G-Computation to Estimate Interpretable Epidemiological Effects R package riskCommunicator version 1.0.1 CRAN Task View: Epidemiology (https://CRAN.R-project.org/view=Epidemiology) 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>. Software Depends: R (>= 3.5) Imports: boot, dplyr, ggplot2, ggpubr, magrittr, MASS, methods, purrr, rlang, stats, tidyr, tidyselect Suggests: knitr, rmarkdown, testthat, tidyverse, printr, stringr, formatR, sandwich Jessica Grembi <jess.grembi@gmail.com> Comprehensive R Archive Network (CRAN) Jessica Grembi [aut, cre, cph] (ORCID: <https://orcid.org/0000-0001-6142-4913>), Elizabeth Rogawski McQuade [ctb] (ORCID: <https://orcid.org/0000-0002-4942-3747>) GPL-3 2022-05-31 application/tgz https://CRAN.R-project.org/package=riskCommunicator doi:10.32614/CRAN.package.riskCommunicator