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‎.github/.keepalive

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‎CHANGELOG.md

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<section class="release" id="unreleased">
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## Unreleased (2025-04-07)
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## Unreleased (2025-04-29)
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<section class="features">
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@@ -34,6 +34,7 @@ This release closes the following issue:
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<details>
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- [`c1acd4b`](https://github.com/stdlib-js/stdlib/commit/c1acd4bf3ed08b40d6d4e93ee245f03c9eef8472) - **bench:** update random value generation [(#6858)](https://github.com/stdlib-js/stdlib/pull/6858) _(by Harsh, Athan Reines)_
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- [`a1e230f`](https://github.com/stdlib-js/stdlib/commit/a1e230f29297caa89880e9c194c615a0400fb7bc) - **chore:** clean up cppcheck-suppress comments _(by Karan Anand)_
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- [`d53a818`](https://github.com/stdlib-js/stdlib/commit/d53a8184c029c3df7c45a7a1a2da9ff90b4f883e) - **docs:** fix errors in the structure of READMEs in `stats/base/*` [(#5138)](https://github.com/stdlib-js/stdlib/pull/5138) _(by Aayush Khanna)_
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- [`af55f0d`](https://github.com/stdlib-js/stdlib/commit/af55f0d6d6b4d06c36f46357740ea89a4639ab5b) - **bench:** refactor random number generation in `stats/base/dists/binomial` [(#4841)](https://github.com/stdlib-js/stdlib/pull/4841) _(by Karan Anand)_
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### Contributors
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A total of 5 people contributed to this release. Thank you to the following contributors:
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A total of 6 people contributed to this release. Thank you to the following contributors:
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- Aayush Khanna
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- Athan Reines
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- Harsh
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- Karan Anand
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- Philipp Burckhardt
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- Prashant Kumar Yadav

‎CONTRIBUTORS

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@@ -51,6 +51,7 @@ Dorrin Sotoudeh <[email protected]>
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EuniceSim142 <[email protected]>
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Frank Kovacs <[email protected]>
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GK Bishnoi <[email protected]>
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GURU PRASAD SHARMA <[email protected]>
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Gaurav <[email protected]>
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Gautam sharma <[email protected]>
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Golden Kumar <[email protected]>
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Justin Dennison <[email protected]>
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Justyn Shelby <[email protected]>
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Karan Anand <[email protected]>
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Karan Yadav <[email protected]>
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Karthik Prakash <[email protected]>
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Kaushikgtm <[email protected]>
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Kavyansh-Bagdi <[email protected]>
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Kshitij-Dale <[email protected]>
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Lovelin Dhoni J B <[email protected]>
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MANI <[email protected]>
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Mahfuza Humayra Mohona <[email protected]>
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Manik Sharma <[email protected]>
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Manvith M <[email protected]>
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Marcus Fantham <[email protected]>
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Neeraj Pathak <[email protected]>
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NirvedMishra <[email protected]>
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Nishant Shinde <[email protected]>
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Nishant singh <[email protected]>
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Nishchay Rajput <[email protected]>
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Nithin Katta <[email protected]>
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Nourhan Hasan <[email protected]>
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PrathamBhamare <[email protected]>
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Pratik Singh <[email protected]>
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Pratyush Kumar Chouhan <[email protected]>
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Pravesh Kunwar <[email protected]>
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Priyansh Prajapati <[email protected]>
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Priyanshu Agarwal <[email protected]>
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Pulkit Gupta <[email protected]>
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Yernar Yergaziyev <[email protected]>
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Yugal Kaushik <[email protected]>
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Yuvi Mittal <[email protected]>
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devshree-bhati <[email protected]>
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ditsu <[email protected]>
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ekambains <[email protected]>
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fadiothman22 <[email protected]>
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iraandrushko <[email protected]>
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lohithganni <[email protected]>
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olenkabilonizhka <[email protected]>
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pranav-1720 <[email protected]>

‎benchmark/benchmark.js

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// MODULES //
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var bench = require( '@stdlib/bench-harness' );
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var Int32Array = require( '@stdlib/array-int32' );
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var Float64Array = require( '@stdlib/array-float64' );
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var uniform = require( '@stdlib/random-base-uniform' );
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var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
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var uniform = require( '@stdlib/random-array-uniform' );
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var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
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var isnan = require( '@stdlib/math-base-assert-is-nan' );
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var pkg = require( './../package.json' ).name;
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var variance = require( './../lib' );
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// MAIN //
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bench( pkg, function benchmark( b ) {
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var len;
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var n;
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var p;
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var y;
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var i;
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len = 100;
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n = new Int32Array( len );
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p = new Float64Array( len );
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for ( i = 0; i < len; i++ ) {
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n[ i ] = discreteUniform( 1, 100 );
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p[ i ] = uniform( 0.0, 1.0 );
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}
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n = discreteUniform( 100, 1, 100, {
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'dtype': 'int32'
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});
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p = uniform( 100, 0.0, 1.0, {
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'dtype': 'float64'
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});
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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y = variance( n[ i % len ], p[ i % len ] );
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y = variance( n[ i % n.length ], p[ i % p.length ] );
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if ( isnan( y ) ) {
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b.fail( 'should not return NaN' );
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}

