Package: riAFTBART 0.3.3

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

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>.

Authors:Liangyuan Hu [aut], Jiayi Ji [aut], Fengrui Zhang [cre]

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9 exports 0.09 score 75 dependencies 19 scripts 891 downloads

Last updated 4 months agofrom:d7493e0d6f. Checks:OK: 7. Indexed: yes.

Exports:cal_PEHEcal_surv_probdat_simintreeplot_gpsriAFTBARTriAFTBART_fitsavar_select

Dependencies:BARTclicodacodetoolscolorspacecowplotcpp11data.tabledbartsDBIdeldirdoParalleldplyrexpmfansifarverforeachgbmgenericsggplot2gluegtableinterpisobanditeratorsjpegjsonlitelabelinglatticelatticeExtralifecyclemagrittrMASSMatrixMatrixModelsmcmcMCMCpackmgcvminqamitoolsmsmmunsellmvtnormnlmennetnumDerivpillarpkgconfigpngpurrrquantregR6randomForestRColorBrewerRcppRcppArmadilloRcppEigenrlangRRFscalesSparseMstringistringrsurveysurvivaltibbletidyrtidyselecttwangutf8vctrsviridisLitewithrxgboostxtable