</div>
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<!-- </toc> -->
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</section>
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<!-- /.usage -->
@@ -64,6 +79,22 @@ console.log( getKeys( tools ) );
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<section class="links">
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<!-- <toc-links> -->
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[@stdlib/math/base/tools/continued-fraction]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/base/tools/continued-fraction
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[@stdlib/math/base/tools/evalpoly]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/base/tools/evalpoly
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[@stdlib/math/base/tools/evalrational]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/base/tools/evalrational
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[@stdlib/math/base/tools/fibpoly]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/base/tools/fibpoly
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[@stdlib/math/base/tools/lucaspoly]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/base/tools/lucaspoly
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[@stdlib/math/base/tools/sum-series]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/base/tools/sum-series
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<!-- </toc-links> -->
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/math/stats/README.md‎

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// returns {...}
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```
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The namespace exposes the following statistical tests:
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<!-- <toc pattern="+(*test*|chi2gof|anova1)"> -->
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<div class="namespace-toc">
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- <span class="signature">[`anova1( x, factor[, opts] )`][@stdlib/math/stats/anova1]</span><span class="delimiter">: </span><span class="description">perform a one-way analysis of variance.</span>
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- <span class="signature">[`chi2gof( x, y[, ...params][, opts] )`][@stdlib/math/stats/chi2gof]</span><span class="delimiter">: </span><span class="description">perform a chi-square goodness-of-fit test.</span>
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- <span class="signature">[`kstest( x, y[, ...params][, opts] )`][@stdlib/math/stats/kstest]</span><span class="delimiter">: </span><span class="description">one-sample Kolmogorov-Smirnov goodness-of-fit test.</span>
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- <span class="signature">[`pcorrtest( x, y[, opts] )`][@stdlib/math/stats/pcorrtest]</span><span class="delimiter">: </span><span class="description">compute a Pearson product-moment correlation test between paired samples.</span>
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- <span class="signature">[`ttest( x[, y][, opts] )`][@stdlib/math/stats/ttest]</span><span class="delimiter">: </span><span class="description">one-sample and paired Student's t-Test.</span>
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- <span class="signature">[`ttest2( x, y[, opts] )`][@stdlib/math/stats/ttest2]</span><span class="delimiter">: </span><span class="description">two-sample Student's t-Test.</span>
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- <span class="signature">[`ztest( x, sigma[, opts] )`][@stdlib/math/stats/ztest]</span><span class="delimiter">: </span><span class="description">one-sample z-Test.</span>
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- <span class="signature">[`ztest2( x, y, sigmax, sigmay[, opts] )`][@stdlib/math/stats/ztest2]</span><span class="delimiter">: </span><span class="description">two-sample z-Test.</span>
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</div>
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<!-- </toc> -->
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In addition, it contains an assortment of functions for computing statistics incrementally as part of the `incr` sub-namespace.
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Other statistical functions included are:
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<!-- <toc pattern="*" ignore="+(*test*|chi2gof|anova1)" ignore="incr"> -->
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<div class="namespace-toc">
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- <span class="signature">[`lowess( x, y[, opts] )`][@stdlib/math/stats/lowess]</span><span class="delimiter">: </span><span class="description">locally-weighted polynomial regression via the LOWESS algorithm.</span>
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</div>
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<!-- </toc> -->
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</section>
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<!-- /.usage -->
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<section class="links">
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<!-- <toc-links> -->
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[@stdlib/math/stats/lowess]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/stats/lowess
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[@stdlib/math/stats/anova1]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/stats/anova1
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[@stdlib/math/stats/chi2gof]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/stats/chi2gof
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[@stdlib/math/stats/kstest]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/stats/kstest
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[@stdlib/math/stats/pcorrtest]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/stats/pcorrtest
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[@stdlib/math/stats/ttest]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/stats/ttest
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[@stdlib/math/stats/ttest2]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/stats/ttest2
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[@stdlib/math/stats/ztest]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/stats/ztest
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[@stdlib/math/stats/ztest2]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/math/stats/ztest2
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<!-- </toc-links> -->
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/math/stats/anova1/README.md‎

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var anova1 = require( '@stdlib/math/stats/anova1' );
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```
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### anova1( x, factor\[, opts] )
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#### anova1( x, factor\[, opts] )
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For an [array][mdn-array] or [typed array][mdn-typed-array] of numeric values `x` and an [array][mdn-array] of classifications `factor`, a one-way analysis of variance is performed. The hypotheses are given as follows:
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y = [ 'Treatment A', 'Treatment B', 'Treatment C', 'Control', 'Treatment A', 'Treatment B', 'Treatment C', 'Control', 'Treatment A', 'Treatment B', 'Treatment C', 'Control' ];
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// Making the object
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out = anova1(x, y);
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out = anova1( x, y );
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/* returns
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{ 'treatmentDf': 11,
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'treatmentSS': 15,
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y = [ 'Treatment A', 'Treatment B', 'Treatment C', 'Control', 'Treatment A', 'Treatment B', 'Treatment C', 'Control', 'Treatment A', 'Treatment B', 'Treatment C', 'Control' ];
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// Making the object
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out = anova1(x, y);
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out = anova1( x, y );
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console.log( out.print() );
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/* =>
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One-Way anova1

