{"id":18530967,"url":"https://github.com/stdlib-js/blas-ext-base-dnannsumkbn","last_synced_at":"2025-04-09T13:31:43.789Z","repository":{"id":41363022,"uuid":"377232529","full_name":"stdlib-js/blas-ext-base-dnannsumkbn","owner":"stdlib-js","description":"Calculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using an improved Kahan–Babuška algorithm.","archived":false,"fork":false,"pushed_at":"2025-03-17T02:53:37.000Z","size":598,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-03-24T06:12:29.682Z","etag":null,"topics":["array","blas","compensated","extended","float64","javascript","math","mathematics","node","node-js","nodejs","statistics","stats","stdlib","strided","strided-array","sum","summation","total","typed"],"latest_commit_sha":null,"homepage":"","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":"2021-06-15T16:48:01.000Z","updated_at":"2025-03-17T01:46:43.000Z","dependencies_parsed_at":"2023-09-24T06:26:48.698Z","dependency_job_id":"9cbd5634-7e5b-4875-803a-db900de43b53","html_url":"https://github.com/stdlib-js/blas-ext-base-dnannsumkbn","commit_stats":{"total_commits":71,"total_committers":1,"mean_commits":71.0,"dds":0.0,"last_synced_commit":"21df7850d39d36780d2cf7badc9202c049ca2aee"},"previous_names":[],"tags_count":30,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stdlib-js%2Fblas-ext-base-dnannsumkbn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stdlib-js%2Fblas-ext-base-dnannsumkbn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stdlib-js%2Fblas-ext-base-dnannsumkbn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stdlib-js%2Fblas-ext-base-dnannsumkbn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/stdlib-js","download_url":"https://codeload.github.com/stdlib-js/blas-ext-base-dnannsumkbn/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248049277,"owners_count":21039195,"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":["array","blas","compensated","extended","float64","javascript","math","mathematics","node","node-js","nodejs","statistics","stats","stdlib","strided","strided-array","sum","summation","total","typed"],"created_at":"2024-11-06T18:04:40.023Z","updated_at":"2025-04-09T13:31:43.782Z","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) 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# dnannsumkbn\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 sum of double-precision floating-point strided array elements, ignoring `NaN` values and using an improved Kahan–Babuška algorithm.\n\n\u003csection class=\"intro\"\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/blas-ext-base-dnannsumkbn\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 dnannsumkbn = require( '@stdlib/blas-ext-base-dnannsumkbn' );\n```\n\n#### dnannsumkbn( N, x, strideX, out, strideOut )\n\nComputes the sum of double-precision floating-point strided array elements, ignoring `NaN` values and using an improved Kahan–Babuška algorithm.\n\n```javascript\nvar Float64Array = require( '@stdlib/array-float64' );\n\nvar x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nvar out = new Float64Array( 2 );\n\nvar v = dnannsumkbn( x.length, x, 1, out, 1 );\n// returns \u003cFloat64Array\u003e[ 1.0, 3 ]\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-   **out**: output [`Float64Array`][@stdlib/array/float64] whose first element is the sum and whose second element is the number of non-NaN elements.\n-   **strideOut**: stride length for `out`.\n\nThe `N` and stride parameters determine which elements are accessed at runtime. For example, to compute the sum of every other element:\n\n```javascript\nvar Float64Array = require( '@stdlib/array-float64' );\n\nvar x = new Float64Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );\nvar out = new Float64Array( 2 );\n\nvar v = dnannsumkbn( 4, x, 2, out, 1 );\n// returns \u003cFloat64Array\u003e[ 5.0, 2 ]\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, NaN, -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 out0 = new Float64Array( 4 );\nvar out1 = new Float64Array( out0.buffer, out0.BYTES_PER_ELEMENT*2 ); // start at 3rd element\n\nvar v = dnannsumkbn( 4, x1, 2, out1, 1 );\n// returns \u003cFloat64Array\u003e[ 5.0, 4 ]\n```\n\n#### dnannsumkbn.ndarray( N, x, strideX, offsetX, out, strideOut, offsetOut )\n\nComputes the sum of double-precision floating-point strided array elements, ignoring `NaN` values and using an improved Kahan–Babuška algorithm and alternative indexing semantics.