Estimation of Prevalence Ratios via Logistic Regression Models R package prLogistic version 2.0.2 Estimates adjusted prevalence ratios (PR) and their confidence intervals from logistic regression models, addressing the well-known limitation of odds ratios (OR) as approximations to PR in cross-sectional studies with common outcomes. Supports independent observations (glm()), clustered/multilevel data (glmer() from 'lme4'), longitudinal data via Generalised Estimating Equations (geeglm() from 'geepack'), and complex survey designs (svyglm() from 'survey'). Inference is available via the delta method (conditional and marginal standardisation) and via bootstrap (normal-approximation and percentile intervals). Continuous covariates are handled through user-specified or median-based reference values; flexible baseline specification allows any reference category to be chosen for factor predictors. Based on the methodology described in Amorim & Ospina (2021) <doi:10.1590/0001-3765202120190316>. Software Depends: R (>= 4.1.0) Imports: boot, lme4, stats, graphics Suggests: geepack, survey, MASS, testthat (>= 3.0.0), knitr, rmarkdown, ggplot2, dplyr Raydonal Ospina <raydonal@de.ufpe.br> Comprehensive R Archive Network (CRAN) Raydonal Ospina [aut, cre] (ORCID: <https://orcid.org/0000-0002-9884-9090>), Leila D. Amorim [aut] (ORCID: <https://orcid.org/0000-0002-1112-2332>) GPL (>= 2) 2026-06-19 application/tgz https://CRAN.R-project.org/package=prLogistic doi:10.32614/CRAN.package.prLogistic