‎lib/node_modules/@stdlib/math/stats/anova1/docs/repl.txt‎

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11

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{{alias}}( x, factor[, opts] )
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{{alias}}( x, factor[, options] )
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Performs a one-way analysis of variance.
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Parameters
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----------
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x: Array<number>
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Measured values.
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y: Array
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factor: Array
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Array of treatments.
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options: Object (optional)
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Test result object.
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out.alpha: number
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Used significance level.
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Significance level.
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out.rejected: boolean
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Test decision.
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out.pValue: number
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P-value of the test.
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p-value of the test.
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Value of test statistic.
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Error degrees of freedom.
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out.treatmentSS: number
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out.errorSS: number
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Error sum of squares.
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... 'control', 'treatA', 'treatB', 'treatC', 'control',
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... ];
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> var out = {{alias}}(x, f);
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> var out = {{alias}}( x, f )
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{...}
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See Also

‎lib/node_modules/@stdlib/math/stats/anova1/lib/anova1.js‎

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var validate = require( './validate.js' );
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var unique = require( './unique.js' );
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var meanTable = require( './mean_table.js' );
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var mean = require( './mean.js' );
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var nGroups;
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var nobs;
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// Initialize all vars
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sumSqTotal = 0.0;
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throw new RangeError( 'invalid argument. First argument must have at least two elements. Value: `' + x + '`.' );
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// Check if factor is an array
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// Check if the arrays are of the same length.
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means = meanTable( x, factor );
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// Now get total ss
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// Now get total ss:
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meanSumSqError = sumSqError / ( x.length - treats.length );
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meanSumSqTreat = ssTreatment / ( nGroups - 1 );
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fScore = meanSumSqTreat / meanSumSqError;
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setReadOnly( out, 'treatmentDf', treats.length - 1 );
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setReadOnly( out, 'treatmentDf', nGroups - 1 );
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setReadOnly( out, 'errorDf', nobs - nGroups );
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‎lib/node_modules/@stdlib/math/stats/anova1/lib/index.js‎

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* 'errorMSS': ~23.1667,
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* 'means': { 'control': { 'mean': 5, 'sampleSize: 3, 'SD': ~4.583 },
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* 'treatA': { 'mean': ~6.667, 'sampleSize': 3, 'SD': ~4.041 },
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* 'treatB': { 'mean': ~8.333, 'sampleSize': 3, 'SD': ~3.512 },
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* 'treatC': { 'mean': 8, 'sampleSize': 3, 'SD': ~6.557 }
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* 'means': { 'control': { 'mean': 5.0, 'sampleSize: 3.0, 'SD': ~4.583 },
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* 'treatA': { 'mean': ~6.667, 'sampleSize': 3.0, 'SD': ~4.041 },
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* 'treatB': { 'mean': ~8.333, 'sampleSize': 3.0, 'SD': ~3.512 },
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* 'treatC': { 'mean': 8.0, 'sampleSize': 3.0, 'SD': ~6.557 }
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‎lib/node_modules/@stdlib/math/stats/anova1/lib/mean.js‎

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'use strict';
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/**
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* Computes the arithmetic mean of a numeric array.
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*
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* @param {NumericArray} arr - input array
7+
* @returns {number} arithmetic mean
8+
*/
9+
function mean( arr ) {
10+
var delta;
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var len;
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var mu;
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var i;
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mu = 0;
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len = arr.length;
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for ( i = 0; i < len; i++ ) {
18+
delta = arr[ i ] - mu;
19+
mu += delta / (i+1);
20+
}
21+
return mu;
22+
}
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// EXPORTS //
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module.exports = mean;

‎lib/node_modules/@stdlib/math/stats/chi2gof/README.md‎

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var chi2gof = require( '@stdlib/math/stats/chi2gof' );
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```
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### chi2gof( x, y\[, ...params]\[, opts] )
33+
#### chi2gof( x, y\[, ...params]\[, opts] )
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For an [array][mdn-array] or [typed array][mdn-typed-array] of integers `x`, a chi-square goodness-of-fit is computed for the null hypothesis that the values of `x` come from the discrete distribution specified by `y`. `y` can be an `array` of expected frequencies, an `array` of population probabilities that sum to one, or a string with the name of the discrete distribution to test against. In the latter case, the parameters of the distribution must be supplied as additional arguments after `y`. The function returns an object holding the calculated test statistic, the p-value of the test, as well as the test decision.
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‎lib/node_modules/@stdlib/math/stats/chi2gof/docs/repl.txt‎

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Test decision.
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P-value of the test.
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p-value of the test.
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7878
out.statistic: number
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Value of test statistic.

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