{"id":22668632,"url":"https://github.com/lanl/pybass","last_synced_at":"2025-04-12T11:09:53.570Z","repository":{"id":45814837,"uuid":"342469388","full_name":"lanl/pyBASS","owner":"lanl","description":"Bayesian Adaptive Spline Surfaces for flexible and automatic regression","archived":false,"fork":false,"pushed_at":"2024-06-20T19:54:28.000Z","size":1921,"stargazers_count":22,"open_issues_count":1,"forks_count":6,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-12T11:09:28.413Z","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":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lanl.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGES.md","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}},"created_at":"2021-02-26T05:07:14.000Z","updated_at":"2024-11-13T08:17:35.000Z","dependencies_parsed_at":"2024-05-08T19:31:00.895Z","dependency_job_id":"55c7f2da-e697-4f75-a82b-52c690247433","html_url":"https://github.com/lanl/pyBASS","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lanl%2FpyBASS","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lanl%2FpyBASS/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lanl%2FpyBASS/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lanl%2FpyBASS/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lanl","download_url":"https://codeload.github.com/lanl/pyBASS/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248557844,"owners_count":21124168,"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":[],"created_at":"2024-12-09T15:16:03.406Z","updated_at":"2025-04-12T11:09:53.521Z","avatar_url":"https://github.com/lanl.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# pyBASS\n[![Build Status][build-status-img]](https://github.com/lanl/pyBASS/actions)\n\nA python implementation of Bayesian adaptive spline surfaces (BASS).  Similar\nto Bayesian multivariate adaptive regression splines (Bayesian MARS) introduced\nin Denison _et al_. (1998).\n\n## Installation\nUse\n```bash\npip install git+https://github.com/lanl/pyBASS.git\n```\n\n## Examples\n* [Example 1](examples/ex1.md) - univariate response\n* [Example 2](examples/ex2.md) - multivariate/functional response\n\n\n## References\n1. Friedman, J.H., 1991. Multivariate adaptive regression splines. _The annals of statistics_, pp.1-67.\n\n2. Denison, D.G., Mallick, B.K. and Smith, A.F., 1998. Bayesian MARS. _Statistics and Computing_, 8(4), pp.337-346.\n\n3. Francom, D., Sansó, B., Kupresanin, A. and Johannesson, G., 2018. Sensitivity analysis and emulation for functional data using Bayesian adaptive splines. _Statistica Sinica_, pp.791-816.\n\n4. Francom, D., Sansó, B., Bulaevskaya, V., Lucas, D. and Simpson, M., 2019. Inferring atmospheric release characteristics in a large computer experiment using Bayesian adaptive splines. _Journal of the American Statistical Association_, 114(528), pp.1450-1465.\n\n5. Francom, D. and Sansó, B., 2020. BASS: An R package for fitting and performing sensitivity analysis of Bayesian adaptive spline surfaces. _Journal of Statistical Software_, 94(1), pp.1-36.\n\n\n\n************\n\nCopyright 2020. Triad National Security, LLC. All rights reserved.\nThis program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos\nNational Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.\nDepartment of Energy/National Nuclear Security Administration. All rights in the program are\nreserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear\nSecurity Administration. The Government is granted for itself and others acting on its behalf a\nnonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare\nderivative works, distribute copies to the public, perform publicly and display publicly, and to permit\nothers to do so.\n\nLANL software release C19112\n\nAuthor: Devin Francom\n\n[build-status-img]: https://github.com/lanl/pyBASS/workflows/Build/badge.svg\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flanl%2Fpybass","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flanl%2Fpybass","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flanl%2Fpybass/lists"}