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) . 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) , to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects.


Reference manual

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0.2.0 by Thomas Robinson, 3 months ago


Report a bug at https://github.com/tsrobinson/cjbart/issues

Browse source code at https://github.com/cran/cjbart

Authors: Thomas Robinson [aut, cre, cph] , Raymond Duch [aut, cph]

Documentation:   PDF Manual  

Apache License (>= 2.0) license

Imports stats, rlang, tidyr, ggplot2, Rdpack, randomForestSRC

Depends on BART

Suggests testthat, knitr, cregg, rmarkdown

See at CRAN