{"id":26286386,"url":"https://github.com/stdlib-js/stats-incr-pcorrmat","last_synced_at":"2025-05-07T16:23:31.550Z","repository":{"id":41395323,"uuid":"377267143","full_name":"stdlib-js/stats-incr-pcorrmat","owner":"stdlib-js","description":"Compute a sample Pearson product-moment correlation matrix incrementally.","archived":false,"fork":false,"pushed_at":"2025-03-31T00:49:04.000Z","size":2460,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-04-12T17:57:32.655Z","etag":null,"topics":["anticorrelation","correlated","correlation","covariance","dispersion","javascript","math","mathematics","matrix","node","node-js","nodejs","sample-covariance","standard-deviation","statistics","stats","stdlib","unbiased","var","variance"],"latest_commit_sha":null,"homepage":"https://github.com/stdlib-js/stdlib","language":"JavaScript","has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/stdlib-js.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":"SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null},"funding":{"github":["stdlib-js"],"open_collective":"stdlib","tidelift":"npm/@stdlib/stdlib"}},"created_at":"2021-06-15T19:01:59.000Z","updated_at":"2025-03-31T00:38:51.000Z","dependencies_parsed_at":"2024-01-01T06:35:26.120Z","dependency_job_id":"efec6280-1f42-404b-b3ed-e65010a900e3","html_url":"https://github.com/stdlib-js/stats-incr-pcorrmat","commit_stats":{"total_commits":44,"total_committers":1,"mean_commits":44.0,"dds":0.0,"last_synced_commit":"68b731f6189064738e33d5df797e82e7c2e1cac0"},"previous_names":[],"tags_count":23,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stdlib-js%2Fstats-incr-pcorrmat","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stdlib-js%2Fstats-incr-pcorrmat/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stdlib-js%2Fstats-incr-pcorrmat/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stdlib-js%2Fstats-incr-pcorrmat/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/stdlib-js","download_url":"https://codeload.github.com/stdlib-js/stats-incr-pcorrmat/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252913465,"owners_count":21824184,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["anticorrelation","correlated","correlation","covariance","dispersion","javascript","math","mathematics","matrix","node","node-js","nodejs","sample-covariance","standard-deviation","statistics","stats","stdlib","unbiased","var","variance"],"created_at":"2025-03-14T20:22:16.287Z","updated_at":"2025-05-07T16:23:31.498Z","avatar_url":"https://github.com/stdlib-js.png","language":"JavaScript","funding_links":["https://github.com/sponsors/stdlib-js","https://opencollective.com/stdlib","https://tidelift.com/funding/github/npm/@stdlib/stdlib"],"categories":[],"sub_categories":[],"readme":"\u003c!--\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--\u003e\n\n\n\u003cdetails\u003e\n  \u003csummary\u003e\n    About stdlib...\n  \u003c/summary\u003e\n  \u003cp\u003eWe 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.\u003c/p\u003e\n  \u003cp\u003eThe 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.\u003c/p\u003e\n  \u003cp\u003eWhen 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.\u003c/p\u003e\n  \u003cp\u003eTo join us in bringing numerical computing to the web, get started by checking us out on \u003ca href=\"https://github.com/stdlib-js/stdlib\"\u003eGitHub\u003c/a\u003e, and please consider \u003ca href=\"https://opencollective.com/stdlib\"\u003efinancially supporting stdlib\u003c/a\u003e. We greatly appreciate your continued support!\u003c/p\u003e\n\u003c/details\u003e\n\n# incrpcorrmat\n\n[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] \u003c!