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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: 1.2.2
Depends: R (≥ 3.4.0)
Imports: coda (≥ 0.4.0), ggcharts, ggplot2, invgamma, MCMCpack, Rcpp, rlang, rootSolve, stats
LinkingTo: Rcpp
Suggests: knitr, microbenchmark, rmarkdown
Published: 2024-05-01
DOI: 10.32614/CRAN.package.bartcs
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_1.2.2.tar.gz
Windows binaries: r-devel: bartcs_1.2.2.zip, r-release: bartcs_1.2.2.zip, r-oldrel: bartcs_1.2.2.zip
macOS binaries: r-release (arm64): bartcs_1.2.2.tgz, r-oldrel (arm64): bartcs_1.2.2.tgz, r-release (x86_64): bartcs_1.2.2.tgz, r-oldrel (x86_64): bartcs_1.2.2.tgz
Old sources: bartcs archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=bartcs to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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