Provides a structured approach to assess the quality and trustworthiness of R packages 
    (documentation, testing, popularity, dependencies), supporting informed decisions in production 
    or research by highlighting strengths and potential risks in adoption or development.

A collection of functions related to the study of etiologic heterogeneity both across disease subtypes and across individual disease markers. The included functions allow one to quantify the extent of etiologic heterogeneity in the context of a case-control study, and provide p-values to test for etiologic heterogeneity across individual risk factors. Begg CB, Zabor EC, Bernstein JL, Bernstein L, Press MF, Seshan VE (2013) <doi:10.1002/sim.5902>.

Estimates flexible epidemiological effect measures including both differences and ratios using the parametric G-formula developed as an alternative to inverse probability weighting.  It is useful for estimating the impact of interventions in the presence of treatment-confounder-feedback. G-computation was originally described by Robbins (1986) <doi:10.1016/0270-0255(86)90088-6> and has been described in detail by Ahern, Hubbard, and Galea (2009) <doi:10.1093/aje/kwp015>; Snowden, Rose, and Mortimer (2011) <doi:10.1093/aje/kwq472>; and Westreich et al. (2012) <doi:10.1002/sim.5316>.

Calculates risk differences (or prevalence differences for 
    cross-sectional data) using generalized linear models with automatic 
    link function selection. Provides robust model fitting with fallback 
    methods, support for stratification and adjustment variables, inverse
    probability of treatment weighting (IPTW) for causal inference, and 
    publication-ready output formatting. Handles model convergence issues
    gracefully and provides confidence intervals using multiple approaches.
    Methods are based on approaches described in Mark W. Donoghoe and 
    Ian C. Marschner (2018) "logbin: An R Package for Relative Risk 
    Regression Using the Log-Binomial Model" <doi:10.18637/jss.v086.i09> 
    for robust GLM fitting, Peter C. Austin (2011) "An Introduction to 
    Propensity Score Methods for Reducing the Effects of Confounding in 
    Observational Studies" <doi:10.1080/00273171.2011.568786> for IPTW methods,
    and standard epidemiological methods for risk difference estimation as 
    described in Kenneth J. Rothman, Sander Greenland and Timothy L. Lash 
    (2008, ISBN:9780781755641) "Modern Epidemiology".