Package: stochtree 0.4.6.9000

stochtree: Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference

Flexible stochastic tree ensemble software. Robust implementations of Bayesian Additive Regression Trees (BART) (Chipman, George, McCulloch (2010) <doi:10.1214/09-AOAS285>) for supervised learning and Bayesian Causal Forests (BCF) (Hahn, Murray, Carvalho (2020) <doi:10.1214/19-BA1195>) for causal inference. Enables model serialization and parallel sampling and provides a low-level interface for custom stochastic forest samplers. Includes the grow-from-root algorithm for accelerated forest sampling (He and Hahn (2021) <doi:10.1080/01621459.2021.1942012>), a log-linear leaf model for forest-based heteroskedasticity (Murray (2020) <doi:10.1080/01621459.2020.1813587>), and the cloglog BART model of Alam and Linero (2025) <doi:10.48550/arXiv.2502.00606> for ordinal outcomes.

Authors:Drew Herren [aut, cre], Richard Hahn [aut], Jared Murray [aut], Carlos Carvalho [aut], Jingyu He [aut], Pedro Lima [ctb], Entejar Alam [ctb], stochtree contributors [cph], Eigen contributors [cph], xgboost contributors [cph], treelite contributors [cph], Microsoft Corporation [cph], Niels Lohmann [cph], Daniel Lemire [cph], Victor Zverovich [cph]