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): bartcs_0.1.2.tgz, 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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