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 Liangyuan Hu (2020) <doi:10.1177/0962280220921909>.
| Version: | 0.2.0 |
| Imports: | nnet, BART, twang, arm, dplyr, Matching, magrittr, car, WeightIt, SuperLearner, tmle, tidyr, stats, class, gam, dbarts |
| Published: | 2020-06-02 |
| 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 |
| Reference manual: | CIMTx.pdf |
| Package source: | CIMTx_0.2.0.tar.gz |
| Windows binaries: | r-devel: CIMTx_0.2.0.zip, r-release: CIMTx_0.2.0.zip, r-oldrel: CIMTx_0.2.0.zip |
| macOS binaries: | r-release: CIMTx_0.2.0.tgz, r-oldrel: CIMTx_0.2.0.tgz |
| Old sources: | CIMTx archive |
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