Package: bartXViz
Title: Visualization of BART and BARP using SHAP
Version: 1.0.11
Date: 2026-01-26
Authors@R: c( person("Dong-eun", "Lee", , "ldongeun.leel@gmail.com", role = c("aut", "cre")),
   person("Eun-Kyung", "Lee",, role = "aut"))
Description: Complex machine learning models are often difficult to
        interpret. Shapley values serve as a powerful tool to
        understand and explain why a model makes a particular
        prediction. This package computes variable contributions using
        permutation-based Shapley values for Bayesian Additive
        Regression Trees (BART) and its extension with
        Post-Stratification (BARP). The permutation-based SHAP method
        proposed by Strumbel and Kononenko (2014)
        <doi:10.1007/s10115-013-0679-x> is grounded in data obtained
        via MCMC sampling. Similar to the BART model introduced by
        Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>,
        this package leverages Bayesian posterior samples generated
        during model estimation, allowing variable contributions to be
        computed without requiring additional sampling. The BART model
        is designed to work with the following R packages: 'BART'
        <doi:10.18637/jss.v097.i01>, 'bartMachine'
        <doi:10.18637/jss.v070.i04>, and 'dbarts'
        <https://CRAN.R-project.org/package=dbarts>. For XGBoost and
        baseline adjustments, the approach by Lundberg et al. (2020)
        <doi:10.1038/s42256-019-0138-9> is also considered. The BARP
        model proposed by Bisbee (2019) <doi:10.1017/S0003055419000480>
        was implemented with reference to
        <https://github.com/jbisbee1/BARP> and is designed to work with
        modified functions based on that implementation. BARP extends
        post-stratification by computing variable contributions within
        each stratum defined by stratifying variables. The resulting
        Shapley values are visualized through both global and local
        explanation methods.
License: GPL (>= 2)
Depends: R (>= 3.5.0), SuperLearner
Imports: bartMachine, BART, ggplot2, ggforce, data.table, ggfittext,
        ggpubr, foreach, gggenes, Rcpp, dplyr, tidyr, stringr,abind,
        utils,grid,dbarts, forcats, gridExtra,reshape2, missForest
LinkingTo: Rcpp, RcppArmadillo
Encoding: UTF-8
RoxygenNote: 7.3.3
URL: https://github.com/ldongeunl/bartXViz
Config/pak/sysreqs: cmake libfontconfig1-dev libfreetype6-dev make
        default-jdk libicu-dev libjpeg-dev libpng-dev libxml2-dev
        libssl-dev
Repository: https://ldongeunl.r-universe.dev
Date/Publication: 2026-01-26 13:06:34 UTC
RemoteUrl: https://github.com/ldongeunl/bartxviz
RemoteRef: HEAD
RemoteSha: 94198eca343118c59666c11bf9f185992c5eef5a
NeedsCompilation: yes
Packaged: 2026-07-03 15:07:23 UTC; root
Author: Dong-eun Lee [aut, cre],
  Eun-Kyung Lee [aut]
Maintainer: Dong-eun Lee <ldongeun.leel@gmail.com>