-- [![dependencies][dependencies-image]][dependencies-url] --\u003e\n\n\u003e Compute a [sample Pearson product-moment correlation matrix][pearson-correlation] incrementally.\n\n\u003csection class=\"intro\"\u003e\n\nA [Pearson product-moment correlation matrix][pearson-correlation] is an M-by-M matrix whose elements specified by indices `j` and `k` are the [Pearson product-moment correlation coefficients][pearson-correlation] between the jth and kth data variables. The [Pearson product-moment correlation coefficient][pearson-correlation] between random variables `X` and `Y` is defined as\n\n\u003c!-- \u003cequation 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.\"\u003e --\u003e\n\n```math\n\\rho_{X,Y} = \\frac{\\mathop{\\mathrm{cov}}(X,Y)}{\\sigma_X \\sigma_Y}\n```\n\n\u003c!-- \u003cdiv 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\"\u003e\n    \u003cimg src=\"https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@49d8cabda84033d55d7b8069f19ee3dd8b8d1496/lib/node_modules/@stdlib/stats/incr/pcorrmat/docs/img/equation_pearson_correlation_coefficient.svg\" alt=\"Equation for the Pearson product-moment correlation coefficient.\"\u003e\n    \u003cbr\u003e\n\u003c/div\u003e --\u003e\n\n\u003c!-- \u003c/equation\u003e --\u003e\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\u003c!-- \u003cequation 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.\"\u003e --\u003e\n\n```math\nr = \\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}}\n```\n\n\u003c!-- \u003cdiv 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\"\u003e\n    \u003cimg src=\"https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@49d8cabda84033d55d7b8069f19ee3dd8b8d1496/lib/node_modules/@stdlib/stats/incr/pcorrmat/docs/img/equation_sample_pearson_correlation_coefficient.svg\" alt=\"Equation for the sample Pearson product-moment correlation coefficient.\"\u003e\n    \u003cbr\u003e\n\u003c/div\u003e --\u003e\n\n\u003c!-- \u003c/equation\u003e --\u003e\n\n\u003c/section\u003e\n\n\u003c!-- /.intro --\u003e\n\n\u003csection class=\"installation\"\u003e\n\n## Installation\n\n```bash\nnpm install @stdlib/stats-incr-pcorrmat\n```\n\nAlternatively,\n\n-   To load the package in a website via a `script` tag without installation and bundlers, use the [ES Module][es-module] available on the [`esm`][esm-url] branch (see [README][esm-readme]).\n-   If you are using Deno, visit the [`deno`][deno-url] branch (see [README][deno-readme] for usage intructions).\n-   For use in Observable, or in browser/node environments, use the [Universal Module Definition (UMD)][umd] build available on the [`umd`][umd-url] branch (see [README][umd-readme]).\n\nThe [branches.md][branches-url] file summarizes the available branches and displays a diagram illustrating their relationships.\n\nTo view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.\n\n\u003c/section\u003e\n\n\u003csection class=\"usage\"\u003e\n\n## Usage\n\n```javascript\nvar incrpcorrmat = require( '@stdlib/stats-incr-pcorrmat' );\n```\n\n#### incrpcorrmat( out\\[, means] )\n\nReturns an accumulator `function` which incrementally computes a [sample Pearson product-moment correlation matrix][pearson-correlation].\n\n```javascript\n// Create an accumulator for computing a 2-dimensional correlation matrix:\nvar accumulator = incrpcorrmat( 2 );\n```\n\nThe `out` argument may be either the order of the [correlation matrix][pearson-correlation] or a square 2-dimensional [`ndarray`][@stdlib/ndarray/ctor] for storing the [correlation matrix][pearson-correlation].