Co-Data Learning for Bayesian Additive Regression Trees R package EBcoBART version 1.1.2 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:10.1002/sim.70004> for details. Software Depends: R (>= 2.10) Imports: dbarts, loo, posterior, univariateML, extraDistr, graphics Jeroen M. Goedhart <jeroengoed@gmail.com> Comprehensive R Archive Network (CRAN) Jeroen M. Goedhart [aut, cre, cph] (ORCID: <https://orcid.org/0000-0003-0134-1897>), Thomas Klausch [aut], Mark A. van de Wiel [aut], Vincent Dorie [ctb] (Author of 'dbarts' 'R' package and auxiliary function getDepth), Hanarth Fonds [fnd] GPL (>= 3) 2025-08-20 application/tgz https://CRAN.R-project.org/package=EBcoBART doi:10.32614/CRAN.package.EBcoBART