Package: EBcoBART 1.1.2

Jeroen M. Goedhart

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:10.1002/sim.70004> 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_1.1.2.tar.gz
EBcoBART_1.1.2.zip(r-4.7-any)EBcoBART_1.1.2.zip(r-4.6-any)EBcoBART_1.1.2.zip(r-4.5-any)
EBcoBART_1.1.2.tgz(r-4.6-any)EBcoBART_1.1.2.tgz(r-4.5-any)
EBcoBART_1.1.2.tar.gz(r-4.7-any)EBcoBART_1.1.2.tar.gz(r-4.6-any)
EBcoBART_1.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
EBcoBART/json (API)

# 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:

On CRAN:

Conda:

2.70 score 1 scripts 371 downloads 2 exports 59 dependencies

Last updated from:c0c9f77817. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK112
source / vignettesOK236
linux-releaseOK115
macos-releaseOK120
macos-oldrelOK112
windows-develOK78
windows-releaseOK68
windows-oldrelOK77
wasm-releaseOK116

Exports:Dat_EBcoBARTEBcoBART

Dependencies:abindactuarassertthatbackportsbbmlebdsmatrixcheckmateclicvardbartsdistributionalexpintextraDistrfastICAfBasicsfGarchgbutilsgenericsgluegssGUILDSintervalslatticelifecyclelogitnormloomagrittrMASSMatrixmatrixStatsmvtnormnakagaminloptrnumDerivpillarpkgconfigpoilogposteriorpoweRlawpracmarbibutilsRcppRcppArmadilloRcppParallelRdpackRfastrlangsadsspatialstabledisttensorAtibbletimeDatetimeSeriesunivariateMLutf8vctrsVGAMzigg