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 |
Documentation:
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