{"id":27125593,"url":"https://github.com/stdlib-js/stats-strided-smeanli","last_synced_at":"2025-06-22T01:36:10.945Z","repository":{"id":286526229,"uuid":"957250676","full_name":"stdlib-js/stats-strided-smeanli","owner":"stdlib-js","description":"Calculate the arithmetic mean of a single-precision floating-point strided array using a one-pass trial mean algorithm.","archived":false,"fork":false,"pushed_at":"2025-06-08T07:11:24.000Z","size":342,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-06-08T08:18:30.475Z","etag":null,"topics":["arithmetic-mean","array","average","avg","central-tendency","javascript","math","mathematics","mean","node","node-js","nodejs","one-pass","statistics","stats","stdlib","strided","strided-array","typed","welford"],"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},"funding":{"github":["stdlib-js"],"open_collective":"stdlib","tidelift":"npm/@stdlib/stdlib"}},"created_at":"2025-03-29T22:44:39.000Z","updated_at":"2025-06-08T07:03:47.000Z","dependencies_parsed_at":"2025-04-07T02:21:52.704Z","dependency_job_id":"73370c37-4c66-4d24-b98b-d6814bb4773e","html_url":"https://github.com/stdlib-js/stats-strided-smeanli","commit_stats":null,"previous_names":["stdlib-js/stats-strided-smeanli"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/stdlib-js/stats-strided-smeanli","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stdlib-js%2Fstats-strided-smeanli","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stdlib-js%2Fstats-strided-smeanli/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stdlib-js%2Fstats-strided-smeanli/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stdlib-js%2Fstats-strided-smeanli/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/stdlib-js","download_url":"https://codeload.github.com/stdlib-js/stats-strided-smeanli/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stdlib-js%2Fstats-strided-smeanli/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261220978,"owners_count":23126850,"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":["arithmetic-mean","array","average","avg","central-tendency","javascript","math","mathematics","mean","node","node-js","nodejs","one-pass","statistics","stats","stdlib","strided","strided-array","typed","welford"],"created_at":"2025-04-07T15:20:28.774Z","updated_at":"2025-06-22T01:36:05.935Z","avatar_url":"https://github.com/stdlib-js.png","language":"JavaScript","readme":"\u003c!--\n\n@license Apache-2.0\n\nCopyright (c) 2020 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# smeanli\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 Calculate the [arithmetic mean][arithmetic-mean] of a single-precision floating-point strided array using a one-pass trial mean algorithm.\n\n\u003csection class=\"intro\"\u003e\n\nThe [arithmetic mean][arithmetic-mean] is defined as\n\n\u003c!-- \u003cequation class=\"equation\" label=\"eq:arithmetic_mean\" align=\"center\" raw=\"\\mu = \\frac{1}{n} \\sum_{i=0}^{n-1} x_i\" alt=\"Equation for the arithmetic mean.\"\u003e --\u003e\n\n```math\n\\mu = \\frac{1}{n} \\sum_{i=0}^{n-1} x_i\n```\n\n\u003c!-- \u003cdiv class=\"equation\" align=\"center\" data-raw-text=\"\\mu = \\frac{1}{n} \\sum_{i=0}^{n-1} x_i\" data-equation=\"eq:arithmetic_mean\"\u003e\n    \u003cimg src=\"https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@fcea3760d526dcb339d39f08234705c4d6e44bbf/lib/node_modules/@stdlib/stats/strided/smeanli/docs/img/equation_arithmetic_mean.svg\" alt=\"Equation for the arithmetic mean.\"\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-strided-smeanli\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 smeanli = require( '@stdlib/stats-strided-smeanli' );\n```\n\n#### smeanli( N, x, strideX )\n\nComputes the [arithmetic mean][arithmetic-mean] of a single-precision floating-point strided array `x` using a one-pass trial mean algorithm.\n\n```javascript\nvar Float32Array = require( '@stdlib/array-float32' );\n\nvar x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n\nvar v = smeanli( x.length, x, 1 );\n// returns ~0.3333\n```\n\nThe function has the following parameters:\n\n-   **N**: number of indexed elements.\n-   **x**: input [`Float32Array`][@stdlib/array/float32].\n-   **strideX**: index increment for `x`.\n\nThe `N` and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the [arithmetic mean][arithmetic-mean] of every other element in `x`,\n\n```javascript\nvar Float32Array = require( '@stdlib/array-float32' );\n\nvar x = new Float32Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );\n\nvar v = smeanli( 4, x, 2 );\n// returns 1.25\n```\n\nNote that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.\n\n\u003c!