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.
Version: 0.3.2 Depends: R (≥ 3.6.0), BART Imports: stats, rlang, tidyr, ggplot2, randomForestSRC (≥ 3.2.2), Rdpack Suggests: testthat (≥ 3.0.0), knitr, parallel, rmarkdown Published: 2023-09-06 DOI: 10.32614/CRAN.package.cjbart Author: Thomas Robinson [aut, cre, cph], Raymond Duch [aut, cph] Maintainer: Thomas Robinson <ts.robinson1994 at gmail.com> BugReports: https://github.com/tsrobinson/cjbart/issues License: Apache License (≥ 2.0) URL: https://github.com/tsrobinson/cjbart NeedsCompilation: no Materials: README NEWS CRAN checks: cjbart results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=cjbart to link to this page.
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