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:

Reference manual:	cjbart.pdf
Vignettes:	Introduction to cjbart

Downloads:

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

Linking:

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