R package bartMachine: Machine learning with Bayesian additive regression trees. 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.
Keywords for this software
References in zbMATH (referenced in 5 articles , 3 standard articles )
Showing results 1 to 5 of 5.
- Hernández, Belinda; Raftery, Adrian E.; Pennington, Stephen R.; Parnell, Andrew C.: Bayesian additive regression trees using Bayesian model averaging (2018)
- Conversano, Claudio; Dusseldorp, Elise: Modeling threshold interaction effects through the logistic classification trunk (2017)
- Adam Kapelner and Justin Bleich: bartMachine: Machine Learning with Bayesian Additive Regression Trees (2016)
- Kapelner, Adam; Bleich, Justin: Prediction with missing data via Bayesian additive regression trees (2015)
- Bleich, Justin; Kapelner, Adam; George, Edward I.; Jensen, Shane T.: Variable selection for BART: an application to gene regulation (2014)