BartMixVs (version 1.0.0)

BartMixVs-package: Varibale Selection Using Bayesian Additive Regression Trees

Description

This package provides implementations of existing BART-based variable selection approaches.

Details

Bayesian additive regression trees (BART) provides flexible nonparametric 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 of the paper, Luo, C and Daniels, MJ (2022), "Variable Selection Using Bayesian Additive Regreesion Trees", and other three existing BART-based variable selection approaches.

References

LUO, C and DANIELS, MJ (2022). Variable Selection Using Bayesian Additive Regression Trees.

BLEICH, J., KAPELNER, A., GEORGE, E. I. and JENSEN, S. T. (2014). Variable selection for BART: an application to gene reg- ulation. Ann. Appl. Stat. 8 1750<U+2013>1781.

LINERO, A. R. (2018). Bayesian regression trees for high-dimensional prediction and variable selection. J. Amer. Statist. Assoc. 113 626<U+2013> 636.

LIU, Y., ROCKOV<U+00C1>, V. and WANG, Y. (2021). Variable selection with ABC Bayesian forests. J. R. Stat. Soc. Ser. B. Stat. Methodol. 83 453<U+2013>481.

SPARAPANI, R., SPANBAUER, C. and MCCULLOCH, R. (2021). Nonparametric machine learning and efficient computation with bayesian additive regression trees: the BART R package. J. Stat. Softw. 97 1<U+2013>66.

See Also

Optional links to other man pages

Examples

# NOT RUN {
  
# }
# NOT RUN {
     ## Optional simple examples of the most important functions
     ## These can be in \dontrun{} and \donttest{} blocks.   
  
# }