Package: dbarts 0.9-34

dbarts: Discrete Bayesian Additive Regression Trees Sampler

Fits Bayesian additive regression trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/Metropolis-Hastings sampler. Also serves as a drop-in replacement for package 'BayesTree'.

Authors:Vincent Dorie [aut, cre], Hugh Chipman [aut], Robert McCulloch [aut], Armon Dadgar [ctb], R Core Team [ctb], Guido U Draheim [ctb], Maarten Bosmans [ctb], Christophe Tournayre [ctb], Michael Petch [ctb], Rafael de Lucena Valle [ctb], Steven G. Johnson [ctb], Matteo Frigo [ctb], John Zaitseff [ctb], Todd Veldhuizen [ctb], Luc Maisonobe [ctb], Scott Pakin [ctb], Daniel Richard G. [ctb]

dbarts_0.9-34.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
dbarts/json (API)

# Install 'dbarts' in R:
install.packages('dbarts', repos = c('https://vdorie.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/vdorie/dbarts/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

11.36 score 61 stars 17 packages 612 scripts 6.7k downloads 2 mentions 15 exports 0 dependencies

Last updated from:e7ebf5205a. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK186
linux-devel-x86_64OK229
source / vignettesOK202
linux-release-arm64OK214
linux-release-x86_64OK186
macos-release-arm64OK118
macos-release-x86_64OK309
macos-oldrel-arm64OK157
macos-oldrel-x86_64OK244
windows-develOK292
windows-releaseOK232
windows-oldrelOK173
wasm-releaseOK123

Exports:bartbart2dbartsdbartsControldbartsDataextractguessNumCoresmakeindmakeModelMatrixFromDataFramemakeTestModelMatrixpd2bartpdbartrbart_viupdatePredictorPerObservationJointlyxbart

Dependencies:

Working with dbarts Saved Trees
Simulation | Model Fitting | Extracting Trees | Flattened Trees | Tree Traversal | Plotting Trees | Getting Tree Predictions | Counts for New Data | Advanced Traversal | References
Rendered fromworking_with_saved_trees.Rmdusingknitr::rmarkdown

Last update: 2026-07-02
Started: 2022-01-24

Building a Gibbs Sampler with dbarts
Model | Conditonal Posteriors | Simulated Data | Implementing a Gibbs Sampler | Saveing the sampler | Visualizing the Result | Multiple Threading
Rendered fromgibbs_sampler_mixture_model.Rmdusingknitr::rmarkdown

Last update: 2020-12-21
Started: 2020-12-21

Readme and manuals

Help Manual

Help pageTopics
Bayesian Additive Regression Treesbart bart2 extract extract.bart fitted.bart plot.bart predict.bart residuals.bart
Discrete Bayesian Additive Regression Trees Samplerdbarts
Discrete Bayesian Additive Regression Trees Sampler ControldbartsControl
Discrete Bayesian Additive Regression Trees Sampler DatadbartsData
Class "dbartsSampler" of Discrete Bayesian Additive Regression Trees SamplerdbartsSampler dbartsSampler-class \S4method{copy}{dbartsSampler} \S4method{getTrees}{dbartsSampler} \S4method{plotTree}{dbartsSampler} \S4method{predict}{dbartsSampler} \S4method{printTrees}{dbartsSampler} \S4method{run}{dbartsSampler} \S4method{sampleNodeParametersFromPrior}{dbartsSampler} \S4method{sampleTreesFromPrior}{dbartsSampler} \S4method{setControl}{dbartsSampler} \S4method{setCutPoints}{dbartsSampler} \S4method{setData}{dbartsSampler} \S4method{setModel}{dbartsSampler} \S4method{setOffset}{dbartsSampler} \S4method{setPredictor}{dbartsSampler} \S4method{setResponse}{dbartsSampler} \S4method{setSigma}{dbartsSampler} \S4method{setTestOffset}{dbartsSampler} \S4method{setTestPredictorAndOffset}{dbartsSampler} \S4method{setTestPredictor}{dbartsSampler} \S4method{show}{dbartsSampler} \S4method{startThreads}{dbartsSampler} \S4method{stopThreads}{dbartsSampler}
Guess Number of CoresguessNumCores
Make Model Matrix from Data Framemakeind makeModelMatrixFromDataFrame makeTestModelMatrix
Partial Dependence Plots for BARTpd2bart pdbart plot.pd2bart plot.pdbart
Bayesian Additive Regression Trees with Random Effectsextract.rbart fitted.rbart plot.rbart predict.rbart rbart_vi residuals.rbart
Jointly Update a Shared Predictor per Observation Across SamplersupdatePredictorPerObservationJointly
Crossvalidation For Bayesian Additive Regression Treesxbart