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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.

Versions across snapshots

VersionRepositoryFileSize
0.3.2 rolling linux/jammy R-4.5 cjbart_0.3.2.tar.gz 317.5 KiB
0.3.2 rolling linux/noble R-4.5 cjbart_0.3.2.tar.gz 317.6 KiB
0.3.2 rolling source/ R- cjbart_0.3.2.tar.gz 262.0 KiB
0.3.2 latest linux/jammy R-4.5 cjbart_0.3.2.tar.gz 317.5 KiB
0.3.2 latest linux/noble R-4.5 cjbart_0.3.2.tar.gz 317.6 KiB
0.3.2 latest source/ R- cjbart_0.3.2.tar.gz 262.0 KiB
0.3.2 2026-04-26 source/ R- cjbart_0.3.2.tar.gz 262.0 KiB
0.3.2 2026-04-23 source/ R- cjbart_0.3.2.tar.gz 262.0 KiB
0.3.2 2026-04-09 windows/windows R-4.5 cjbart_0.3.2.zip 317.6 KiB
0.3.2 2025-04-20 source/ R- cjbart_0.3.2.tar.gz 262.0 KiB

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