risks: Estimate Risk Ratios and Risk Differences using Regression

Risk ratios and risk differences are estimated using regression models that allow for binary, categorical, and continuous exposures and confounders. Implemented are marginal standardization after fitting logistic models (g-computation) with delta-method and bootstrap standard errors, Miettinen's case-duplication approach (Schouten et al. 1993, <doi:10.1002/sim.4780121808>), log-binomial (Poisson) models with empirical variance (Zou 2004, <doi:10.1093/aje/kwh090>), binomial models with starting values from Poisson models (Spiegelman and Hertzmark 2005, <doi:10.1093/aje/kwi188>), and others.

Version: 0.4.2
Depends: R (≥ 3.5.0)
Imports: boot, bcaboot, broom, dplyr, purrr, rlang, sandwich, stats, tibble, tidyr
Suggests: addreg, covr, knitr, logbin, MASS, rmarkdown, testthat
Published: 2023-06-13
Author: Konrad Stopsack ORCID iD [aut, cre], Travis Gerke ORCID iD [aut]
Maintainer: Konrad Stopsack <stopsack at post.harvard.edu>
BugReports: https://github.com/stopsack/risks/issues
License: GPL-3
URL: https://stopsack.github.io/risks/
NeedsCompilation: no
CRAN checks: risks results

Documentation:

Reference manual: risks.pdf
Vignettes: Marginal standardization
Models and model comparisons
Introduction to the risks package: Get Started

Downloads:

Package source: risks_0.4.2.tar.gz
Windows binaries: r-devel: risks_0.4.2.zip, r-release: risks_0.4.2.zip, r-oldrel: risks_0.4.2.zip
macOS binaries: r-release (arm64): risks_0.4.2.tgz, r-oldrel (arm64): risks_0.4.2.tgz, r-release (x86_64): risks_0.4.2.tgz

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