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: 1.1.0
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 (≥ 1.5.0.2), 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: 2021-11-29
Author: Liangyuan Hu [aut], Chenyang Gu [aut], Michael Lopez [aut], Jiayi Ji [aut, cre]
Maintainer: Jiayi Ji <Jiayi.Ji at mountsinai.org>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: CIMTx results

Documentation:

Reference manual: CIMTx.pdf

Downloads:

Package source: CIMTx_1.1.0.tar.gz
Windows binaries: r-devel: CIMTx_1.0.0.zip, r-release: CIMTx_1.0.0.zip, r-oldrel: CIMTx_1.0.0.zip
macOS binaries: r-release (arm64): CIMTx_1.0.0.tgz, r-release (x86_64): CIMTx_1.0.0.tgz, r-oldrel: CIMTx_1.0.0.tgz
Old sources: CIMTx archive

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