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 10 articles , 3 standard articles )
Showing results 1 to 10 of 10.
- Page, Garritt L.; Quintana, Fernando A.; Rosner, Gary L.: Discovering interactions using covariate informed random partition models (2021)
- Rodney Sparapani, Charles Spanbauer, Robert McCulloch: Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package (2021) not zbMATH
- Berk, Richard A.: Statistical learning from a regression perspective (2020)
- Park, Soyoung; Carriquiry, Alicia: Learning algorithms to evaluate forensic glass evidence (2019)
- Hernández, Belinda; Raftery, Adrian E.; Pennington, Stephen R.; Parnell, Andrew C.: Bayesian additive regression trees using Bayesian model averaging (2018)
- Linero, Antonio R.: Bayesian regression trees for high-dimensional prediction and variable selection (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) not zbMATH
- 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)