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unexpected eof while 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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# incrgrubbs\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 [Grubbs' test][grubbs-test] for outliers.\n\n\u003csection class=\"intro\"\u003e\n\n[Grubbs' test][grubbs-test] (also known as the **maximum normalized residual test** or **extreme studentized deviate test**) is a statistical test used to detect outliers in a univariate dataset assumed to come from a normally distributed population. [Grubbs' test][grubbs-test] is defined for the hypothesis:\n\n-   **H_0**: the dataset does **not** contain outliers.\n-   **H_1**: the dataset contains **exactly** one outlier.\n\nThe [Grubbs' test][grubbs-test] statistic for a two-sided alternative hypothesis is defined as\n\n\u003c!-- \u003cequation class=\"equation\" label=\"eq:grubbs_test_statistic\" align=\"center\" raw=\"G = \\frac{\\max_{i=0,\\ldots,N-1} |Y_i - \\bar{Y}|}{s}\" alt=\"Grubbs' test statistic.\"\u003e --\u003e\n\n```math\nG = \\frac{\\max_{i=0,\\ldots,N-1} |Y_i - \\bar{Y}|}{s}\n```\n\n\u003c!-- \u003cdiv class=\"equation\" align=\"center\" data-raw-text=\"G = \\frac{\\max_{i=0,\\ldots,N-1} |Y_i - \\bar{Y}|}{s}\" data-equation=\"eq:grubbs_test_statistic\"\u003e\n    \u003cimg src=\"https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@d6af7da0801d2116a9507668d13ef7bf607fd275/lib/node_modules/@stdlib/stats/incr/grubbs/docs/img/equation_grubbs_test_statistic.svg\" alt=\"Grubbs' test statistic.\"\u003e\n    \u003cbr\u003e\n\u003c/div\u003e --\u003e\n\n\u003c!-- \u003c/equation\u003e --\u003e\n\nwhere `s` is the sample standard deviation. The [Grubbs test][grubbs-test] statistic is thus the largest absolute deviation from the sample mean in units of the sample standard deviation.\n\nThe [Grubbs' test][grubbs-test] statistic for the alternative hypothesis that the minimum value is an outlier is defined as\n\n\u003c!-- \u003cequation class=\"equation\" label=\"eq:grubbs_test_statistic_min\" align=\"center\" raw=\"G = \\frac{\\bar{Y} - Y_{\\textrm{min}}}{s}\" alt=\"Grubbs' test statistic for testing whether the minimum value is an outlier.\"\u003e --\u003e\n\n```math\nG = \\frac{\\bar{Y} - Y_{\\textrm{min}}}{s}\n```\n\n\u003c!-- \u003cdiv class=\"equation\" align=\"center\" data-raw-text=\"G = \\frac{\\bar{Y} - Y_{\\textrm{min}}}{s}\" data-equation=\"eq:grubbs_test_statistic_min\"\u003e\n    \u003cimg src=\"https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@d6af7da0801d2116a9507668d13ef7bf607fd275/lib/node_modules/@stdlib/stats/incr/grubbs/docs/img/equation_grubbs_test_statistic_min.svg\" alt=\"Grubbs' test statistic for testing whether the minimum value is an outlier.\"\u003e\n    \u003cbr\u003e\n\u003c/div\u003e --\u003e\n\n\u003c!-- \u003c/equation\u003e --\u003e\n\nThe [Grubbs' test][grubbs-test] statistic for the alternative hypothesis that the maximum value is an outlier is defined as\n\n\u003c!-- \u003cequation class=\"equation\" label=\"eq:grubbs_test_statistic_max\" align=\"center\" raw=\"G = \\frac{Y_{\\textrm{max}} - \\bar{Y}}{s}\" alt=\"Grubbs' test statistic for testing whether the maximum value is an outlier.\"\u003e --\u003e\n\n```math\nG = \\frac{Y_{\\textrm{max}} - \\bar{Y}}{s}\n```\n\n\u003c!