cjbart: Heterogeneous Effects Analysis of Conjoint Experiments (original) (raw)
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 |
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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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