Package: cjbart Title: Heterogeneous Effects Analysis of Conjoint Experiments Version: 0.3.2 Authors@R: c( person(given = "Thomas", family = "Robinson", role = c("aut", "cre", "cph"), email = "ts.robinson1994@gmail.com", comment = c(ORCID = "0000-0001-7097-1599")), person(given = "Raymond", family = "Duch", role = c("aut","cph"), email = "raymond.duch@nuffield.ox.ac.uk", comment = c(ORCID = "0000-0002-1166-7674")) ) 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. License: Apache License (>= 2.0) Encoding: UTF-8 Roxygen: list(markdown = TRUE) RoxygenNote: 7.2.3 Depends: R (>= 3.6.0), BART Imports: stats, rlang, tidyr, ggplot2, randomForestSRC (>= 3.2.2), Rdpack Suggests: testthat (>= 3.0.0), knitr, parallel, rmarkdown VignetteBuilder: knitr URL: https://github.com/tsrobinson/cjbart BugReports: https://github.com/tsrobinson/cjbart/issues RdMacros: Rdpack Config/testthat/edition: 3 Config/pak/sysreqs: cmake libglpk-dev make libicu-dev libuv1-dev libxml2-dev libx11-dev Repository: https://tsrobinson.r-universe.dev Date/Publication: 2023-09-06 15:00:25 UTC RemoteUrl: https://github.com/tsrobinson/cjbart RemoteRef: HEAD RemoteSha: b13ecf8399fbf5e4f69b5ffa39964d78fcbd8441 NeedsCompilation: no Packaged: 2026-07-04 13:32:53 UTC; root Author: Thomas Robinson [aut, cre, cph] (ORCID: ), Raymond Duch [aut, cph] (ORCID: ) Maintainer: Thomas Robinson