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.

c(person(given = "Peter", family = "Higgins",
email="higgi13425@yahoo.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-7111-0077")))
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.
Peter Higgins [aut, cre] (<https://orcid.org/0000-0002-7111-0077>)

Medical Risk and Comorbidity Tools for ICD-9-CM Data
c(person("Patrick", "McCormick", role = c("aut", "cre"),
email = "patrick.mccormick@alum.mit.edu"),
person("Thomas", "Joseph", role = "aut",
email = "thomas.joseph@mountsinai.org"))
Generates risk estimates and comorbidity flags from ICD-9-CM
codes available in administrative medical datasets. The package supports
the Charlson Comorbidity Index, the Elixhauser Comorbidity
classification, the Revised Cardiac Risk Index, and the Risk Stratification
Index.  Methods are table-based, fast, and use the plyr package, so
parallelization is possible for large jobs. Also includes a sample of
real ICD-9 data for 100 patients from a publicly available dataset.
Patrick McCormick [aut, cre],
Thomas Joseph [aut]