BartMixVs: Variable Selection Using Bayesian Additive Regression Trees
Bayesian additive regression trees (BART) provides flexible non-parametric modeling of mixed-type predictors for continuous and binary responses. This package is built upon CRAN R package 'BART', version 2.7 (<https://github.com/cran/BART>). It implements the three proposed variable selection approaches in the paper: Luo, C and Daniels, M. J. (2021), "Variable Selection Using Bayesian Additive Regression Trees." <arXiv:2112.13998>, and other three existing BART-based variable selection approaches.
| Version: |
1.0.0 |
| Depends: |
R (≥ 2.10), nlme, nnet |
| Imports: |
Rcpp (≥ 1.0.6), loo, mvtnorm |
| LinkingTo: |
Rcpp |
| Published: |
2022-05-05 |
| Author: |
Chuji Luo [aut, cre],
Michael J. Daniels [aut],
Robert McCulloch [ctb],
Rodney Sparapani [ctb] |
| Maintainer: |
Chuji Luo <cjluo at ufl.edu> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| Materials: |
README |
| CRAN checks: |
BartMixVs results |
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