BARMPy: Bayesian additive regression models Python package
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-validat...
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| Published in | Computational statistics Vol. 40; no. 5; pp. 2807 - 2824 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Heidelberg
Springer Nature B.V
01.06.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0943-4062 1613-9658 |
| DOI | 10.1007/s00180-024-01535-9 |
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| 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. |
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| AbstractList | 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. |
| Author | Van Boxel, Danielle |
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| Cites_doi | 10.1002/wics.1212 10.1109/MCSE.2007.55 10.1002/wics.1348 10.2139/ssrn.5031722 10.25080/Majora-92bf1922-011 10.7717/peerj-cs.1516 10.1145/2601097.2601175 10.1007/s41133-020-00038-8 10.1007/978-3-031-38747-0 10.1109/72.935086 10.1038/s41586-020-2649-2 10.1214/09-AOAS285 10.1038/s41746-021-00438-z 10.1007/b98874 10.1007/s11222-014-9511-z 10.1029/2018GC008127 10.1016/j.gca.2004.05.035 10.1023/A:1010933404324 10.1080/01621459.1998.10473750 10.1007/s11222-021-09997-3 10.1002/essoar.10507995.2 10.1007/978-3-031-16802-4_15 |
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