Package: bartMan 0.1.1
Alan Inglis
bartMan: Create Visualisations for BART Models
Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman, H. A., George, E. I., & McCulloch, R. E. 2010) <doi:10.1214/09-AOAS285> model fits. We construct conventional plots to analyze a model’s performance and stability as well as create new tree-based plots to analyze variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our visualisations are designed to work with the most popular BART R packages available, namely 'BART' Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 <doi:10.18637/jss.v097.i01>, 'dbarts' (Vincent Dorie 2023) <https://CRAN.R-project.org/package=dbarts>, and 'bartMachine' (Adam Kapelner and Justin Bleich 2016) <doi:10.18637/jss.v070.i04>.
Authors:
bartMan_0.1.1.tar.gz
bartMan_0.1.1.tar.gz(r-4.5-noble)bartMan_0.1.1.tar.gz(r-4.4-noble)
bartMan_0.1.1.tgz(r-4.4-emscripten)bartMan_0.1.1.tgz(r-4.3-emscripten)
bartMan.pdf |bartMan.html✨
bartMan/json (API)
| # Install 'bartMan' in R: |
| install.packages('bartMan', repos = 'https://cloud.r-project.org') |
- input_data - Input_data
- tree_data_example - Tree_data_example
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 8 months agofrom:e5c750c705. Checks:2 OK, 1 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 22 2025 |
| R-4.5-linux | NOTE | Mar 22 2025 |
| R-4.4-linux | OK | Mar 22 2025 |
Exports:acceptRatebartClassifDiagbartDiagbartRegrDiagbivariate_rangebivariate_scaleclusterTreescombineDummyextractTreeDatagetChildrengetObservationsguide_colorfanguide_colourfanlocalProceduremdsBartnode_depthpal_vsuppermVimppermVintplotProximityplotSingleTreeplotTreesproximityMatrixRangeBivariateScaleBivariatesort_trees_by_depthMaxsplitDensityterminalFunctiontrain_bivariatetree_dataframetreeBarPlottreeDepthtreeListtreeNodesvimpBartvimpPlotvintPlotviviBartviviBartMatrixviviBartPlot
Dependencies:abindarrayhelpersbackportsBARTbartMachinebartMachineJARsbase64encbitbit64bslibcacachemcheckmateclicliprclustercodacodetoolscolorspacecowplotcpp11crayondbartsDendSerdigestdistributionaldoRNGdplyrevaluatefansifarverfastmapfontawesomeforeachfsgclusgenericsggdistggforceggiraphggnewscaleggplot2ggraphggrepelgluegraphlayoutsgridExtragtablehighrhmshtmltoolshtmlwidgetsigraphisobanditeratorsitertoolsjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmemoisemgcvmimemissForestmunsellnlmenumDerivpatchworkpermutepillarpkgconfigpolyclipposteriorprettyunitsprogresspurrrqapquadprogR6randomForestrappdirsRColorBrewerRcppRcppArmadilloRcppEigenreadrregistryrJavarlangrmarkdownrngtoolsrrapplysassscalesseriationstringistringrsurvivalsvUnitsystemfontstensorAtibbletidybayestidygraphtidyrtidyselecttidytreatmenttinytexTSPtweenrtzdbutf8uuidvctrsveganviridisviridisLitevroomwithrxfunyaml
Citation
To cite package ‘bartMan’ in publications use:
Inglis A, Parnell A, Hurley C (2024). bartMan: Create Visualisations for BART Models. R package version 0.1.1, https://CRAN.R-project.org/package=bartMan.
Corresponding BibTeX entry:
@Manual{,
title = {bartMan: Create Visualisations for BART Models},
author = {Alan Inglis and Andrew Parnell and Catherine Hurley},
year = {2024},
note = {R package version 0.1.1},
url = {https://CRAN.R-project.org/package=bartMan},
}
Readme and manuals
bartMan
For more detailed information and a comprehensive
discussion, please refer to our paper associated with this document,
available here:
https://doi.org/10.52933/jdssv.v4i1.79
bartMan is an R-package for investigating and visualising Bayesian Additive Regression Tree (BART) model fits. We construct conventional plots to analyze a model’s performance and stability as well as create new tree-based plots to analyze variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using color scale to represent posterior uncertainty. Our visualizations are designed to work with the most popular BART R packages available, namely BART, dbarts, and bartMachine. A practical example of the package in use can be found in our detailed vignette.
Installation
You can install the released version of bartMan from CRAN with:
install.packages("bartMan")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("AlanInglis/bartMan")
You can then load the package with:
library(bartMan)