bartcs: Bayesian Additive Regression Trees for Confounder Selection

Fit Bayesian Regression Additive Trees (BART) models to select true confounders from a large set of potential confounders and to estimate average treatment effect. For more information, see Kim et al. (2023) <doi:10.1111/biom.13833>.

Version:	1.2.1
Depends:	R (≥ 3.4.0)
Imports:	coda (≥ 0.4.0), ggcharts, ggplot2, invgamma, MCMCpack, Rcpp (≥ 0.11.0), rlang, rootSolve, stats
LinkingTo:	Rcpp
Suggests:	knitr, microbenchmark, rmarkdown
Published:	2024-01-24
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
Citation:	bartcs citation info
Materials:	README NEWS
In views:	Bayesian
CRAN checks:	bartcs results

Documentation:

Reference manual:	bartcs.pdf
Vignettes:	Introduction to bartcs

Downloads:

Package source:	bartcs_1.2.1.tar.gz
Windows binaries:	r-prerel: bartcs_1.2.1.zip, r-release: bartcs_1.2.1.zip, r-oldrel: bartcs_1.2.1.zip
macOS binaries:	r-prerel (arm64): bartcs_1.2.1.tgz, r-release (arm64): bartcs_1.2.1.tgz, r-oldrel (arm64): bartcs_1.2.1.tgz, r-prerel (x86_64): bartcs_1.2.1.tgz, r-release (x86_64): bartcs_1.2.1.tgz
Old sources:	bartcs archive

Linking:

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