CRAN/E | riAFTBART

riAFTBART

A Flexible Approach for Causal Inference with Multiple Treatments and Clustered Survival Outcomes

Installation

About

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.

Key Metrics

Version 0.3.2
Published 2022-05-16 880 days ago
Needs compilation? no
License MIT
License File
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Maintainer

Maintainer

Jiayi Ji

Authors

Liangyuan Hu

aut

Jiayi Ji

aut / cre

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

riAFTBART archive

Imports

MASS
MCMCpack
msm
dbarts
magrittr
foreach
doParallel
dplyr
BART
stringr
tidyr
survival
cowplot
ggplot2
twang
nnet
RRF
randomForest