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EBcoBART
View on CRAN: Click
here
Download and install EBcoBART package within the R console
Install from CRAN:
install.packages("EBcoBART")
Install from Github:
library("remotes")
install_github("cran/EBcoBART")
Install by package version:
library("remotes")
install_version("EBcoBART", "1.1.1")
Attach the package and use:
library("EBcoBART")
Maintained by
Jeroen M. Goedhart
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.EBcoBART . https://github.com/JeroenGoedhart/EBcoBART . EBcoBART results . EBcoBART.pdf . EBcoBART_1.1.1.tar.gz . EBcoBART_1.1.1.zip . EBcoBART_1.1.1.zip . EBcoBART_1.1.1.zip . EBcoBART_1.1.1.tgz . EBcoBART_1.1.1.tgz . EBcoBART_1.1.1.tgz . EBcoBART_1.1.1.tgz . EBcoBART_1.1.1.tgz . EBcoBART_1.1.1.tgz . EBcoBART archive . https://CRAN.R-project.org/package=EBcoBART .
First Published: 2024-09-02
Latest Update: 2024-09-03
Description:
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.
How to cite:
Jeroen M. Goedhart (2024). EBcoBART: Co-Data Learning for Bayesian Additive Regression Trees. R package version 1.1.1, https://cran.r-project.org/web/packages/EBcoBART. Accessed 01 Apr. 2025.
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