{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mgcpy-a-comprehensive-high-dimensional","title":"hyppo: A Multivariate Hypothesis Testing Python Package","arxiv_id":"1907.02088","date":"2019-07-03","proceeding":null,"authors":["Sambit Panda","Satish Palaniappan","Junhao Xiong","Eric W. Bridgeford","Ronak Mehta","Cencheng Shen","Joshua T. Vogelstein"],"abstract":"We introduce hyppo, a unified library for performing multivariate hypothesis testing, including independence, two-sample, and k-sample testing. While many multivariate independence tests have R packages available, the interfaces are inconsistent and most are not available in Python. hyppo includes many state of the art multivariate testing procedures. The package is easy-to-use and is flexible enough to enable future extensions. The documentation and all releases are available at https://hyppo.neurodata.io.","url_abs":"https://arxiv.org/abs/1907.02088v7","url_pdf":"https://arxiv.org/pdf/1907.02088v7.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mgcpy-a-comprehensive-high-dimensional","repo_url":"https://github.com/neurodata/MGC-paper","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"mgcpy-a-comprehensive-high-dimensional","repo_url":"https://github.com/neurodata/hyppo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"mgcpy-a-comprehensive-high-dimensional","repo_url":"https://github.com/neurodata/mgc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"mgcpy-a-comprehensive-high-dimensional","repo_url":"https://github.com/neurodata/mgcpy","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"hypothesis-testing","task_name":"Two-sample testing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1907.02088","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}