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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.3.2 |
2026-04-09 windows/windows R-4.5 | cjbart_0.3.2.zip |
317.6 KiB |