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bartXViz  

Visualization of BART and BARP using SHAP
View on CRAN: Click here


Download and install bartXViz package within the R console
Install from CRAN:
install.packages("bartXViz")

Install from Github:
library("remotes")
install_github("cran/bartXViz")

Install by package version:
library("remotes")
install_version("bartXViz", "1.0.3")



Attach the package and use:
library("bartXViz")
Maintained by
Dong-eun Lee
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-04-28
Latest Update: 2025-04-28
Description:
The contribution of variables in Bayesian Additive Regression Trees (BART) and Bayesian Additive Regression Trees with Post-Stratification (BARP) models is computed using permutation-based Shapley values. The computed SHAP values are then utilized to visualize the contribution of each variable through various plots. The computation of SHAP values for most models follows the methodology proposed by Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x>, while for XGBoost, the approach introduced by Lundberg et al. (2020) <doi:10.1038/s42256-019-0138-9> was also considered. The BART model was referenced based on the works of Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285> and Kapelner and Bleich (2013) <doi:10.18637/jss.v070.i04>, while the methodology for the BARP model was based on Bisbee (2019) <doi:10.1017/S0003055419000480>.
How to cite:
Dong-eun Lee (2025). bartXViz: Visualization of BART and BARP using SHAP. R package version 1.0.3, https://cran.r-project.org/web/packages/bartXViz. Accessed 08 May. 2025.
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