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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.
Getting started
Browse package contents
Package details |
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| Author | Thomas Robinson [aut, cre, cph] (<https://orcid.org/0000-0001-7097-1599>), Raymond Duch [aut, cph] (<https://orcid.org/0000-0002-1166-7674>) |
| Maintainer | Thomas Robinson <ts.robinson1994@gmail.com> |
| License | Apache License (>= 2.0) |
| Version | 0.3.2 |
| URL | https://github.com/tsrobinson/cjbart |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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