-- eslint-disable stdlib/capitalized-comments --\u003e\n\n```javascript\nvar Float32Array = require( '@stdlib/array-float32' );\n\nvar x0 = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );\nvar x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element\n\nvar v = smeanli( 4, x1, 2 );\n// returns 1.25\n```\n\n#### smeanli.ndarray( N, x, stride, offset )\n\nComputes the [arithmetic mean][arithmetic-mean] of a single-precision floating-point strided array using a one-pass trial mean algorithm and alternative indexing semantics.\n\n```javascript\nvar Float32Array = require( '@stdlib/array-float32' );\n\nvar x = new Float32Array( [ 1.0, -2.0, 2.0 ] );\n\nvar v = smeanli.ndarray( x.length, x, 1, 0 );\n// returns ~0.33333\n```\n\nThe function has the following additional parameters:\n\n-   **offsetX**: starting index for `x`.\n\nWhile [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameter supports indexing semantics based on a starting index. For example, to calculate the [arithmetic mean][arithmetic-mean] for every other element in `x` starting from the second element\n\n```javascript\nvar Float32Array = require( '@stdlib/array-float32' );\n\nvar x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );\n\nvar v = smeanli.ndarray( 4, x, 2, 1 );\n// returns 1.25\n```\n\n\u003c/section\u003e\n\n\u003c!-- /.usage --\u003e\n\n\u003csection class=\"notes\"\u003e\n\n## Notes\n\n-   If `N \u003c= 0`, both functions return `NaN`.\n-   The underlying algorithm is a specialized case of Welford's algorithm. Similar to the method of assumed mean, the first strided array element is used as a trial mean. The trial mean is subtracted from subsequent data values, and the average deviations used to adjust the initial guess. Accordingly, the algorithm's accuracy is best when data is **unordered** (i.e., the data is **not** sorted in either ascending or descending order such that the first value is an \"extreme\" value).\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 discreteUniform = require( '@stdlib/random-array-discrete-uniform' );\nvar smeanli = require( '@stdlib/stats-strided-smeanli' );\n\nvar x = discreteUniform( 10, -50, 50, {\n    'dtype': 'float32'\n});\nconsole.log( x );\n\nvar v = smeanli( x.length, x, 1 );\nconsole.log( v );\n```\n\n\u003c/section\u003e\n\n\u003c!-- /.examples --\u003e\n\n\u003c!-- C interface documentation. --\u003e\n\n* * *\n\n\u003csection class=\"c\"\u003e\n\n## C APIs\n\n\u003c!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. --\u003e\n\n\u003csection class=\"intro\"\u003e\n\n\u003c/section\u003e\n\n\u003c!-- /.intro --\u003e\n\n\u003c!-- C usage documentation. --\u003e\n\n\u003csection class=\"usage\"\u003e\n\n### Usage\n\n```c\n#include \"stdlib/stats/strided/smeanli.h\"\n```\n\n#### stdlib_strided_smeanli( N, \\*X, strideX )\n\nComputes the [arithmetic mean][arithmetic-mean] of a single-precision floating-point strided array using a one-pass trial mean algorithm.\n\n```c\nconst float x[] = { 1.0f, 2.0f, 3.0f };\n\nfloat v = stdlib_strided_smeanli( 3, x, 1 );\n// returns 2.0f\n```\n\nThe function accepts the following arguments:\n\n-   **N**: `[in] CBLAS_INT` number of indexed elements.\n-   **X**: `[in] float*` input array.\n-   **strideX**: `[in] CBLAS_INT` stride length.\n\n```c\nfloat stdlib_strided_smeanli( const CBLAS_INT N, const float *X, const CBLAS_INT strideX );\n```\n\n#### stdlib_strided_smeanli_ndarray( N, \\*X, strideX, offsetX )\n\nComputes the [arithmetic mean][arithmetic-mean] of a single-precision floating-point strided array using a one-pass trial mean algorithm and alternative indexing semantics.\n\n```c\nconst float x[] = { 1.0f, 2.0f, 3.0f };\n\nfloat v = stdlib_strided_smeanli_ndarray( 3, x, 1, 0 );\n// returns 2.0f\n```\n\nThe function accepts the following arguments:\n\n-   **N**: `[in] CBLAS_INT` number of indexed elements.\n-   **X**: `[in] float*` input array.\n-   **strideX**: `[in] CBLAS_INT` stride length.\n-   **offsetX**: `[in] CBLAS_INT` starting index.\n\n```c\nfloat stdlib_strided_smeanli_ndarray( const CBLAS_INT N, const float *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );\n```\n\n\u003c/section\u003e\n\n\u003c!-- /.usage --\u003e\n\n\u003c!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. --\u003e\n\n\u003csection class=\"notes\"\u003e\n\n\u003c/section\u003e\n\n\u003c!-- /.notes --\u003e\n\n\u003c!-- C API usage examples. --\u003e\n\n\u003csection class=\"examples\"\u003e\n\n### Examples\n\n```c\n#include \"stdlib/stats/strided/smeanli.h\"\n#include \u003cstdio.h\u003e\n\nint main( void ) {\n    // Create a strided array:\n    const float x[] = { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f };\n\n    // Specify the number of elements:\n    const int N = 4;\n\n    // Specify the stride length:\n    const int strideX = 2;\n\n    // Compute the arithmetic mean:\n    float v = stdlib_strided_smeanli( N, x, strideX );\n\n    // Print the result:\n    printf( \"mean: %f\\n\", v );\n}\n```\n\n\u003c/section\u003e\n\n\u003c!