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flexBART: A More Flexible BART Model
Implements a faster and more expressive version of Bayesian Additive Regression Trees that, at a high level, approximates unknown functions as a weighted sum of binary regression tree ensembles. Supports fitting (generalized) linear varying coefficient models that posits a linear relationship between the inverse link and some covariates but allows that relationship to change as a function of other covariates. Additionally supports fitting heteroscedastic BART models, in which both the mean and log-variance are approximated with separate regression tree ensembles. A formula interface allows for different splitting variables to be used in each ensemble. For more details see Deshpande (2025) <doi:10.1080/10618600.2024.2431072> and Deshpande et al. (2026) <doi:10.1214/24-BA1470>.
Getting started
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Package details |
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| Author | Sameer K. Deshpande [aut, cre] (ORCID: <https://orcid.org/0000-0003-4116-5533>), George Perrett [aut] (ORCID: <https://orcid.org/0000-0002-1930-2581>), Ryan Yee [aut] (ORCID: <https://orcid.org/0009-0005-5691-4009>), Cecilia Balocchi [aut] (ORCID: <https://orcid.org/0000-0002-1234-6063>), Jennifer Hill [aut] (ORCID: <https://orcid.org/0000-0003-4983-2206>) |
| Maintainer | Sameer K. Deshpande <sameer.deshpande@wisc.edu> |
| License | GPL (>= 3) |
| Version | 2.0.6 |
| URL | https://skdeshpande91.github.io/flexBART/ |
| Package repository | View on CRAN |
| Installation |
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