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cjbart  

Heterogeneous Effects Analysis of Conjoint Experiments
View on CRAN: Click here


Download and install cjbart package within the R console
Install from CRAN:
install.packages("cjbart")

Install from Github:
library("remotes")
install_github("cran/cjbart")

Install by package version:
library("remotes")
install_version("cjbart", "0.3.2")



Attach the package and use:
library("cjbart")
Maintained by
Thomas Robinson
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-05-25
Latest Update: 2023-06-07
Description:
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.
How to cite:
Thomas Robinson (2021). cjbart: Heterogeneous Effects Analysis of Conjoint Experiments. R package version 0.3.2, https://cran.r-project.org/web/packages/cjbart. Accessed 03 Dec. 2024.
Previous versions and publish date:
0.1.0 (2021-05-25 13:50), 0.2.0 (2021-11-03 21:00), 0.2.1 (2022-02-14 15:50), 0.2.2 (2022-03-02 15:20), 0.3.0 (2023-04-11 23:30), 0.3.1 (2023-06-07 12:20)
Other packages that cited cjbart R package
View cjbart citation profile
Other R packages that cjbart depends, imports, suggests or enhances
Complete documentation for cjbart
Functions, R codes and Examples using the cjbart R package
Some associated functions: AMCE . IMCE . RMCE . cjbart . het_vimp . pIMCE . plot.cjbart . plot.cjbart.vimp . rf_vimp . summary.cjbart . 
Some associated R codes: cjbart.R . generics.R . pIMCE.R . utils.R . vimp.R . zzz.R .  Full cjbart package functions and examples
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