{ "_id": "6a35727c3efcd9bda43eb9f0", "Package": "prLogistic", "Type": "Package", "Title": "Estimation of Prevalence Ratios via Logistic Regression Models", "Version": "2.0.2", "Date": "2026-06-12", "Authors@R": "c(\nperson(\"Raydonal\", \"Ospina\",\nemail = \"raydonal@de.ufpe.br\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-9884-9090\")),\nperson(\"Leila D.\", \"Amorim\",\nemail = \"leiladen@ufba.br\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0002-1112-2332\"))\n)", "Description": "Estimates adjusted prevalence ratios (PR) and their\nconfidence intervals from logistic regression models,\naddressing the well-known limitation of odds ratios (OR) as\napproximations to PR in cross-sectional studies with common\noutcomes. Supports independent observations (glm()),\nclustered/multilevel data (glmer() from 'lme4'), longitudinal\ndata via Generalised Estimating Equations (geeglm() from\n'geepack'), and complex survey designs (svyglm() from\n'survey'). Inference is available via the delta method\n(conditional and marginal standardisation) and via bootstrap\n(normal-approximation and percentile intervals). Continuous\ncovariates are handled through user-specified or median-based\nreference values; flexible baseline specification allows any\nreference category to be chosen for factor predictors. Based on\nthe methodology described in Amorim & Ospina (2021)\n.", "License": "GPL (>= 2)", "URL": "https://github.com/Raydonal/prLogistic,\nhttps://raydonal.github.io/prLogistic/", "BugReports": "https://github.com/Raydonal/prLogistic/issues", "Encoding": "UTF-8", "LazyData": "true", "LazyDataCompression": "xz", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Config/roxygen2/version": "8.0.0", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-19 16:43:33 UTC", "User": "root" }, "Author": "Raydonal Ospina [aut, cre] (ORCID:\n), Leila D. 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