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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 |
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 |
Reference manual: | cjbart.pdf |
Vignettes: |
Introduction to cjbart |
Package source: | cjbart_0.3.2.tar.gz |
Windows binaries: | r-devel: cjbart_0.3.2.zip, r-release: cjbart_0.3.2.zip, r-oldrel: cjbart_0.3.2.zip |
macOS binaries: | r-release (arm64): cjbart_0.3.2.tgz, r-oldrel (arm64): cjbart_0.3.2.tgz, r-release (x86_64): cjbart_0.3.2.tgz, r-oldrel (x86_64): cjbart_0.3.2.tgz |
Old sources: | cjbart archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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