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.">

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
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 ORCID iD [aut, cre, cph], Raymond Duch ORCID iD [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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