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>.

Version: 1.2.0
Imports: nnet (≥ 7.3-16), BART (≥ 2.9), twang (≥ 2.5), arm (≥ 1.2-12), dplyr (≥ 1.0.7), Matching (≥ 4.9-11), magrittr (≥ 2.0.1), WeightIt (≥ 0.12.0), tmle (≥, tidyr (≥ 1.1.4), stats, ggplot2 (≥ 3.3.5), cowplot (≥ 1.1.1), mgcv (≥ 1.8-38), metR (≥ 0.11.0), stringr (≥ 1.4.0), SuperLearner (≥ 2.0-28), foreach (≥ 1.5.1), doParallel (≥ 1.0.16)
Published: 2022-06-24
DOI: 10.32614/CRAN.package.CIMTx
Author: Liangyuan Hu [aut], Chenyang Gu [aut], Michael Lopez [aut], Jiayi Ji [aut, cre]
Maintainer: Jiayi Ji <jjy2876 at>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: CIMTx results


Reference manual: CIMTx.pdf


Package source: CIMTx_1.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): CIMTx_1.2.0.tgz, r-oldrel (arm64): CIMTx_1.2.0.tgz, r-release (x86_64): CIMTx_1.2.0.tgz, r-oldrel (x86_64): CIMTx_1.2.0.tgz
Old sources: CIMTx archive


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