Package: cjbart 0.3.2

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.

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

cjbart_0.3.2.tar.gz
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cjbart.pdf |cjbart.html✨
cjbart/json (API)
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Bug tracker:https://github.com/tsrobinson/cjbart/issues

On CRAN:

7 exports 9 stars 1.48 score 79 dependencies 4 scripts 447 downloads

Last updated 1 years agofrom:b13ecf8399. Checks:OK: 7. Indexed: yes.

Exports:AMCEcjbarthet_vimpIMCEpIMCErf_vimpRMCE

Dependencies:BARTbase64encbitbit64bslibcachemclicliprcolorspacecpp11crayondata.treeDiagrammeRdigestdplyrevaluatefansifarverfastmapfontawesomefsgenericsggplot2gluegtablehighrhmshtmltoolshtmlwidgetsigraphisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmepillarpkgconfigprettyunitsprogresspurrrR6randomForestSRCrappdirsrbibutilsRColorBrewerRcppRdpackreadrrlangrmarkdownrstudioapisassscalesstringistringrsurvivaltibbletidyrtidyselecttinytextzdbutf8vctrsviridisLitevisNetworkvroomwithrxfunyaml