  CIMTx provides efficient and unified functions to implement modern methods
for causal inferences with multiple treatments using observational data with a
focus on binary outcomes. The methods include regression adjustment, inverse
probability of treatment weighting, Bayesian additive regression trees,
regression adjustment with multivariate spline of the generalized propensity
score, vector matching and targeted maximum likelihood estimation. In addition,
CIMTx illustrates ways in which users can simulate data adhering to the complex
data structures in the multiple treatment setting. Furthermore, the CIMTx
package offers a unique set of features to address the key causal assumptions:
positivity and ignorability. For the positivity assumption, CIMTx demonstrates
techniques to identify the common support region for retaining inferential
units using inverse probability of treatment weighting, Bayesian additive
regression trees and vector matching}. To handle the ignorability assumption,
CIMTx provides a flexible Monte Carlo sensitivity analysis approach to evaluate
how causal conclusions would be altered in response to different magnitude of
departure from ignorable treatment assignment.
