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  "Description": "Simplifies regression modelling in R by integrating\nmodelling, diagnostics, visualisation, and summarisation tools\ninto a cohesive, user-friendly interface. Designed to be\naccessible for researchers, particularly those in Low- and\nMiddle-Income Countries (LMIC). Supports logistic, linear,\ncount, matched case-control, ordinal, multinomial, multilevel,\nCox, parametric survival, and competing-risk regression. Built\nupon widely accepted statistical methods, including logistic\nregression (Hosmer et al. 2013, ISBN:9781118548429),\nlog-binomial regression (Spiegelman and Hertzmark 2005\n<doi:10.1093/aje/kwi188>), Firth penalized logistic regression\n(Firth 1993 <doi:10.1093/biomet/80.1.27>), Poisson and robust\nPoisson regression (Zou 2004 <doi:10.1093/aje/kwh090>),\nnegative binomial regression (Hilbe 2011, ISBN:9780521179515),\nCox proportional hazards regression, parametric survival\nregression, causal mediation analysis, and linear regression\n(Kutner et al. 2005, ISBN:9780071122214). Leverages multiple\ndependencies to ensure high-quality output and generate\nreproducible, publication-ready tables in alignment with best\npractices in epidemiology and applied statistics.",
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  "Date/Publication": "2026-10-17 06:39:49 UTC",
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