\n\n```javascript\nvar Float64Array = require( '@stdlib/array-float64' );\n\nvar x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );\nvar out = new Float64Array( 2 );\n\nvar v = dnannsumkbn.ndarray( x.length, x, 1, 0, out, 1, 0 );\n// returns \u003cFloat64Array\u003e[ 1.0, 3 ]\n```\n\nThe function has the following additional parameters:\n\n-   **offsetX**: starting index for `x`.\n-   **offsetOut**: starting index for `out`.\n\nWhile [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying buffer, offset parameters support indexing semantics based on starting indices. For example, to calculate the sum of 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, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );\nvar out = new Float64Array( 4 );\n\nvar v = dnannsumkbn.ndarray( 4, x, 2, 1, out, 2, 1 );\n// returns \u003cFloat64Array\u003e[ 0.0, 5.0, 0.0, 4 ]\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 a sum equal to `0.0`.\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 bernoulli = require( '@stdlib/random-base-bernoulli' );\nvar discreteUniform = require( '@stdlib/random-base-discrete-uniform' );\nvar filledarrayBy = require( '@stdlib/array-filled-by' );\nvar Float64Array = require( '@stdlib/array-float64' );\nvar dnannsumkbn = require( '@stdlib/blas-ext-base-dnannsumkbn' );\n\nfunction rand() {\n    if ( bernoulli( 0.5 ) \u003c 1 ) {\n        return discreteUniform( 0, 100 );\n    }\n    return NaN;\n}\n\nvar x = filledarrayBy( 10, 'float64', rand );\nconsole.log( x );\n\nvar out = new Float64Array( 2 );\ndnannsumkbn( x.length, x, 1, out, 1 );\nconsole.log( out );\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/blas/ext/base/dnannsumkbn.h\"\n```\n\n#### stdlib_strided_dnannsumkbn( N, \\*X, strideX, \\*n )\n\nComputes the sum of double-precision floating-point strided array elements, ignoring `NaN` values and using an improved Kahan–Babuška algorithm.\n\n```c\n#include \"stdlib/blas/base/shared.h\"\n\nconst double x[] = { 1.0, 2.0, 0.0/0.0, 4.0 };\nCBLAS_INT n = 0;\n\ndouble v = stdlib_strided_dnannsumkbn( 4, x, 1, \u0026n );\n// returns 7.0\n```\n\nThe function accepts the following arguments:\n\n-   **N**: `[in] CBLAS_INT` number of indexed elements.\n-   **X**: `[in] double*` input array.\n-   **strideX**: `[in] CBLAS_INT` stride length.\n-   **n**: `[out] CBLAS_INT*` pointer for storing the number of non-NaN elements.\n\n```c\ndouble stdlib_strided_dnannsumkbn( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, CBLAS_INT *n );\n```\n\n#### stdlib_strided_dnannsumkbn_ndarray( N, \\*X, strideX, offsetX, \\*n )\n\nComputes the sum of double-precision floating-point strided array elements, ignoring `NaN` values and using an improved Kahan–Babuška algorithm and alternative indexing semantics.\n\n```c\n#include \"stdlib/blas/base/shared.h\"\n\nconst double x[] = { 1.0, 2.0, 0.0/0.0, 4.0 };\nCBLAS_INT n = 0;\n\ndouble v = stdlib_strided_dnannsumkbn_ndarray( 4, x, 1, 0, \u0026n );\n// returns 7.0\n```\n\nThe function accepts the following arguments:\n\n-   **N**: `[in] CBLAS_INT` number of indexed elements.\n-   **X**: `[in] double*` input array.\n-   **strideX**: `[in] CBLAS_INT` stride length.\n-   **offsetX**: `[in] CBLAS_INT` starting index.\n-   **n**: `[out] CBLAS_INT*` pointer for storing the number of non-NaN elements.\n\n```c\ndouble stdlib_strided_dnannsumkbn_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, CBLAS_INT *n );\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/blas/ext/base/dnannsumkbn.h\"\n#include \"stdlib/blase/base/shared.h\"\n#include \u003cstdio.h\u003e\n\nint main( void ) {\n    // Create a strided array:\n    const double x[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 0.0/0.0, 0.0/0.0 };\n\n    // Specify the number of elements:\n    const int N = 5;\n\n    // Specify the stride length:\n    const int strideX = 2;\n\n    // Initialize a variable for storing the number of non-NaN elements:\n    CBLAS_INT n = 0;\n\n    // Compute the sum:\n    double v = stdlib_strided_dnannsumkbn( N, x, strideX, \u0026n );\n\n    // Print the result:\n    printf( \"sum: %lf\\n\", v );\n    printf( \"n: %\"CBLAS_IFMT\"\\n\", n );\n}\n```\n\n\u003c/section\u003e\n\n\u003c!-- /.examples --\u003e\n\n\u003c/section\u003e\n\n\u003c!-- /.c --\u003e\n\n\u003csection class=\"references\"\u003e\n\n## References\n\n-   Neumaier, Arnold. 1974. \"Rounding Error Analysis of Some Methods for Summing Finite Sums.