‎benchmark/benchmark.native.js

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var resolve = require( 'path' ).resolve;
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var bench = require( '@stdlib/bench-harness' );
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var Int32Array = require( '@stdlib/array-int32' );
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var Float64Array = require( '@stdlib/array-float64' );
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var tryRequire = require( '@stdlib/utils-try-require' );
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var uniform = require( '@stdlib/random-base-uniform' );
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var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
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var uniform = require( '@stdlib/random-array-uniform' );
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var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
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var isnan = require( '@stdlib/math-base-assert-is-nan' );
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var pkg = require( './../package.json' ).name;
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// MAIN //
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bench( pkg+'::native', opts, function benchmark( b ) {
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var len;
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var p;
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var y;
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len = 100;
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n = new Int32Array( len );
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p = new Float64Array( len );
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for ( i = 0; i < len; i++ ) {
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n[ i ] = discreteUniform( 1, 100 );
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p[ i ] = uniform( 0.0, 1.0 );
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}
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n = discreteUniform( 100, 1, 100, {
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'dtype': 'int32'
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});
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p = uniform( 100, 0.0, 1.0, {
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'dtype': 'float64'
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});
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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y = variance( n[ i % len ], p[ i % len ] );
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y = variance( n[ i % n.length ], p[ i % p.length ] );
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b.fail( 'should not return NaN' );
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}

‎package.json

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"@stdlib/utils-library-manifest": "^0.2.2"
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},
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"devDependencies": {
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"@stdlib/array-float64": "^0.2.2",
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"@stdlib/array-int32": "^0.2.2",
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"@stdlib/constants-float64-eps": "^0.2.2",
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"@stdlib/constants-float64-ninf": "^0.2.2",
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"@stdlib/math-base-special-abs": "^0.2.2",
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"@stdlib/math-base-special-ceil": "^0.2.2",
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"@stdlib/math-base-special-round": "^0.3.0",
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"@stdlib/random-base-discrete-uniform": "^0.2.1",
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"@stdlib/random-array-discrete-uniform": "^0.2.1",
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"@stdlib/random-array-uniform": "^0.2.1",
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"@stdlib/random-base-randu": "^0.2.1",
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"@stdlib/random-base-uniform": "^0.2.1",
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"@stdlib/utils-try-require": "^0.2.2",
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"tape": "git+https://github.com/kgryte/tape.git#fix/globby",
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"istanbul": "^0.4.1",

‎test/test.js

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tape( 'if provided `NaN` for any parameter, the function returns `NaN`', function test( t ) {
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var v = variance( NaN, 0.5 );
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t.equal( isnan( v ), true, 'returns NaN' );
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t.equal( isnan( v ), true, 'returns expected value' );
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v = variance( 10, NaN );
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t.equal( isnan( v ), true, 'returns NaN' );
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t.equal( isnan( v ), true, 'returns expected value' );
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v = variance( NaN, NaN );
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t.equal( isnan( v ), true, 'returns NaN' );
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t.equal( isnan( v ), true, 'returns expected value' );
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v = variance( 1.5, 0.5 );
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t.equal( isnan( v ), true, 'returns NaN' );
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t.equal( isnan( v ), true, 'returns expected value' );
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v = variance( -2, 0.5 );
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t.equal( isnan( v ), true, 'returns NaN' );
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t.equal( isnan( v ), true, 'returns expected value' );
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v = variance( -1, 0.5 );
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t.equal( isnan( v ), true, 'returns NaN' );
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t.equal( isnan( v ), true, 'returns expected value' );
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v = variance( 2.5, 0.5 );
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t.equal( isnan( v ), true, 'returns NaN' );
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t.equal( isnan( v ), true, 'returns expected value' );
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v = variance( PINF, 0.5 );
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t.equal( isnan( v ), true, 'returns NaN' );
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t.equal( isnan( v ), true, 'returns expected value' );
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t.end();
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v = variance( 20, -1.0 );
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t.equal( isnan( v ), true, 'returns NaN' );
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t.equal( isnan( v ), true, 'returns expected value' );
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v = variance( 20, 1.5 );
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t.equal( isnan( v ), true, 'returns NaN' );
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t.equal( isnan( v ), true, 'returns expected value' );
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v = variance( 20, NINF );
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t.equal( isnan( v ), true, 'returns NaN' );
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t.equal( isnan( v ), true, 'returns expected value' );
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v = variance( 20, PINF );
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t.equal( isnan( v ), true, 'returns NaN' );
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t.equal( isnan( v ), true, 'returns expected value' );
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‎test/test.native.js

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tape( 'if provided `NaN` for any parameter, the function returns `NaN`', opts, function test( t ) {
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t.equal( isnan( v ), true, 'returns NaN' );
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t.equal( isnan( v ), true, 'returns expected value' );
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t.equal( isnan( v ), true, 'returns NaN' );
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t.equal( isnan( v ), true, 'returns expected value' );
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v = variance( -1, 0.5 );
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t.equal( isnan( v ), true, 'returns NaN' );
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t.equal( isnan( v ), true, 'returns expected value' );
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y = variance( 10, -0.1 );
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t.equal( isnan( y ), true, 'returns NaN' );
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t.equal( isnan( y ), true, 'returns expected value' );
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y = variance( 10, 1.1 );
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t.equal( isnan( y ), true, 'returns NaN' );
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t.equal( isnan( y ), true, 'returns expected value' );
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t.end();
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});

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