We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010) <DOI:10.1214/10-STS321>, Imai, Keele and Tingley (2010) <DOI:10.1037/a0020761>, Imai, Tingley and Yamamoto (2013) <DOI:10.1111/j.1467-985X.2012.01032.x>, and Imai and Yamamoto (2013) <DOI:10.1093/pan/mps040>. In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.

Simulation-based sensitivity analysis for causal mediation studies. It numerically and graphically evaluates the sensitivity of causal mediation analysis results 
 to the presence of unmeasured pretreatment confounding. The proposed method has primary advantages over existing methods. 
 First, using an unmeasured pretreatment confounder conditional associations with the treatment, mediator, and outcome as 
 sensitivity parameters, the method enables users to intuitively assess sensitivity in reference to prior knowledge about the 
 strength of a potential unmeasured pretreatment confounder. Second, the method accurately reflects the influence of unmeasured 
 pretreatment confounding on the efficiency of estimation of the causal effects. Third, the method can be implemented in 
 different causal mediation analysis approaches, including regression-based, simulation-based, and propensity score-based 
 methods. It is applicable to both randomized experiments and observational studies.

Provides tools for working with medical coding schemas such as the
  International Classification of Diseases (ICD). Includes functions for
  comorbidity classification algorithms such as the Pediatric Complex Chronic
  Conditions (PCCC), Charlson, and Elixhauser indices.

Provides access to well-documented medical datasets for teaching.
    Featuring several from the Teaching of Statistics in the Health Sciences 
    website <https://www.causeweb.org/tshs/category/dataset/>, a few reconstructed datasets of historical significance in medical
    research, some reformatted and extended from existing R packages, 
    and some data donations.