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Apache-2.0\n\nCopyright (c) 2025 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# dmeanwd\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 the [arithmetic mean][arithmetic-mean] of a double-precision floating-point strided array using Welford's algorithm.\n\n\u003csection class=\"installation\"\u003e\n\n## Installation\n\n```bash\nnpm install @stdlib/stats-strided-wasm-dmeanwd\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 dmeanwd = require( '@stdlib/stats-strided-wasm-dmeanwd' );\n```\n\n#### dmeanwd.main( N, x, strideX )\n\nComputes the [arithmetic mean][arithmetic-mean] of a double-precision floating-point strided array using Welford's algorithm.\n\n```javascript\nvar Float64Array = require( '@stdlib/array-float64' );\n\nvar x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n\nvar y = dmeanwd.main( 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 [`Float64Array`][@stdlib/array/float64].\n-   **strideX**: stride length for `x`.\n\nThe `N` and stride parameters determine which elements in the strided array are accessed at runtime. For example, to access every other element in `x`,\n\n```javascript\nvar Float64Array = require( '@stdlib/array-float64' );\n\nvar x = new Float64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );\n\nvar y = dmeanwd.main( 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 Float64Array = require( '@stdlib/array-float64' );\n\nvar x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );\nvar x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element\n\nvar y = dmeanwd.main( 4, x1, 2 );\n// returns 1.25\n```\n\n#### dmeanwd.ndarray( N, x, strideX, offsetX )\n\nComputes the [arithmetic mean][arithmetic-mean] of a double-precision floating-point strided array using Welford's algorithm and alternative indexing semantics.\n\n```javascript\nvar Float64Array = require( '@stdlib/array-float64' );\n\nvar x = new Float64Array( [ 1.0, -2.0, 2.0 ] );\n\nvar y = dmeanwd.ndarray( x.length, x, 1, 0 );\n// returns ~0.3333\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 access every other element starting from the second element:\n\n```javascript\nvar Float64Array = require( '@stdlib/array-float64' );\n\nvar x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );\n\nvar y = dmeanwd.ndarray( 4, x, 2, 1 );\n// returns 1.25\n```\n\n* * *\n\n### Module\n\n#### dmeanwd.Module( memory )\n\nReturns a new WebAssembly [module wrapper][@stdlib/wasm/module-wrapper] instance which uses the provided WebAssembly [memory][@stdlib/wasm/memory] instance as its underlying memory.\n\n\u003c!-- eslint-disable node/no-sync --\u003e\n\n```javascript\nvar Memory = require( '@stdlib/wasm-memory' );\n\n// Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):\nvar mem = new Memory({\n    'initial': 10,\n    'maximum': 100\n});\n\n// Create a new routine:\nvar mod = new dmeanwd.Module( mem );\n// returns \u003cModule\u003e\n\n// Initialize the routine:\nmod.initializeSync();\n```\n\n#### dmeanwd.Module.prototype.main( N, xp, sx )\n\nComputes the [arithmetic mean][arithmetic-mean] of a double-precision floating-point strided array using Welford's algorithm.\n\n\u003c!-- eslint-disable node/no-sync --\u003e\n\n```javascript\nvar Memory = require( '@stdlib/wasm-memory' );\nvar oneTo = require( '@stdlib/array-one-to' );\nvar zeros = require( '@stdlib/array-zeros' );\n\n// Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):\nvar mem = new Memory({\n    'initial': 10,\n    'maximum': 100\n});\n\n// Create a new routine:\nvar mod = new dmeanwd.Module( mem );\n// returns \u003cModule\u003e\n\n// Initialize the routine:\nmod.initializeSync();\n\n// Define a vector data type:\nvar dtype = 'float64';\n\n// Specify a vector length:\nvar N = 3;\n\n// Define a pointer (i.e., byte offset) for storing the input vector:\nvar xptr = 0;\n\n// Write vector values to module memory:\nmod.write( xptr, oneTo( N, dtype ) );\n\n// Perform computation:\nvar y = mod.main( N, xptr, 1 );\n// returns 2.0\n```\n\nThe function has the following parameters:\n\n-   **N**: number of indexed elements.\n-   **xp**: input [`Float64Array`][@stdlib/array/float64] pointer (i.e., byte offset).\n-   **sx**: stride length for `x`.\n\n#### dmeanwd.Module.prototype.ndarray( N, alpha, xp, sx, ox )\n\nComputes the [arithmetic mean][arithmetic-mean] of a double-precision floating-point strided array using Welford's algorithm and alternative indexing semantics.\n\n\u003c!