CIMTx swMATH ID: 40350 Software Authors: Liangyuan Hu, Chenyang Gu, Michael Lopez, Jiayi Ji Description: R package CIMTx: Causal Inference for Multiple Treatments with a Binary Outcome. Different methods to conduct causal inference for multiple treatments with a binary outcome, including regression adjustment, vector matching, Bayesian additive regression trees, targeted maximum likelihood and inverse probability of treatment weighting using different generalized propensity score models such as multinomial logistic regression, generalized boosted models and super learner. For more details, see the paper by Hu et al. <doi:10.1177/0962280220921909>. Homepage: https://cran.r-project.org/web/packages/CIMTx/index.html Source Code: https://github.com/cran/CIMTx Dependencies: R Keywords: arXiv_stat.ME; arXiv_stat.AP; arXiv_stat.CO; R; R package; Comparative effectiveness research; Bayesian machine learning; sensitivity analysis; unmeasured confounding; common support Related Software: BART; tmle; SuperLearner; drtmle; EValue; episensr; optmatch; Matching; MatchIt; sbw; optweight; PSW; causalweight; CBPS; ATE; WeightIt; twang; PSweight; R Cited in: 0 Publications Standard Articles 1 Publication describing the Software Year CIMTx: An R package for causal inference with multiple treatments using observational data Liangyuan Hu, Jiayi Ji 2021