The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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.
| Version: |
1.1.0 |
| Depends: |
R (≥ 2.10) |
| Imports: |
dbarts, loo, posterior, univariateML, extraDistr, graphics |
| Published: |
2024-09-26 |
| DOI: |
10.32614/CRAN.package.EBcoBART |
| Author: |
Jeroen M. Goedhart
 [aut, cre,
cph],
Thomas Klausch [aut],
Mark A. van de Wiel [aut],
Vincent Dorie [ctb] (Author of 'dbarts' 'R' package and auxiliary
function getDepth),
Hanarth Fonds [fnd] |
| Maintainer: |
Jeroen M. Goedhart <jeroengoed at gmail.com> |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/JeroenGoedhart/EBcoBART |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
EBcoBART results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=EBcoBART
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.