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Stdlib Authors","url":"https://github.com/stdlib-js/stdlib/graphs/contributors"},"license":"Apache-2.0","homepage":"https://stdlib.io","keywords":["stdlib","stdmath","statistics","stats","mathematics","math","covariance","sample covariance","variance","unbiased","var","correlation","corr","pearson","product-moment","bivariate","incremental","accumulator"],"repository":{"type":"git","url":"git://github.com/stdlib-js/stats-incr-pcorr.git"},"description":"Compute a sample Pearson product-moment correlation coefficient.","contributors":[{"name":"The Stdlib Authors","url":"https://github.com/stdlib-js/stdlib/graphs/contributors"}],"maintainers":[{"name":"stdlib-bot","email":"kgryte+stdlibbot@gmail.com"},{"name":"kgryte","email":"kgryte@gmail.com"},{"name":"planeshifter","email":"pgb@andrew.cmu.edu"},{"name":"rreusser","email":"rsreusser@gmail.com"}],"readme":"<!--\n\n@license Apache-2.0\n\nCopyright (c) 2018 The Stdlib Authors.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n   http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\n-->\n\n\n<details>\n  <summary>\n    About stdlib...\n  </summary>\n  <p>We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.</p>\n  <p>The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.</p>\n  <p>When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.</p>\n  <p>To join us in bringing numerical computing to the web, get started by checking us out on <a href=\"https://github.com/stdlib-js/stdlib\">GitHub</a>, and please consider <a href=\"https://opencollective.com/stdlib\">financially supporting stdlib</a>. We greatly appreciate your continued support!</p>\n</details>\n\n# incrpcorr\n\n[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] <!-- [![dependencies][dependencies-image]][dependencies-url] -->\n\n> Compute a [sample Pearson product-moment correlation coefficient][pearson-correlation] incrementally.\n\n<section class=\"intro\">\n\nThe [Pearson product-moment correlation coefficient][pearson-correlation] between random variables `X` and `Y` is defined as\n\n<!-- <equation class=\"equation\" label=\"eq:pearson_correlation_coefficient\" align=\"center\" raw=\"\\rho_{X,Y} = \\frac{\\operatorname{cov}(X,Y)}{\\sigma_X \\sigma_Y}\" alt=\"Equation for the Pearson product-moment correlation coefficient.\"> -->\n\n<div class=\"equation\" align=\"center\" data-raw-text=\"\\rho_{X,Y} = \\frac{\\operatorname{cov}(X,Y)}{\\sigma_X \\sigma_Y}\" data-equation=\"eq:pearson_correlation_coefficient\">\n    <img src=\"https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@49d8cabda84033d55d7b8069f19ee3dd8b8d1496/lib/node_modules/@stdlib/stats/incr/pcorr/docs/img/equation_pearson_correlation_coefficient.svg\" alt=\"Equation for the Pearson product-moment correlation coefficient.\">\n    <br>\n</div> -->\n\n<!-- </equation> -->\n\nwhere the numerator is the [covariance][covariance] and the denominator is the product of the respective standard deviations.\n\nFor a sample of size `n`, the [sample Pearson product-moment correlation coefficient][pearson-correlation] is defined as\n\n<!-- <equation class=\"equation\" label=\"eq:sample_pearson_correlation_coefficient\" align=\"center\" raw=\"r = \\frac{\\displaystyle\\sum_{i=0}^{n-1} (x_i - \\bar{x})(y_i - \\bar{y})}{\\displaystyle\\sqrt{\\sum_{i=0}^{n-1} (x_i - \\bar{x})^2} \\sqrt{\\sum_{i=0}^{n-1} (y_i - \\bar{y})^2}}\" alt=\"Equation for the sample Pearson product-moment correlation coefficient.\"> -->\n\n<!-- <div class=\"equation\" align=\"center\" data-raw-text=\"r = \\frac{\\displaystyle\\sum_{i=0}^{n-1} (x_i - \\bar{x})(y_i - \\bar{y})}{\\displaystyle\\sqrt{\\sum_{i=0}^{n-1} (x_i - \\bar{x})^2} \\sqrt{\\sum_{i=0}^{n-1} (y_i - \\bar{y})^2}}\" data-equation=\"eq:sample_pearson_correlation_coefficient\">\n    <img src=\"https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@49d8cabda84033d55d7b8069f19ee3dd8b8d1496/lib/node_modules/@stdlib/stats/incr/pcorr/docs/img/equation_sample_pearson_correlation_coefficient.svg\" alt=\"Equation for the sample Pearson product-moment correlation coefficient.\">\n    <br>\n</div>\n\n<!-- </equation> -->\n\n</section>\n\n<!-- /.intro -->\n\n<section class=\"installation\">\n\n## Installation\n\n```bash\nnpm install @stdlib/stats-incr-pcorr\n```\n\n</section>\n\n<section class=\"usage\">\n\n## Usage\n\n```javascript\nvar incrpcorr = require( '@stdlib/stats-incr-pcorr' );\n```\n\n#### incrpcorr( \\[mx, my] )\n\nReturns an accumulator `function` which incrementally computes a [sample Pearson product-moment correlation coefficient][pearson-correlation].\n\n```javascript\nvar accumulator = incrpcorr();\n```\n\nIf the means are already known, provide `mx` and `my` arguments.\n\n```javascript\nvar accumulator = incrpcorr( 3.0, -5.5 );\n```\n\n#### accumulator( \\[x, y] )\n\nIf provided input value `x` and `y`, the accumulator function returns an updated [sample correlation coefficient][pearson-correlation]. If not provided input values `x` and `y`, the accumulator function returns the current [sample correlation coefficient][pearson-correlation].