cjbart: Heterogeneous Effects Analysis of Conjoint Experiments
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 |
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