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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.12051 |
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| _version_ | 1866908841999859712 |
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| author | Herren, Andrew Hahn, P. Richard Murray, Jared Carvalho, Carlos |
| author_facet | Herren, Andrew Hahn, P. Richard Murray, Jared Carvalho, Carlos |
| contents | stochtree is a C++ library for Bayesian tree ensemble models such as BART and Bayesian Causal Forests (BCF), as well as user-specified variations. Unlike previous BART packages, stochtree provides bindings to both R and Python for full interoperability. stochtree boasts a more comprehensive range of models relative to previous packages, including heteroskedastic forests, random effects, and treed linear models. Additionally, stochtree offers flexible handling of model fits: the ability to save model fits, reinitialize models from existing fits (facilitating improved model initialization heuristics), and pass fits between R and Python. On both platforms, stochtree exposes lower-level functionality, allowing users to specify models incorporating Bayesian tree ensembles without needing to modify C++ code. We illustrate the use of stochtree in three settings: i) straightfoward applications of existing models such as BART and BCF, ii) models that include more sophisticated components like heteroskedasticity and leaf-wise regression models, and iii) as a component of custom MCMC routines to fit nonstandard tree ensemble models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12051 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | StochTree: BART-based modeling in R and Python Herren, Andrew Hahn, P. Richard Murray, Jared Carvalho, Carlos Computation stochtree is a C++ library for Bayesian tree ensemble models such as BART and Bayesian Causal Forests (BCF), as well as user-specified variations. Unlike previous BART packages, stochtree provides bindings to both R and Python for full interoperability. stochtree boasts a more comprehensive range of models relative to previous packages, including heteroskedastic forests, random effects, and treed linear models. Additionally, stochtree offers flexible handling of model fits: the ability to save model fits, reinitialize models from existing fits (facilitating improved model initialization heuristics), and pass fits between R and Python. On both platforms, stochtree exposes lower-level functionality, allowing users to specify models incorporating Bayesian tree ensembles without needing to modify C++ code. We illustrate the use of stochtree in three settings: i) straightfoward applications of existing models such as BART and BCF, ii) models that include more sophisticated components like heteroskedasticity and leaf-wise regression models, and iii) as a component of custom MCMC routines to fit nonstandard tree ensemble models. |
| title | StochTree: BART-based modeling in R and Python |
| topic | Computation |
| url | https://arxiv.org/abs/2512.12051 |