A Flexible Approach for Causal Inference with Multiple Treatments and Clustered Survival Outcomes R package riAFTBART version 0.3.3 Random-intercept accelerated failure time (AFT) model utilizing Bayesian additive regression trees (BART) for drawing causal inferences about multiple treatments while accounting for the multilevel survival data structure. It also includes an interpretable sensitivity analysis approach to evaluate how the drawn causal conclusions might be altered in response to the potential magnitude of departure from the no unmeasured confounding assumption.This package implements the methods described by Hu et al. (2022) <doi:10.1002/sim.9548>. Software Imports: MCMCpack, msm, dbarts, magrittr, foreach, doParallel, dplyr, BART, stringr, tidyr, survival, cowplot, ggplot2, twang, nnet, RRF, randomForest Fengrui Zhang <fz174@sph.rutgers.edu> Comprehensive R Archive Network (CRAN) Liangyuan Hu [aut], Jiayi Ji [aut], Fengrui Zhang [cre] MIT + file LICENSE (https://CRAN.R-project.org/package=riAFTBART/LICENSE) 2024-05-29 application/tgz https://CRAN.R-project.org/package=riAFTBART doi:10.32614/CRAN.package.riAFTBART