\n\n```javascript\nvar Float64Array = require( '@stdlib/array-float64' );\nvar ndarray = require( '@stdlib/ndarray-ctor' );\n\nvar buffer = new Float64Array( 4 );\nvar shape = [ 2, 2 ];\nvar strides = [ 2, 1 ];\n\n// Create a 2-dimensional output correlation matrix:\nvar corr = ndarray( 'float64', buffer, shape, strides, 0, 'row-major' );\n\nvar accumulator = incrpcorrmat( corr );\n```\n\nWhen means are known, the function supports providing a 1-dimensional [`ndarray`][@stdlib/ndarray/ctor] containing mean values.\n\n```javascript\nvar Float64Array = require( '@stdlib/array-float64' );\nvar ndarray = require( '@stdlib/ndarray-ctor' );\n\nvar buffer = new Float64Array( 2 );\nvar shape = [ 2 ];\nvar strides = [ 1 ];\n\nvar means = ndarray( 'float64', buffer, shape, strides, 0, 'row-major' );\n\nmeans.set( 0, 3.0 );\nmeans.set( 1, -5.5 );\n\nvar accumulator = incrpcorrmat( 2, means );\n```\n\n#### accumulator( \\[vector] )\n\nIf provided a data vector, the accumulator function returns an updated [sample Pearson product-moment correlation matrix][pearson-correlation]. If not provided a data vector, the accumulator function returns the current [sample Pearson product-moment correlation matrix][pearson-correlation].\n\n```javascript\nvar Float64Array = require( '@stdlib/array-float64' );\nvar ndarray = require( '@stdlib/ndarray-ctor' );\n\nvar buffer = new Float64Array( 4 );\nvar shape = [ 2, 2 ];\nvar strides = [ 2, 1 ];\nvar corr = ndarray( 'float64', buffer, shape, strides, 0, 'row-major' );\n\nbuffer = new Float64Array( 2 );\nshape = [ 2 ];\nstrides = [ 1 ];\nvar vec = ndarray( 'float64', buffer, shape, strides, 0, 'row-major' );\n\nvar accumulator = incrpcorrmat( corr );\n\nvec.set( 0, 2.0 );\nvec.set( 1, 1.0 );\n\nvar out = accumulator( vec );\n// returns \u003cndarray\u003e\n\nvar bool = ( out === corr );\n// returns true\n\nvec.set( 0, 1.0 );\nvec.set( 1, -5.0 );\n\nout = accumulator( vec );\n// returns \u003cndarray\u003e\n\nvec.set( 0, 3.0 );\nvec.set( 1, 3.14 );\n\nout = accumulator( vec );\n// returns \u003cndarray\u003e\n\nout = accumulator();\n// returns \u003cndarray\u003e\n```\n\n\u003c/section\u003e\n\n\u003c!-- /.usage --\u003e\n\n\u003csection class=\"notes\"\u003e\n\n\u003c/section\u003e\n\n\u003c!-- /.notes --\u003e\n\n\u003csection class=\"examples\"\u003e\n\n## Examples\n\n\u003c!-- eslint no-undef: \"error\" --\u003e\n\n```javascript\nvar randu = require( '@stdlib/random-base-randu' );\nvar ndarray = require( '@stdlib/ndarray-ctor' );\nvar Float64Array = require( '@stdlib/array-float64' );\nvar incrpcorrmat = require( '@stdlib/stats-incr-pcorrmat' );\n\nvar corr;\nvar rxy;\nvar ryx;\nvar rx;\nvar ry;\nvar i;\n\n// Initialize an accumulator for a 2-dimensional correlation matrix:\nvar accumulator = incrpcorrmat( 2 );\n\n// Create a 1-dimensional data vector:\nvar buffer = new Float64Array( 2 );\nvar shape = [ 2 ];\nvar strides = [ 1 ];\n\nvar vec = ndarray( 'float64', buffer, shape, strides, 0, 'row-major' );\n\n// For each simulated data vector, update the sample correlation matrix...\nfor ( i = 0; i \u003c 100; i++ ) {\n    vec.set( 0, randu()*100.0 );\n    vec.set( 1, randu()*100.0 );\n    corr = accumulator( vec );\n\n    rx = corr.get( 0, 0 ).toFixed( 4 );\n    ry = corr.get( 1, 1 ).toFixed( 4 );\n    rxy = corr.get( 0, 1 ).toFixed( 4 );\n    ryx = corr.get( 1, 0 ).toFixed( 4 );\n\n    console.log( '[ %d, %d\\n  %d, %d ]', rx, rxy, ryx, ry );\n}\n```\n\n\u003c/section\u003e\n\n\u003c!-- /.examples --\u003e\n\n\u003c!