bartcs: Bayesian Additive Regression Trees for Confounder Selection

Fit Bayesian Regression Additive Trees (BART) models to select relevant confounders among a large set of potential confounders and to estimate average treatment effect. For more information, see Kim et al. (2022) <doi:10.48550/arXiv.2203.11798>.

Version: 0.1.2
Depends: R (≥ 2.10)
Imports: ggcharts, ggplot2, invgamma, MCMCpack, Rcpp, rlang, rootSolve, stats
LinkingTo: Rcpp
Suggests: knitr, microbenchmark, rmarkdown
Published: 2022-08-06
Author: Yeonghoon Yoo [aut, cre]
Maintainer: Yeonghoon Yoo <yooyh.stat at gmail.com>
BugReports: https://github.com/yooyh/bartcs/issues
License: GPL (≥ 3)
URL: https://github.com/yooyh/bartcs
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: bartcs results

Documentation:

Reference manual: bartcs.pdf
Vignettes: Introduction to bartcs

Downloads:

Package source: bartcs_0.1.2.tar.gz
Windows binaries: r-devel: bartcs_0.1.2.zip, r-release: bartcs_0.1.2.zip, r-oldrel: bartcs_0.1.2.zip
macOS binaries: r-release (arm64): bartcs_0.1.2.tgz, r-oldrel (arm64): not available, r-release (x86_64): bartcs_0.1.2.tgz, r-oldrel (x86_64): bartcs_0.1.2.tgz
Old sources: bartcs archive

Linking:

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