# bartMachine: Machine Learning with Bayesian Additive Regression Trees

arXiv: https://arxiv.org/abs/1312.2171 · HTML: https://mlsanity.com/paper/1312.2171/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2013.

## Built on by (most cited first, 24 of 24)

- [Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy](https://mlsanity.com/paper/2202.09924/) (2022)
- [GP-BART: a novel Bayesian additive regression trees approach using Gaussian processes](https://mlsanity.com/paper/2204.02112/) (2022)
- [Sharded Bayesian Additive Regression Trees](https://mlsanity.com/paper/2306.00361/) (2023)
- [Adaptive network reliability analysis: Methodology and applications to power grid](https://mlsanity.com/paper/2109.05360/) (2021)
- [BARK: A Fully Bayesian Tree Kernel for Black-box Optimization](https://mlsanity.com/paper/2503.05574/) (2025)
- [Accounting for shared covariates in semi-parametric Bayesian additive regression trees](https://mlsanity.com/paper/2108.07636/) (2021)
- [SoftBart: Soft Bayesian Additive Regression Trees](https://mlsanity.com/paper/2210.16375/) (2022)
- [Bayesian CART models for insurance claims frequency](https://mlsanity.com/paper/2303.01923/) (2023)
- [Very fast Bayesian Additive Regression Trees on GPU](https://mlsanity.com/paper/2410.23244/) (2024)
- [Uncovering Dominant Features in Short-term Power Load Forecasting Based on Multi-source Feature](https://mlsanity.com/paper/2103.12534/) (2021)
- [Horseshoe Forests for High-Dimensional Causal Survival Analysis](https://mlsanity.com/paper/2507.22004/) (2025)
- [Two-sample comparison through additive tree models for density ratios](https://mlsanity.com/paper/2508.03059/) (2025)
- [Bayesian Additive Distribution Regression](https://mlsanity.com/paper/2603.06462/) (2026)
- [Machine learning in the social and health sciences](https://mlsanity.com/paper/2106.10716/) (2021)
- [A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients](https://mlsanity.com/paper/2109.06707/) (2021)
- [Extraction of Visual Information to Predict Crowdfunding Success](https://mlsanity.com/paper/2203.14806/) (2022)
- [Co-data Learning for Bayesian Additive Regression Trees](https://mlsanity.com/paper/2311.09997/) (2023)
- [What is to be gained by ensemble models in analysis of spectroscopic data?](https://mlsanity.com/paper/2404.02184/) (2024)
- [Functional BART with Shape Priors: A Bayesian Tree Approach to Constrained Functional Regression](https://mlsanity.com/paper/2502.16888/) (2025)
- [Bayesian Hybrid Machine Learning of Gallstone Risk](https://mlsanity.com/paper/2506.14561/) (2025)
- [Bayesian Neural Networks for Functional ANOVA model](https://mlsanity.com/paper/2510.00545/) (2025)
- [Improving the Efficiency of Subgroup Analysis in Randomized Controlled Trials with TMLE](https://mlsanity.com/paper/2605.15483/) (2026)
- [Semi-Parametric Bayesian Additive Regression Trees for Risk Prediction with High-Dimensional Epigenetic Signatures and Low-Dimensional Covariates](https://mlsanity.com/paper/2605.20143/) (2026)
- [Bayesian fusion forests for heterogeneous treatment effects on survival from randomised and real-world data](https://mlsanity.com/paper/2607.29295/) (2026)

## Abstract (arXiv)

We present a new package in R implementing Bayesian additive regression trees (BART). The package introduces many new features for data analysis using BART such as variable selection, interaction detection, model diagnostic plots, incorporation of missing data and the ability to save trees for future prediction. It is significantly faster than the current R implementation, parallelized, and capable of handling both large sample sizes and high-dimensional data.