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. --\u003e\n\n\u003csection class=\"related\"\u003e\n\n* * *\n\n## See Also\n\n-   \u003cspan class=\"package-name\"\u003e[`@stdlib/stats-incr/covmat`][@stdlib/stats/incr/covmat]\u003c/span\u003e\u003cspan class=\"delimiter\"\u003e: \u003c/span\u003e\u003cspan class=\"description\"\u003ecompute an unbiased sample covariance matrix incrementally.\u003c/span\u003e\n-   \u003cspan class=\"package-name\"\u003e[`@stdlib/stats-incr/pcorr`][@stdlib/stats/incr/pcorr]\u003c/span\u003e\u003cspan class=\"delimiter\"\u003e: \u003c/span\u003e\u003cspan class=\"description\"\u003ecompute a sample Pearson product-moment correlation coefficient.\u003c/span\u003e\n-   \u003cspan class=\"package-name\"\u003e[`@stdlib/stats-incr/pcorrdistmat`][@stdlib/stats/incr/pcorrdistmat]\u003c/span\u003e\u003cspan class=\"delimiter\"\u003e: \u003c/span\u003e\u003cspan class=\"description\"\u003ecompute a sample Pearson product-moment correlation distance matrix incrementally.\u003c/span\u003e\n\n\u003c/section\u003e\n\n\u003c!-- /.related --\u003e\n\n\u003c!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. --\u003e\n\n\n\u003csection class=\"main-repo\" \u003e\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 \u0026copy; 2016-2025. The Stdlib [Authors][stdlib-authors].\n\n\u003c/section\u003e\n\n\u003c!-- /.stdlib --\u003e\n\n\u003c!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. --\u003e\n\n\u003csection class=\"links\"\u003e\n\n[npm-image]: http://img.shields.io/npm/v/@stdlib/stats-incr-pcorrmat.svg\n[npm-url]: https://npmjs.org/package/@stdlib/stats-incr-pcorrmat\n\n[test-image]: https://github.com/stdlib-js/stats-incr-pcorrmat/actions/workflows/test.yml/badge.svg?branch=main\n[test-url]: https://github.com/stdlib-js/stats-incr-pcorrmat/actions/workflows/test.yml?query=branch:main\n\n[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-incr-pcorrmat/main.svg\n[coverage-url]: https://codecov.io/github/stdlib-js/stats-incr-pcorrmat?branch=main\n\n\u003c!--\n\n[dependencies-image]: https://img.shields.io/david/stdlib-js/stats-incr-pcorrmat.svg\n[dependencies-url]: https://david-dm.org/stdlib-js/stats-incr-pcorrmat/main\n\n--\u003e\n\n[chat-image]: https://img.shields.io/gitter/room/stdlib-js/stdlib.svg\n[chat-url]: https://app.gitter.im/#/room/#stdlib-js_stdlib:gitter.im\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-pcorrmat/tree/deno\n[deno-readme]: https://github.com/stdlib-js/stats-incr-pcorrmat/blob/deno/README.md\n[umd-url]: https://github.com/stdlib-js/stats-incr-pcorrmat/tree/umd\n[umd-readme]: https://github.com/stdlib-js/stats-incr-pcorrmat/blob/umd/README.md\n[esm-url]: https://github.com/stdlib-js/stats-incr-pcorrmat/tree/esm\n[esm-readme]: https://github.com/stdlib-js/stats-incr-pcorrmat/blob/esm/README.md\n[branches-url]: https://github.com/stdlib-js/stats-incr-pcorrmat/blob/main/branches.md\n\n[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-incr-pcorrmat/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[@stdlib/ndarray/ctor]: https://github.com/stdlib-js/ndarray-ctor\n\n\u003c!-- \u003crelated-links\u003e --\u003e\n\n[@stdlib/stats/incr/covmat]: https://github.com/stdlib-js/stats-incr-covmat\n\n[@stdlib/stats/incr/pcorr]: https://github.com/stdlib-js/stats-incr-pcorr\n\n[@stdlib/stats/incr/pcorrdistmat]: https://github.com/stdlib-js/stats-incr-pcorrdistmat\n\n\u003c!-- \u003c/related-links\u003e --\u003e\n\n\u003c/section\u003e\n\n\u003c!-- /.links --\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstdlib-js%2Fstats-incr-pcorrmat","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstdlib-js%2Fstats-incr-pcorrmat","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstdlib-js%2Fstats-incr-pcorrmat/lists"}