{"title":"BARMPy: Bayesian additive regression models Python package","authors":"Danielle Van Boxel","doi":"10.1007/s00180-024-01535-9","DOIUrl":null,"url":null,"abstract":"<p>We make Bayesian additive regression networks (BARN) available as a Python package, <span>barmpy</span>, with documentation at https://dvbuntu.github.io/barmpy/ for general machine learning practitioners. Our object-oriented design is compatible with SciKit-Learn, allowing usage of their tools like cross-validation. To ease learning to use <span>barmpy</span>, we produce a companion tutorial that expands on reference information in the documentation. Any interested user can <span>pip install barmpy</span> from the official PyPi repository. <span>barmpy</span> also serves as a baseline Python library for generic Bayesian additive regression models.</p>","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"55 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s00180-024-01535-9","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
We make Bayesian additive regression networks (BARN) available as a Python package, barmpy, with documentation at https://dvbuntu.github.io/barmpy/ for general machine learning practitioners. Our object-oriented design is compatible with SciKit-Learn, allowing usage of their tools like cross-validation. To ease learning to use barmpy, we produce a companion tutorial that expands on reference information in the documentation. Any interested user can pip install barmpy from the official PyPi repository. barmpy also serves as a baseline Python library for generic Bayesian additive regression models.
期刊介绍:
Computational Statistics (CompStat) is an international journal which promotes the publication of applications and methodological research in the field of Computational Statistics. The focus of papers in CompStat is on the contribution to and influence of computing on statistics and vice versa. The journal provides a forum for computer scientists, mathematicians, and statisticians in a variety of fields of statistics such as biometrics, econometrics, data analysis, graphics, simulation, algorithms, knowledge based systems, and Bayesian computing. CompStat publishes hardware, software plus package reports.