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. (2024) <doi:10.1214/24-BA1470>.
| Version: |
2.0.3 |
| Imports: |
Rcpp (≥ 1.0.12), glmnet (≥ 4.0), methods |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
igraph, knitr, quarto |
| Published: |
2026-02-12 |
| DOI: |
10.32614/CRAN.package.flexBART |
| Author: |
Sameer K. Deshpande
 [aut, cre],
George Perrett
[aut],
Ryan Yee [aut],
Cecilia Balocchi
[aut],
Jennifer Hill
[aut] |
| Maintainer: |
Sameer K. Deshpande <sameer.deshpande at wisc.edu> |
| License: |
GPL (≥ 3) |
| URL: |
https://skdeshpande91.github.io/flexBART/ |
| NeedsCompilation: |
yes |
| Citation: |
flexBART citation info |
| CRAN checks: |
flexBART results |
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