\n\n```javascript\nvar accumulator = incrpcorr();\n\nvar v = accumulator( 2.0, 1.0 );\n// returns 0.0\n\nv = accumulator( 1.0, -5.0 );\n// returns 1.0\n\nv = accumulator( 3.0, 3.14 );\n// returns ~0.965\n\nv = accumulator();\n// returns ~0.965\n```\n\n</section>\n\n<!-- /.usage -->\n\n<section class=\"notes\">\n\n## Notes\n\n-   Input values are **not** type checked. If provided `NaN` or a value which, when used in computations, results in `NaN`, the accumulated value is `NaN` for **all** future invocations. If non-numeric inputs are possible, you are advised to type check and handle accordingly **before** passing the value to the accumulator function.\n\n</section>\n\n<!-- /.notes -->\n\n<section class=\"examples\">\n\n## Examples\n\n<!-- eslint no-undef: \"error\" -->\n\n```javascript\nvar randu = require( '@stdlib/random-base-randu' );\nvar incrpcorr = require( '@stdlib/stats-incr-pcorr' );\n\nvar accumulator;\nvar x;\nvar y;\nvar i;\n\n// Initialize an accumulator:\naccumulator = incrpcorr();\n\n// For each simulated datum, update the sample correlation coefficient...\nfor ( i = 0; i < 100; i++ ) {\n    x = randu() * 100.0;\n    y = randu() * 100.0;\n    accumulator( x, y );\n}\nconsole.log( accumulator() );\n```\n\n</section>\n\n<!-- /.examples -->\n\n<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->\n\n<section class=\"related\">\n\n* * *\n\n## See Also\n\n-   <span class=\"package-name\">[`@stdlib/stats-incr/covariance`][@stdlib/stats/incr/covariance]</span><span class=\"delimiter\">: </span><span class=\"description\">compute an unbiased sample covariance incrementally.</span>\n-   <span class=\"package-name\">[`@stdlib/stats-incr/mpcorr`][@stdlib/stats/incr/mpcorr]</span><span class=\"delimiter\">: </span><span class=\"description\">compute a moving sample Pearson product-moment correlation coefficient incrementally.</span>\n-   <span class=\"package-name\">[`@stdlib/stats-incr/summary`][@stdlib/stats/incr/summary]</span><span class=\"delimiter\">: </span><span class=\"description\">compute a statistical summary incrementally.</span>\n\n</section>\n\n<!-- /.related -->\n\n<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->\n\n\n<section class=\"main-repo\" >\n\n* * *\n\n## Notice\n\nThis package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.\n\nFor more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].\n\n#### Community\n\n[![Chat][chat-image]][chat-url]\n\n---\n\n## License\n\nSee [LICENSE][stdlib-license].\n\n\n## Copyright\n\nCopyright &copy; 2016-2026. The Stdlib [Authors][stdlib-authors].\n\n</section>\n\n<!-- /.stdlib -->\n\n<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->\n\n<section class=\"links\">\n\n[npm-image]: http://img.shields.io/npm/v/@stdlib/stats-incr-pcorr.svg\n[npm-url]: https://npmjs.org/package/@stdlib/stats-incr-pcorr\n\n[test-image]: https://github.com/stdlib-js/stats-incr-pcorr/actions/workflows/test.yml/badge.svg?branch=v0.2.3\n[test-url]: https://github.com/stdlib-js/stats-incr-pcorr/actions/workflows/test.yml?query=branch:v0.2.3\n\n[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-incr-pcorr/main.svg\n[coverage-url]: https://codecov.io/github/stdlib-js/stats-incr-pcorr?branch=main\n\n<!--\n\n[dependencies-image]: https://img.shields.io/david/stdlib-js/stats-incr-pcorr.svg\n[dependencies-url]: https://david-dm.org/stdlib-js/stats-incr-pcorr/main\n\n-->\n\n[chat-image]: https://img.shields.io/badge/zulip-join_chat-brightgreen.svg\n[chat-url]: https://stdlib.zulipchat.com\n\n[stdlib]: https://github.com/stdlib-js/stdlib\n\n[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors\n\n[umd]: https://github.com/umdjs/umd\n[es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules\n\n[deno-url]: https://github.com/stdlib-js/stats-incr-pcorr/tree/deno\n[deno-readme]: https://github.com/stdlib-js/stats-incr-pcorr/blob/deno/README.md\n[umd-url]: https://github.com/stdlib-js/stats-incr-pcorr/tree/umd\n[umd-readme]: https://github.com/stdlib-js/stats-incr-pcorr/blob/umd/README.md\n[esm-url]: https://github.com/stdlib-js/stats-incr-pcorr/tree/esm\n[esm-readme]: https://github.com/stdlib-js/stats-incr-pcorr/blob/esm/README.md\n[branches-url]: https://github.com/stdlib-js/stats-incr-pcorr/blob/main/branches.md\n\n[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-incr-pcorr/main/LICENSE\n\n[pearson-correlation]: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient\n\n[covariance]: https://en.wikipedia.org/wiki/Covariance\n\n<!-- <related-links> -->\n\n[@stdlib/stats/incr/covariance]: https://www.npmjs.com/package/@stdlib/stats-incr-covariance\n\n[@stdlib/stats/incr/mpcorr]: https://www.npmjs.com/package/@stdlib/stats-incr-mpcorr\n\n[@stdlib/stats/incr/summary]: https://www.npmjs.com/package/@stdlib/stats-incr-summary\n\n<!-- </related-links> -->\n\n</section>\n\n<!-- /.links -->\n","readmeFilename":"README.md"}