-- /.examples --\u003e\n\n\u003c/section\u003e\n\n\u003c!-- /.c --\u003e\n\n* * *\n\n\u003csection class=\"references\"\u003e\n\n## References\n\n-   Welford, B. P. 1962. \"Note on a Method for Calculating Corrected Sums of Squares and Products.\" _Technometrics_ 4 (3). Taylor \u0026 Francis: 419–20. doi:[10.1080/00401706.1962.10490022][@welford:1962a].\n-   van Reeken, A. J. 1968. \"Letters to the Editor: Dealing with Neely's Algorithms.\" _Communications of the ACM_ 11 (3): 149–50. doi:[10.1145/362929.362961][@vanreeken:1968a].\n-   Ling, Robert F. 1974. \"Comparison of Several Algorithms for Computing Sample Means and Variances.\" _Journal of the American Statistical Association_ 69 (348). American Statistical Association, Taylor \u0026 Francis, Ltd.: 859–66. doi:[10.2307/2286154][@ling:1974a].\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-strided/dmeanli`][@stdlib/stats/strided/dmeanli]\u003c/span\u003e\u003cspan class=\"delimiter\"\u003e: \u003c/span\u003e\u003cspan class=\"description\"\u003ecalculate the arithmetic mean of a double-precision floating-point strided array using a one-pass trial mean algorithm.\u003c/span\u003e\n-   \u003cspan class=\"package-name\"\u003e[`@stdlib/stats-base/smean`][@stdlib/stats/base/smean]\u003c/span\u003e\u003cspan class=\"delimiter\"\u003e: \u003c/span\u003e\u003cspan class=\"description\"\u003ecalculate the arithmetic mean of a single-precision floating-point strided array.\u003c/span\u003e\n-   \u003cspan class=\"package-name\"\u003e[`@stdlib/stats-base/smeanlipw`][@stdlib/stats/base/smeanlipw]\u003c/span\u003e\u003cspan class=\"delimiter\"\u003e: \u003c/span\u003e\u003cspan class=\"description\"\u003ecalculate the arithmetic mean of a single-precision floating-point strided array using a one-pass trial mean algorithm with pairwise summation.\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-strided-smeanli.svg\n[npm-url]: https://npmjs.org/package/@stdlib/stats-strided-smeanli\n\n[test-image]: https://github.com/stdlib-js/stats-strided-smeanli/actions/workflows/test.yml/badge.svg?branch=main\n[test-url]: https://github.com/stdlib-js/stats-strided-smeanli/actions/workflows/test.yml?query=branch:main\n\n[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-strided-smeanli/main.svg\n[coverage-url]: https://codecov.io/github/stdlib-js/stats-strided-smeanli?branch=main\n\n\u003c!--\n\n[dependencies-image]: https://img.shields.io/david/stdlib-js/stats-strided-smeanli.svg\n[dependencies-url]: https://david-dm.org/stdlib-js/stats-strided-smeanli/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-strided-smeanli/tree/deno\n[deno-readme]: https://github.com/stdlib-js/stats-strided-smeanli/blob/deno/README.md\n[umd-url]: https://github.com/stdlib-js/stats-strided-smeanli/tree/umd\n[umd-readme]: https://github.com/stdlib-js/stats-strided-smeanli/blob/umd/README.md\n[esm-url]: https://github.com/stdlib-js/stats-strided-smeanli/tree/esm\n[esm-readme]: https://github.com/stdlib-js/stats-strided-smeanli/blob/esm/README.md\n[branches-url]: https://github.com/stdlib-js/stats-strided-smeanli/blob/main/branches.md\n\n[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-strided-smeanli/main/LICENSE\n\n[arithmetic-mean]: https://en.wikipedia.org/wiki/Arithmetic_mean\n\n[@stdlib/array/float32]: https://github.com/stdlib-js/array-float32\n\n[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray\n\n[@welford:1962a]: https://doi.org/10.1080/00401706.1962.10490022\n\n[@vanreeken:1968a]: https://doi.org/10.1145/362929.362961\n\n[@ling:1974a]: https://doi.org/10.2307/2286154\n\n\u003c!-- \u003crelated-links\u003e --\u003e\n\n[@stdlib/stats/strided/dmeanli]: https://github.com/stdlib-js/stats-strided-dmeanli\n\n[@stdlib/stats/base/smean]: https://github.com/stdlib-js/stats-base-smean\n\n[@stdlib/stats/base/smeanlipw]: https://github.com/stdlib-js/stats-base-smeanlipw\n\n\u003c!-- \u003c/related-links\u003e --\u003e\n\n\u003c/section\u003e\n\n\u003c!-- /.links --\u003e\n","funding_links":["https://github.com/sponsors/stdlib-js","https://opencollective.com/stdlib","https://tidelift.com/funding/github/npm/@stdlib/stdlib"],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstdlib-js%2Fstats-strided-smeanli","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstdlib-js%2Fstats-strided-smeanli","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstdlib-js%2Fstats-strided-smeanli/lists"}