CIMTx
Causal Inference for Multiple Treatments with a Binary Outcome
v1.2.0
·
Jun 24, 2022
·
MIT + file LICENSE
Description
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>.
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r-devel-linux-x86_64-fedora-clang
dependencies in R code
Namespace in Imports field not imported from: ‘magrittr’ All declared Imports should be used.
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r-devel-linux-x86_64-fedora-gcc
dependencies in R code
Namespace in Imports field not imported from: ‘magrittr’ All declared Imports should be used.