Package: EBcoBART 1.0.1

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.

Authors:Jeroen M. Goedhart [aut, cre, cph], Thomas Klausch [aut], Mark A. van de Wiel [aut], Vincent Dorie [ctb], Hanarth Fonds [fnd]

EBcoBART_1.0.1.tar.gz
EBcoBART_1.0.1.zip(r-4.5)EBcoBART_1.0.1.zip(r-4.4)EBcoBART_1.0.1.zip(r-4.3)
EBcoBART_1.0.1.tgz(r-4.5-any)EBcoBART_1.0.1.tgz(r-4.4-any)EBcoBART_1.0.1.tgz(r-4.3-any)
EBcoBART_1.0.1.tar.gz(r-4.5-noble)EBcoBART_1.0.1.tar.gz(r-4.4-noble)
EBcoBART_1.0.1.tgz(r-4.4-emscripten)EBcoBART_1.0.1.tgz(r-4.3-emscripten)
EBcoBART.pdf |EBcoBART.html
EBcoBART/json (API)
NEWS

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

Bug tracker:https://github.com/jeroengoedhart/ebcobart/issues

Datasets:
  • dat - Exemplary Data Set

On CRAN:

Conda:

3.30 score 1 scripts 545 downloads 2 exports 60 dependencies

Last updated 7 months agofrom:cbceb03bbb. Checks:6 OK, 3 ERROR. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 17 2025
R-4.5-winOKMar 17 2025
R-4.5-macERRORMar 17 2025
R-4.5-linuxOKMar 17 2025
R-4.4-winOKMar 17 2025
R-4.4-macERRORMar 17 2025
R-4.4-linuxOKMar 17 2025
R-4.3-winOKMar 17 2025
R-4.3-macERRORMar 17 2025

Exports:Dat_EBcoBARTEBcoBART

Dependencies:abindactuarassertthatbackportsbbmlebdsmatrixcheckmateclicvardbartsdistributionalexpintextraDistrfansifastICAfBasicsfGarchgbutilsgenericsgluegssGUILDSintervalslatticelifecyclelogitnormloomagrittrMASSMatrixmatrixStatsmvtnormnakagaminloptrnumDerivpillarpkgconfigpoilogposteriorpoweRlawpracmarbibutilsRcppRcppArmadilloRcppParallelRdpackRfastrlangsadsspatialstabledisttensorAtibbletimeDatetimeSeriesunivariateMLutf8vctrsVGAMzigg