-- eslint-disable node/no-sync --\u003e\n\n```javascript\nvar Memory = require( '@stdlib/wasm-memory' );\nvar oneTo = require( '@stdlib/array-one-to' );\nvar zeros = require( '@stdlib/array-zeros' );\n\n// Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):\nvar mem = new Memory({\n    'initial': 10,\n    'maximum': 100\n});\n\n// Create a new routine:\nvar mod = new dmeanwd.Module( mem );\n// returns \u003cModule\u003e\n\n// Initialize the routine:\nmod.initializeSync();\n\n// Define a vector data type:\nvar dtype = 'float64';\n\n// Specify a vector length:\nvar N = 3;\n\n// Define a pointer (i.e., byte offset) for storing the input vector:\nvar xptr = 0;\n\n// Write vector values to module memory:\nmod.write( xptr, oneTo( N, dtype ) );\n\n// Perform computation:\nvar y = mod.ndarray( N, xptr, 1, 0 );\n// returns 2.0\n```\n\nThe function has the following additional parameters:\n\n-   **ox**: starting index for `x`.\n\n\u003c/section\u003e\n\n\u003c!-- /.usage --\u003e\n\n\u003csection class=\"notes\"\u003e\n\n* * *\n\n## Notes\n\n-   If `N \u003c= 0`, both `main` and `ndarray` methods return `0.0`.\n-   This package implements routines using WebAssembly. When provided arrays which are not allocated on a `dmeanwd` module memory instance, data must be explicitly copied to module memory prior to computation. Data movement may entail a performance cost, and, thus, if you are using arrays external to module memory, you should prefer using [`@stdlib/stats-strided/dmeanwd`][@stdlib/stats/strided/dmeanwd]. However, if working with arrays which are allocated and explicitly managed on module memory, you can achieve better performance when compared to the pure JavaScript implementations found in [`@stdlib/stats/strided/dmeanwd`][@stdlib/stats/strided/dmeanwd]. Beware that such performance gains may come at the cost of additional complexity when having to perform manual memory management. Choosing between implementations depends heavily on the particular needs and constraints of your application, with no one choice universally better than the other.\n\n\u003c/section\u003e\n\n\u003c!-- /.notes --\u003e\n\n\u003csection class=\"examples\"\u003e\n\n* * *\n\n## Examples\n\n\u003c!-- eslint no-undef: \"error\" --\u003e\n\n```javascript\nvar discreteUniform = require( '@stdlib/random-array-discrete-uniform' );\nvar dmeanwd = require( '@stdlib/stats-strided-wasm-dmeanwd' );\n\nvar opts = {\n    'dtype': 'float64'\n};\nvar x = discreteUniform( 10, 0, 100, opts );\nconsole.log( x );\n\nvar y = dmeanwd.ndarray( x.length, x, 1, 0 );\nconsole.log( y );\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\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-strided-wasm-dmeanwd.svg\n[npm-url]: https://npmjs.org/package/@stdlib/stats-strided-wasm-dmeanwd\n\n[test-image]: https://github.com/stdlib-js/stats-strided-wasm-dmeanwd/actions/workflows/test.yml/badge.svg?branch=main\n[test-url]: https://github.com/stdlib-js/stats-strided-wasm-dmeanwd/actions/workflows/test.yml?query=branch:main\n\n[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-strided-wasm-dmeanwd/main.svg\n[coverage-url]: https://codecov.io/github/stdlib-js/stats-strided-wasm-dmeanwd?branch=main\n\n\u003c!--\n\n[dependencies-image]: https://img.shields.io/david/stdlib-js/stats-strided-wasm-dmeanwd.svg\n[dependencies-url]: https://david-dm.org/stdlib-js/stats-strided-wasm-dmeanwd/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-strided-wasm-dmeanwd/tree/deno\n[deno-readme]: https://github.com/stdlib-js/stats-strided-wasm-dmeanwd/blob/deno/README.md\n[umd-url]: https://github.com/stdlib-js/stats-strided-wasm-dmeanwd/tree/umd\n[umd-readme]: https://github.com/stdlib-js/stats-strided-wasm-dmeanwd/blob/umd/README.md\n[esm-url]: https://github.com/stdlib-js/stats-strided-wasm-dmeanwd/tree/esm\n[esm-readme]: https://github.com/stdlib-js/stats-strided-wasm-dmeanwd/blob/esm/README.md\n[branches-url]: https://github.com/stdlib-js/stats-strided-wasm-dmeanwd/blob/main/branches.md\n\n[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-strided-wasm-dmeanwd/main/LICENSE\n\n[arithmetic-mean]: https://en.wikipedia.org/wiki/Arithmetic_mean\n\n[@stdlib/array/float64]: https://github.com/stdlib-js/array-float64\n\n[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray\n\n[@stdlib/wasm/memory]: https://github.com/stdlib-js/wasm-memory\n\n[@stdlib/wasm/module-wrapper]: https://github.com/stdlib-js/wasm-module-wrapper\n\n[@stdlib/stats/strided/dmeanwd]: https://github.com/stdlib-js/stats-strided-dmeanwd\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-strided-wasm-dmeanwd","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstdlib-js%2Fstats-strided-wasm-dmeanwd","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstdlib-js%2Fstats-strided-wasm-dmeanwd/lists"}