Package: bartcs 1.2.2

Yeonghoon Yoo

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>.

Authors:Yeonghoon Yoo [aut, cre]

bartcs_1.2.2.tar.gz
bartcs_1.2.2.tar.gz(r-4.5-noble)bartcs_1.2.2.tar.gz(r-4.4-noble)
bartcs_1.2.2.tgz(r-4.4-emscripten)bartcs_1.2.2.tgz(r-4.3-emscripten)
bartcs.pdf |bartcs.html✨
bartcs/json (API)
NEWS

# Install 'bartcs' in R:

install.packages('bartcs', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/yooyh/bartcs/issues

Uses libs:

c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

ihdp - Infant Health and Development Program Data

3.00 score 3 scripts 366 downloads 3 exports 43 dependencies

Last updated 6 months agofrom:3c33038cc9. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Sep 29 2024
R-4.5-linux-x86_64	OK	Sep 29 2024

Exports:count_omp_thread separate_bart single_bart

Dependencies:cli coda colorspace dplyr fansi farver generics ggcharts ggplot2 glue gtable invgamma isoband labeling lattice lifecycle magrittr MASS Matrix MatrixModels mcmc MCMCpack mgcv munsell nlme patchwork pillar pkgconfig quantreg R6 RColorBrewer Rcpp rlang rootSolve scales SparseM survival tibble tidyselect utf8 vctrs viridisLite withr

Introduction to bartcs

Yeonghoon Yoo

Rendered frombartcs.Rmdusingknitr::rmarkdownon Sep 29 2024.

Last update: 2023-05-27
Started: 2022-07-19

Citation

Readme and manuals

Help Manual

Help page	Topics
Fit BART models to select confounders and estimate treatment effect	bart separate_bart single_bart
Count the number of OpenMP threads for parallel computation	count_omp_thread
Infant Health and Development Program Data	ihdp
Draw plot for 'bartcs' object	plot.bartcs
Summary for 'bartcs' object	summary.bartcs