EBcoBART: Co-Data Learning for Bayesian Additive Regression Trees

Estimate prior variable weights for Bayesian Additive Regression Trees (BART). These weights correspond to the probabilities of the variables being selected in the splitting rules of the sum-of-trees. Weights are estimated using empirical Bayes and external information on the explanatory variables (co-data). BART models are fitted using the 'dbarts' 'R' package. See Goedhart and others (2023) <doi:10.48550/arXiv.2311.09997> for details.

Version:	1.0.2
Depends:	R (≥ 2.10)
Imports:	dbarts, loo, posterior, univariateML, extraDistr
Published:	2024-09-03
DOI:	10.32614/CRAN.package.EBcoBART
Author:	Jeroen M. Goedhart [aut, cre, cph], Thomas Klausch [aut], Mark A. van de Wiel [aut], Vincent Dorie [ctb] (Author of 'dbarts' 'R' package and auxiliary function getDepth), Hanarth Fonds [fnd]
Maintainer:	Jeroen M. Goedhart <jeroengoed at gmail.com>
License:	GPL (≥ 3)
URL:	https://github.com/JeroenGoedhart/EBcoBART
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	EBcoBART results

Documentation:

Reference manual:

EBcoBART.pdf

Downloads:

Package source:	EBcoBART_1.0.2.tar.gz
Windows binaries:	r-devel: EBcoBART_1.0.2.zip, r-release: EBcoBART_1.0.2.zip, r-oldrel: EBcoBART_1.0.2.zip
macOS binaries:	r-release (arm64): EBcoBART_1.0.2.tgz, r-oldrel (arm64): EBcoBART_1.0.2.tgz, r-release (x86_64): EBcoBART_1.0.2.tgz, r-oldrel (x86_64): EBcoBART_1.0.2.tgz
Old sources:	EBcoBART archive

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