gtregression1.0.0 package

Tools for Creating Publication-Ready Regression Tables

check_collinearity

Check Collinearity Using VIF for Fitted Models

check_convergence

Check Convergence for a Regression Model

descriptive_table

Descriptive Summary Table for Study Characteristics (User-Friendly)

dissect

Dissect a Dataset Before Regression

dot-fit_multi_model

Fit multivariate Regression Model (Internal)

dot-fit_uni_model

Fit Regression Model with One or More Predictors (Internal)

dot-get_abbreviation

Get Abbreviation Explanation

dot-get_effect_label_adjusted

Get Adjusted Effect Label

dot-get_effect_label

Get Unadjusted Effect Label

dot-get_remove_abbreviation

Get Abbreviation to Remove

dot-reg_check_linear

Linear Regression Diagnostic Checks (Internal) similar to reg check in...

dot-validate_exposures

Validate Exposure Variable(s) for Regression

identify_confounder

Identify Confounders Using the Change-in-Estimate Method

interaction_models

Compare Models With and Without Interaction Term

merge_tables

Merge Multiple gtsummary Tables (Descriptive, Univariate, Multivariabl...

modify_table

Modify Regression Table Labels and Layout

multi_reg

Multivariable Regression (Adjusted Odds, Risk, or Rate Ratios)

plot_reg_combine

Visualize Univariate and Multivariate Regression Side-by-Side

plot_reg

Visualize a Regression Model as a Forest Plot

save_docx

Save Multiple Tables and Plots to a Word Document

save_plot

Save a Single Plot

save_table

Save a Single Regression Table

select_models

Stepwise Model Selection with Evaluation Metrics

stratified_multi_reg

Stratified Multivariable Regression (Adjusted OR, RR, IRR, or Beta)

stratified_uni_reg

Performs univariate regression for each exposure on a binary, count, o...

uni_reg

Univariate regression (Odds, Risk, or Rate Ratios)

Simplifies regression modeling in R by integrating multiple modeling and summarization tools into a cohesive, user-friendly interface. Designed to be accessible for researchers, particularly those in Low- and Middle-Income Countries (LMIC). 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>), Poisson and robust Poisson regression (Zou 2004 <doi:10.1093/aje/kwh090>), negative binomial regression (Hilbe 2011, ISBN:9780521179515), 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.

  • Maintainer: Rubeshkumar Polani
  • License: MIT + file LICENSE
  • Last published: 2025-08-18