-- \u003cdiv class=\"equation\" align=\"center\" data-raw-text=\"G = \\frac{Y_{\\textrm{max}} - \\bar{Y}}{s}\" data-equation=\"eq:grubbs_test_statistic_max\"\u003e\n    \u003cimg src=\"https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@d6af7da0801d2116a9507668d13ef7bf607fd275/lib/node_modules/@stdlib/stats/incr/grubbs/docs/img/equation_grubbs_test_statistic_max.svg\" alt=\"Grubbs' test statistic for testing whether the maximum value is an outlier.\"\u003e\n    \u003cbr\u003e\n\u003c/div\u003e --\u003e\n\n\u003c!-- \u003c/equation\u003e --\u003e\n\nFor a two-sided test, the hypothesis that a dataset does **not** contain an outlier is rejected at significance level α if\n\n\u003c!-- \u003cequation class=\"equation\" label=\"eq:grubbs_test_two_sided\" align=\"center\" raw=\"G \u003e \\frac{N-1}{\\sqrt{N}} \\sqrt{\\frac{t^2_{\\alpha/(2N),N-2}}{N - 2 + t^2_{\\alpha/(2N),N-2}}}\" alt=\"Two-sided Grubbs' test.\"\u003e --\u003e\n\n```math\nG \u003e \\frac{N-1}{\\sqrt{N}} \\sqrt{\\frac{t^2_{\\alpha/(2N),N-2}}{N - 2 + t^2_{\\alpha/(2N),N-2}}}\n```\n\n\u003c!-- \u003cdiv class=\"equation\" align=\"center\" data-raw-text=\"G \u003e \\frac{N-1}{\\sqrt{N}} \\sqrt{\\frac{t^2_{\\alpha/(2N),N-2}}{N - 2 + t^2_{\\alpha/(2N),N-2}}}\" data-equation=\"eq:grubbs_test_two_sided\"\u003e\n    \u003cimg src=\"https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@d6af7da0801d2116a9507668d13ef7bf607fd275/lib/node_modules/@stdlib/stats/incr/grubbs/docs/img/equation_grubbs_test_two_sided.svg\" alt=\"Two-sided Grubbs' test.\"\u003e\n    \u003cbr\u003e\n\u003c/div\u003e --\u003e\n\n\u003c!-- \u003c/equation\u003e --\u003e\n\nwhere `t` denotes the upper critical value of the _t_-distribution with `N-2` degrees of freedom and a significance level of `α/(2N)`.\n\nFor a one-sided test, the hypothesis that a dataset does **not** contain an outlier is rejected at significance level α if\n\n\u003c!-- \u003cequation class=\"equation\" label=\"eq:grubbs_test_one_sided\" align=\"center\" raw=\"G \u003e \\frac{N-1}{\\sqrt{N}} \\sqrt{\\frac{t^2_{\\alpha/N,N-2}}{N - 2 + t^2_{\\alpha/N,N-2}}}\" alt=\"One-sided Grubbs' test.\"\u003e --\u003e\n\n```math\nG \u003e \\frac{N-1}{\\sqrt{N}} \\sqrt{\\frac{t^2_{\\alpha/N,N-2}}{N - 2 + t^2_{\\alpha/N,N-2}}}\n```\n\n\u003c!-- \u003cdiv class=\"equation\" align=\"center\" data-raw-text=\"G \u003e \\frac{N-1}{\\sqrt{N}} \\sqrt{\\frac{t^2_{\\alpha/N,N-2}}{N - 2 + t^2_{\\alpha/N,N-2}}}\" data-equation=\"eq:grubbs_test_one_sided\"\u003e\n    \u003cimg src=\"https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@d6af7da0801d2116a9507668d13ef7bf607fd275/lib/node_modules/@stdlib/stats/incr/grubbs/docs/img/equation_grubbs_test_one_sided.svg\" alt=\"One-sided Grubbs' test.\"\u003e\n    \u003cbr\u003e\n\u003c/div\u003e --\u003e\n\n\u003c!-- \u003c/equation\u003e --\u003e\n\nwhere `t` denotes the upper critical value of the _t_-distribution with `N-2` degrees of freedom and a significance level of `α/N`.\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-grubbs\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 incrgrubbs = require( '@stdlib/stats-incr-grubbs' );\n```\n\n#### incrgrubbs( \\[options] )\n\nReturns an accumulator `function` which incrementally performs [Grubbs' test][grubbs-test] for outliers.