Heterogeneous Effects Analysis of Conjoint Experiments R package cjbart version 0.3.2 A tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285>. This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) <doi:10.1002/sim.7803>, to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects. Software Depends: R (>= 3.6.0), BART Imports: stats, rlang, tidyr, ggplot2, randomForestSRC (>= 3.2.2), Rdpack Suggests: testthat (>= 3.0.0), knitr, parallel, rmarkdown Thomas Robinson <ts.robinson1994@gmail.com> Comprehensive R Archive Network (CRAN) Thomas Robinson [aut, cre, cph] (ORCID: <https://orcid.org/0000-0001-7097-1599>), Raymond Duch [aut, cph] (ORCID: <https://orcid.org/0000-0002-1166-7674>) Apache License (>= 2.0) 2023-09-06 application/tgz https://CRAN.R-project.org/package=cjbart doi:10.32614/CRAN.package.cjbart