\" _Zeitschrift Für Angewandte Mathematik Und Mechanik_ 54 (1): 39–51. doi:[10.1002/zamm.19740540106][@neumaier: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/blas-ext/base/dnannsum`][@stdlib/blas/ext/base/dnannsum]\u003c/span\u003e\u003cspan class=\"delimiter\"\u003e: \u003c/span\u003e\u003cspan class=\"description\"\u003ecalculate the sum of double-precision floating-point strided array elements, ignoring NaN values.\u003c/span\u003e\n-   \u003cspan class=\"package-name\"\u003e[`@stdlib/blas-ext/base/dnannsumkbn2`][@stdlib/blas/ext/base/dnannsumkbn2]\u003c/span\u003e\u003cspan class=\"delimiter\"\u003e: \u003c/span\u003e\u003cspan class=\"description\"\u003ecalculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using a second-order iterative Kahan–Babuška algorithm.\u003c/span\u003e\n-   \u003cspan class=\"package-name\"\u003e[`@stdlib/blas-ext/base/dnannsumors`][@stdlib/blas/ext/base/dnannsumors]\u003c/span\u003e\u003cspan class=\"delimiter\"\u003e: \u003c/span\u003e\u003cspan class=\"description\"\u003ecalculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.\u003c/span\u003e\n-   \u003cspan class=\"package-name\"\u003e[`@stdlib/blas-ext/base/dnannsumpw`][@stdlib/blas/ext/base/dnannsumpw]\u003c/span\u003e\u003cspan class=\"delimiter\"\u003e: \u003c/span\u003e\u003cspan class=\"description\"\u003ecalculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using pairwise summation.\u003c/span\u003e\n-   \u003cspan class=\"package-name\"\u003e[`@stdlib/blas-ext/base/dsumkbn`][@stdlib/blas/ext/base/dsumkbn]\u003c/span\u003e\u003cspan class=\"delimiter\"\u003e: \u003c/span\u003e\u003cspan class=\"description\"\u003ecalculate the sum of double-precision floating-point strided array elements using an improved Kahan–Babuška algorithm.\u003c/span\u003e\n-   \u003cspan class=\"package-name\"\u003e[`@stdlib/blas-ext/base/gnannsumkbn`][@stdlib/blas/ext/base/gnannsumkbn]\u003c/span\u003e\u003cspan class=\"delimiter\"\u003e: \u003c/span\u003e\u003cspan class=\"description\"\u003ecalculate the sum of strided array elements, ignoring NaN values and using an improved Kahan–Babuška algorithm.\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/blas-ext-base-dnannsumkbn.svg\n[npm-url]: https://npmjs.org/package/@stdlib/blas-ext-base-dnannsumkbn\n\n[test-image]: https://github.com/stdlib-js/blas-ext-base-dnannsumkbn/actions/workflows/test.yml/badge.svg?branch=main\n[test-url]: https://github.com/stdlib-js/blas-ext-base-dnannsumkbn/actions/workflows/test.yml?query=branch:main\n\n[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/blas-ext-base-dnannsumkbn/main.svg\n[coverage-url]: https://codecov.io/github/stdlib-js/blas-ext-base-dnannsumkbn?branch=main\n\n\u003c!--\n\n[dependencies-image]: https://img.shields.io/david/stdlib-js/blas-ext-base-dnannsumkbn.svg\n[dependencies-url]: https://david-dm.org/stdlib-js/blas-ext-base-dnannsumkbn/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/blas-ext-base-dnannsumkbn/tree/deno\n[deno-readme]: https://github.com/stdlib-js/blas-ext-base-dnannsumkbn/blob/deno/README.md\n[umd-url]: https://github.com/stdlib-js/blas-ext-base-dnannsumkbn/tree/umd\n[umd-readme]: https://github.com/stdlib-js/blas-ext-base-dnannsumkbn/blob/umd/README.md\n[esm-url]: https://github.com/stdlib-js/blas-ext-base-dnannsumkbn/tree/esm\n[esm-readme]: https://github.com/stdlib-js/blas-ext-base-dnannsumkbn/blob/esm/README.md\n[branches-url]: https://github.com/stdlib-js/blas-ext-base-dnannsumkbn/blob/main/branches.md\n\n[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/blas-ext-base-dnannsumkbn/main/LICENSE\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[@neumaier:1974a]: https://doi.org/10.1002/zamm.19740540106\n\n\u003c!-- \u003crelated-links\u003e --\u003e\n\n[@stdlib/blas/ext/base/dnannsum]: https://github.com/stdlib-js/blas-ext-base-dnannsum\n\n[@stdlib/blas/ext/base/dnannsumkbn2]: https://github.com/stdlib-js/blas-ext-base-dnannsumkbn2\n\n[@stdlib/blas/ext/base/dnannsumors]: https://github.com/stdlib-js/blas-ext-base-dnannsumors\n\n[@stdlib/blas/ext/base/dnannsumpw]: https://github.com/stdlib-js/blas-ext-base-dnannsumpw\n\n[@stdlib/blas/ext/base/dsumkbn]: https://github.com/stdlib-js/blas-ext-base-dsumkbn\n\n[@stdlib/blas/ext/base/gnannsumkbn]: https://github.com/stdlib-js/blas-ext-base-gnannsumkbn\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%2Fblas-ext-base-dnannsumkbn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstdlib-js%2Fblas-ext-base-dnannsumkbn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstdlib-js%2Fblas-ext-base-dnannsumkbn/lists"}