\n\n```javascript\nvar accumulator = incrgrubbs();\n```\n\nThe function accepts the following `options`:\n\n-   **alpha**: significance level. Default: `0.05`.\n\n-   **alternative**: alternative hypothesis. The option may be one of the following values:\n\n    -   `'two-sided'`: test whether the minimum or maximum value is an outlier.\n    -   `'min'`: test whether the minimum value is an outlier.\n    -   `'max'`: test whether the maximum value is an outlier.\n\n    Default: `'two-sided'`.\n\n-   **init**: number of data points the accumulator should use to compute initial statistics **before** testing for an outlier. Until the accumulator is provided the number of data points specified by this option, the accumulator returns `null`. Default: `100`.\n\n#### accumulator( \\[x] )\n\nIf provided an input value `x`, the accumulator function returns updated test results. If not provided an input value `x`, the accumulator function returns the current test results.\n\n```javascript\nvar rnorm = require( '@stdlib/random-base-normal' );\n\nvar opts = {\n    'init': 0\n};\nvar accumulator = incrgrubbs( opts );\n\nvar results = accumulator( rnorm( 10.0, 5.0 ) );\n// returns null\n\nresults = accumulator( rnorm( 10.0, 5.0 ) );\n// returns null\n\nresults = accumulator( rnorm( 10.0, 5.0 ) );\n// returns \u003cObject\u003e\n\nresults = accumulator();\n// returns \u003cObject\u003e\n```\n\nThe accumulator function returns an `object` having the following fields:\n\n-   **rejected**: boolean indicating whether the null hypothesis should be rejected.\n-   **alpha**: significance level.\n-   **criticalValue**: critical value.\n-   **statistic**: test statistic.\n-   **df**: degrees of freedom.\n-   **mean**: sample mean.\n-   **sd**: corrected sample standard deviation.\n-   **min**: minimum value.\n-   **max**: maximum value.\n-   **alt**: alternative hypothesis.\n-   **method**: method name.\n-   **print**: method for pretty-printing test output.\n\nThe `print` method accepts the following options:\n\n-   **digits**: number of digits after the decimal point. Default: `4`.\n-   **decision**: `boolean` indicating whether to print the test decision. Default: `true`.\n\n\u003c/section\u003e\n\n\u003c!-- /.usage --\u003e\n\n\u003csection class=\"notes\"\u003e\n\n## Notes\n\n-   [Grubbs' test][grubbs-test] **assumes** that data is normally distributed. Accordingly, one should first **verify** that the data can be _reasonably_ approximated by a normal distribution before applying the [Grubbs' test][grubbs-test].\n-   The accumulator must be provided **at least** three data points before performing [Grubbs' test][grubbs-test]. Until at least three data points are provided, the accumulator returns `null`.\n-   Input values are **not** type checked. If provided `NaN` or a value which, when used in computations, results in `NaN`, the test statistic 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\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 incrgrubbs = require( '@stdlib/stats-incr-grubbs' );\n\nvar data;\nvar opts;\nvar acc;\nvar i;\n\n// Define a data set (8 mass spectrometer measurements of a uranium isotope; see Tietjen and Moore. 1972. \"Some Grubbs-Type Statistics for the Detection of Several Outliers\".)