{"id":32078592,"url":"https://github.com/nfft/pyanovaapprox","last_synced_at":"2025-10-19T08:58:26.030Z","repository":{"id":315445825,"uuid":"1059497252","full_name":"NFFT/pyANOVAapprox","owner":"NFFT","description":"Approximation Package for High-Dimensional Functions in Python","archived":false,"fork":false,"pushed_at":"2025-09-18T15:30:26.000Z","size":72,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-09-18T18:26:40.454Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/NFFT.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-09-18T14:20:53.000Z","updated_at":"2025-09-18T15:30:10.000Z","dependencies_parsed_at":"2025-09-18T18:26:42.461Z","dependency_job_id":"0a3f045d-82c1-433e-8053-2e87e3230934","html_url":"https://github.com/NFFT/pyANOVAapprox","commit_stats":null,"previous_names":["nfft/pyanovaapprox"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/NFFT/pyANOVAapprox","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NFFT%2FpyANOVAapprox","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NFFT%2FpyANOVAapprox/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NFFT%2FpyANOVAapprox/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NFFT%2FpyANOVAapprox/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/NFFT","download_url":"https://codeload.github.com/NFFT/pyANOVAapprox/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NFFT%2FpyANOVAapprox/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279760842,"owners_count":26222502,"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","status":"online","status_checked_at":"2025-10-19T02:00:07.647Z","response_time":64,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":[],"created_at":"2025-10-19T08:58:24.078Z","updated_at":"2025-10-19T08:58:26.024Z","avatar_url":"https://github.com/NFFT.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# pyANOVAapprox\n\n[![](https://github.com/NFFT/pyANOVAapprox/actions/workflows/ci.yml/badge.svg)](https://github.com/NFFT/pyANOVAapprox/actions/workflows/ci.yml)\n\nThis package provides a framework for the method ANOVAapprox to approximate high-dimensional functions with a low superposition dimension or a sparse ANOVA decomposition from scattered data. The method has been dicussed and applied in the following articles/preprints:\n\n\u003cul\u003e\n  \u003cli\u003eD. Potts and M. Schmischke \u003cbr\u003e \n  \u003cb\u003eInterpretable transformed ANOVA approximation on the example of the prevention of forest fires\u003c/b\u003e \u003cbr\u003e\n  \u003ca href=\"https://arxiv.org/abs/2110.07353\"\u003earXiv\u003c/a\u003e, \u003ca href=\"https://www-user.tu-chemnitz.de/~mischmi/papers/transformedanova.pdf\"\u003ePDF\u003c/a\u003e\u003c/li\u003e\n  \u003cli\u003eF. Bartel, D. Potts und M. Schmischke \u003cbr\u003e \n  \u003cb\u003eGrouped transformations and Regularization in high-dimensional explainable ANOVA approximation\u003c/b\u003e \u003cbr\u003e\n  SIAM Journal on Scientific Computing (accepted) \u003cbr\u003e\n  \u003ca href=\"https://arxiv.org/abs/2010.10199\"\u003earXiv\u003c/a\u003e, \u003ca href=\"https://www-user.tu-chemnitz.de/~mischmi/papers/groupedtransforms.pdf\"\u003ePDF\u003c/a\u003e\u003c/li\u003e\n  \u003cli\u003eD. Potts and M. Schmischke \u003cbr\u003e \n  \u003cb\u003eInterpretable approximation of high-dimensional data\u003c/b\u003e \u003cbr\u003e\n  SIAM Journal on Mathematics of Data Science (accepted) \u003cbr\u003e\n  \u003ca href=\"https://arxiv.org/abs/2103.13787\"\u003earXiv\u003c/a\u003e, \u003ca href=\"https://www-user.tu-chemnitz.de/~mischmi/papers/attributeranking.pdf\"\u003ePDF\u003c/a\u003e, \u003ca href=\"https://github.com/NFFT/AttributeRankingExamples\"\u003eSoftware\u003c/a\u003e\u003c/li\u003e\n  \u003cli\u003eD. Potts and M. Schmischke \u003cbr\u003e \n  \u003cb\u003eLearning multivariate functions with low-dimensional structures using polynomial bases\u003c/b\u003e\u003cbr\u003e\n  Journal of Computational and Applied Mathematics 403, 113821, 2021\u003cbr\u003e\n  \u003ca href=\"https://doi.org/10.1016/j.cam.2021.113821\"\u003eDOI\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/1912.03195\"\u003earXiv\u003c/a\u003e, \u003ca href=\"https://www-user.tu-chemnitz.de/~mischmi/papers/anovacube.pdf\"\u003ePDF\u003c/a\u003e\u003c/li\u003e\n  \u003cli\u003eD. Potts and M. Schmischke \u003cbr\u003e \n  \u003cb\u003eApproximation of high-dimensional periodic functions with Fourier-based methods\u003c/b\u003e\u003cbr\u003e\n  SIAM Journal on Numerical Analysis 59 (5), 2393-2429, 2021\u003cbr\u003e\n  \u003ca href=\"https://doi.org/10.1137/20M1354921\"\u003eDOI\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/1907.11412\"\u003earXiv\u003c/a\u003e, \u003ca href=\"https://www-user.tu-chemnitz.de/~mischmi/papers/anovafourier.pdf\"\u003ePDF\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eL. Lippert, D. Potts and T. Ullrich \u003cbr\u003e \n  \u003cb\u003eFast Hyperbolic Wavelet Regression meets ANOVA\u003c/b\u003e\u003cbr\u003e\n  ArXiv: 2108.13197\u003cbr\u003e\n  \u003ca href=\"https://arxiv.org/abs/2108.13197\"\u003earXiv\u003c/a\u003e, \u003ca href=\"https://www-user.tu-chemnitz.de/~lipl/paper/HWR.pdf\"\u003ePDF\u003c/a\u003e\u003c/li\u003e\n\n\n\u003c/ul\u003e\n\n`pyANOVAapprox` provides the following functionality:\n- approximation of high-dimensional periodic and nonperiodic functions with a sparse ANOVA decomposition\n- analysis tools for interpretability (global sensitvitiy indices, attribute ranking, shapley values)\n\n## Getting started\n\nThe [pyANOVAapprox package](https://pypi.org/project/pyANOVAapprox/) can be installed via pip:\n\n```\npip install -i https://test.pypi.org/simple/ pyANOVAapprox\n```\n\nRead the [documentation](https://nfft.github.io/pyANOVAapprox/) for specific usage information.\n\nRequirements\n------------\n\n- Python 3.8 or greater\n- pyGroupedTransforms 0.1.0 or greater\n- NumPy 2.0.0 or greater\n- SciPy 1.16.0 or greater\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnfft%2Fpyanovaapprox","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnfft%2Fpyanovaapprox","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnfft%2Fpyanovaapprox/lists"}