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]

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

On CRAN:

2 exports 1.00 score 45 dependencies 1 scripts

Last updated 20 days agofrom:cbceb03bbb. Checks:OK: 5 ERROR: 2. Indexed: yes.

Exports:Dat_EBcoBARTEBcoBART

Dependencies:abindactuarassertthatbackportscheckmateclicvardbartsdistributionalexpintextraDistrfansifastICAfBasicsfGarchgbutilsgenericsgluegsslatticelifecyclelogitnormloomagrittrMASSMatrixmatrixStatsnakagaminumDerivpillarpkgconfigposteriorrbibutilsRcppRdpackrlangspatialstabledisttensorAtibbletimeDatetimeSeriesunivariateMLutf8vctrs