gtregression: Tools for Creating Publication-Ready Regression Tables

Simplifies regression modelling in R by integrating modelling, diagnostics, visualisation, and summary tools into a cohesive, user-friendly interface. Designed to be accessible for researchers, particularly those in Low- and Middle-Income Countries (LMIC). Supports logistic, linear, count, matched case-control, ordinal, multinomial, multilevel, Cox, parametric survival, and competing-risk regression, and absolute risk differences with the number needed to treat. Built upon widely accepted statistical methods, including logistic regression (Hosmer et al. 2013, ISBN:9781118548429), log-binomial regression (Spiegelman and Hertzmark 2005 <doi:10.1093/aje/kwi188>), Firth penalized logistic regression (Firth 1993 <doi:10.1093/biomet/80.1.27>), Poisson and robust Poisson regression (Zou 2004 <doi:10.1093/aje/kwh090>), negative binomial regression (Hilbe 2011, ISBN:9780521179515), Cox proportional hazards regression, parametric survival regression, causal mediation analysis, risk differences (Miettinen and Nurminen 1985 <doi:10.1002/sim.4780040211>), and linear regression (Kutner et al. 2005, ISBN:9780071122214). Leverages multiple dependencies to ensure high-quality output and generate reproducible, publication-ready tables in alignment with best practices in epidemiology and applied statistics.

Version: 1.2.0
Depends: R (≥ 4.1.0)
Imports: dplyr, risks, purrr, MASS, rlang, stats, lmtest, patchwork, ggplot2, utils, sandwich, tibble, broom, broom.helpers, gt (≥ 0.6.0), officer, flextable, scales (≥ 1.3.0), forestploter, logistf, survival, cmprsk, lme4, ordinal, nnet
Suggests: haven, testthat (≥ 3.1.5), knitr, rmarkdown, car, ggtext, htmltools, jsonlite, shiny, rstudioapi, DT, readxl, rio
Published: 2026-10-20
DOI: 10.32614/CRAN.package.gtregression
Author: Rubeshkumar Polani ORCID iD [aut, cre], Salin K Eliyas ORCID iD [aut], Manikandanesan Sakthivel ORCID iD [aut], Mogan Kaviprawin ORCID iD [aut], Yuvaraj Krishnamoorthy ORCID iD [aut], Marie Gilbert Majella ORCID iD [aut]
Maintainer: Rubeshkumar Polani <rubesh at thinkdenominator.com>
BugReports: https://github.com/ThinkDenominator/gtregression/issues
License: MIT + file LICENSE
URL: https://gtregression.thinkdenominator.com/, https://github.com/ThinkDenominator/gtregression
NeedsCompilation: no
Language: en-US
Materials: README, NEWS
CRAN checks: gtregression results

Documentation:

Reference manual: gtregression.html , gtregression.pdf
Vignettes: Causal Mediation Analysis (source, R code)
Which Function Should I Use? (source)
Confounding and Interaction (source, R code)
Customize, Merge, and Export (source, R code)
Descriptive Tables (source, R code)
Diagnostics and Model Selection (source, R code)
Function Options (source, R code)
Using the gtregression app (source, R code)
Start Here: Model to Manuscript (source, R code)
Merge and Customise Tables (source, R code)
Regression Tables (source, R code)
Risk Difference and NNT (source, R code)
Special Regression Models (source, R code)
Stratified Analysis (source, R code)
Survival Analysis (source, R code)
Visualise Regression Results (source, R code)

Downloads:

Package source: gtregression_1.2.0.tar.gz
Windows binaries: r-devel: gtregression_1.1.0.zip, r-release: gtregression_1.1.0.zip, r-oldrel: gtregression_1.1.0.zip
macOS binaries: r-release (arm64): gtregression_1.1.0.tgz, r-oldrel (arm64): gtregression_1.1.0.tgz, r-release (x86_64): gtregression_1.1.0.tgz, r-oldrel (x86_64): gtregression_1.2.0.tgz
Old sources: gtregression archive

Linking:

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