\ndata = [ 199.31, 199.53, 200.19, 200.82, 201.92, 201.95, 202.18, 245.57 ];\n\n// Create a new accumulator:\nopts = {\n    'init': data.length,\n    'alternative': 'two-sided'\n};\nacc = incrgrubbs( opts );\n\n// Update the accumulator:\nfor ( i = 0; i \u003c data.length; i++ ) {\n    acc( data[ i ] );\n}\n\n// Print the test results:\nconsole.log( acc().print() );\n/* e.g., =\u003e\nGrubbs' Test\n\nAlternative hypothesis: The maximum value (245.57) is an outlier\n\n    criticalValue: 2.1266\n    statistic: 2.4688\n    df: 6\n\nTest Decision: Reject null in favor of alternative at 5% significance level\n\n*/\n```\n\n\u003c/section\u003e\n\n\u003c!-- /.examples --\u003e\n\n\u003csection class=\"references\"\u003e\n\n* * *\n\n## References\n\n-   Grubbs, Frank E. 1950. \"Sample Criteria for Testing Outlying Observations.\" _The Annals of Mathematical Statistics_ 21 (1). The Institute of Mathematical Statistics: 27–58. doi:[10.1214/aoms/1177729885][@grubbs:1950a].\n-   Grubbs, Frank E. 1969. \"Procedures for Detecting Outlying Observations in Samples.\" _Technometrics_ 11 (1). Taylor \u0026 Francis: 1–21. doi:[10.1080/00401706.1969.10490657][@grubbs:1969a].\n\n\u003c/section\u003e\n\n\u003c!-- /.references --\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/mgrubbs`][@stdlib/stats/incr/mgrubbs]\u003c/span\u003e\u003cspan class=\"delimiter\"\u003e: \u003c/span\u003e\u003cspan class=\"description\"\u003emoving Grubbs' test for outliers.\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; 2016-2026. 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-grubbs.svg\n[npm-url]: https://npmjs.org/package/@stdlib/stats-incr-grubbs\n\n[test-image]: https://github.com/stdlib-js/stats-incr-grubbs/actions/workflows/test.yml/badge.svg?branch=v0.2.3\n[test-url]: https://github.com/stdlib-js/stats-incr-grubbs/actions/workflows/test.yml?query=branch:v0.2.3\n\n[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-incr-grubbs/main.svg\n[coverage-url]: https://codecov.io/github/stdlib-js/stats-incr-grubbs?branch=main\n\n\u003c!--\n\n[dependencies-image]: https://img.shields.io/david/stdlib-js/stats-incr-grubbs.svg\n[dependencies-url]: https://david-dm.org/stdlib-js/stats-incr-grubbs/main\n\n--\u003e\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-grubbs/tree/deno\n[deno-readme]: https://github.com/stdlib-js/stats-incr-grubbs/blob/deno/README.md\n[umd-url]: https://github.com/stdlib-js/stats-incr-grubbs/tree/umd\n[umd-readme]: https://github.com/stdlib-js/stats-incr-grubbs/blob/umd/README.md\n[esm-url]: https://github.com/stdlib-js/stats-incr-grubbs/tree/esm\n[esm-readme]: https://github.com/stdlib-js/stats-incr-grubbs/blob/esm/README.md\n[branches-url]: https://github.com/stdlib-js/stats-incr-grubbs/blob/main/branches.md\n\n[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-incr-grubbs/main/LICENSE\n\n[grubbs-test]: https://en.wikipedia.org/wiki/Grubbs%27_test_for_outliers\n\n[@grubbs:1950a]: https://doi.org/10.1214/aoms/1177729885\n\n[@grubbs:1969a]: https://doi.org/10.1080/00401706.1969.10490657\n\n\u003c!-- \u003crelated-links\u003e --\u003e\n\n[@stdlib/stats/incr/mgrubbs]: https://github.com/stdlib-js/stats-incr-mgrubbs\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-grubbs","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstdlib-js%2Fstats-incr-grubbs","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstdlib-js%2Fstats-incr-grubbs/lists"}