Learn R Programming
bartcs (version 1.3.0)

bartcs-package: bartcs: Bayesian Additive Regression Trees for Confounder Selection

Description

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) tools:::Rd_expr_doi("10.1111/biom.13833").

Arguments

Author

Maintainer: Yeonghoon Yoo yooyh.stat@gmail.com

Details

Functions in bartcs serve one of three purposes.

  1. Functions for fitting: separate_bart() and single_bart().

  2. Functions for summary: summary() and plot().

  3. Utility function for OpenMP: count_omp_thread().

The code of BART model are based on the 'BART' package by Sparapani et al. (2021) under the GPL license, with modifications. The modifications from the BART package include (but are not limited to):

  • Add CHANGE step.

  • Add Single and Separate Model.

  • Add causal effect estimation.

  • Add confounder selection.

References

Sparapani R, Spanbauer C, McCulloch R (2021). “Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package.” Journal of Statistical Software, 97(1), 1–66. tools:::Rd_expr_doi("10.18637/jss.v097.i01")

Kim, C., Tec, M., & Zigler, C. M. (2023). Bayesian Nonparametric Adjustment of Confounding, Biometrics tools:::Rd_expr_doi("10.1111/biom.13833")

See Also

Useful links: