Package: gtregression 1.1.0

Rubeshkumar Polani

gtregression: Tools for Creating Publication-Ready Regression Tables

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.

Authors:Rubeshkumar Polani [aut, cre], Salin K Eliyas [aut], Manikandanesan Sakthivel [aut], Yuvaraj Krishnamoorthy [aut], Marie Gilbert Majella [aut]

gtregression_1.1.0.tar.gz
gtregression_1.1.0.zip(r-4.7-any)gtregression_1.1.0.zip(r-4.6-any)gtregression_1.1.0.zip(r-4.5-any)
gtregression_1.1.0.tgz(r-4.6-any)gtregression_1.1.0.tgz(r-4.5-any)
gtregression_1.1.0.tar.gz(r-4.7-any)gtregression_1.1.0.tar.gz(r-4.6-any)
gtregression_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
gtregression/json (API)

# Install 'gtregression' in R:
install.packages('gtregression', repos = c('https://thinkdenominator.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/thinkdenominator/gtregression/issues

Pkgdown/docs site:https://thinkdenominator.github.io

Datasets:

On CRAN:

Conda:

5.51 score 330 downloads 20 exports 107 dependencies

Last updated from:66bc4e11b0. Checks:1 ERROR, 8 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR241
source / vignettesOK250
linux-release-x86_64OK252
macos-release-arm64OK151
macos-oldrel-arm64OK207
windows-devel-x86_64OK167
windows-release-x86_64OK180
windows-oldrel-x86_64OK198
wasm-releaseOK173

Exports:check_collinearitycheck_convergencedescriptive_tabledissectforest_dfforest_regidentify_confounderinteraction_modelsmerge_tablesmodify_tablemulti_regplot_regplot_reg_combinesave_docxsave_plotsave_tableselect_modelsstratified_multi_regstratified_uni_reguni_reg

Dependencies:askpassbackportsbase64encbcabootbigDbitbit64bitopsbootbroombroom.helpersbslibcachemcardsclicliprcommonmarkcpp11crayoncurldata.tabledigestdplyrevaluatefarverfastmapflextablefontawesomefontBitstreamVerafontLiberationfontquiverforcatsforestploterfsgdtoolsgenericsggplot2gluegridExtragtgtablehavenhighrhmshtmltoolshtmlwidgetsisobandjquerylibjsonlitejuicyjuiceknitrlabelinglabelledlatticelifecyclelitedownlmtestmagrittrmarkdownMASSmemoisemimeofficeropensslpatchworkpillarpkgconfigprettyunitsprogresspurrrR6raggrappdirsRColorBrewerRcppreactablereactRreadrrisksrlangrmarkdownS7sandwichsassscalesstringistringrsyssystemfontstextshapingtibbletidyrtidyselecttinytextzdbutf8uuidV8vctrsviridisLitevroomwithrxfunxml2yamlzipzoo

Confounding and Interaction
Which Function Should I Use? | Identify Confounders | Test Interaction | What To Inspect

Last update: 2026-07-22
Started: 2026-07-22

Customize, Merge, and Export
Customize Labels | Merge Tables | Save Outputs | Word Reports | What To Inspect

Last update: 2026-07-22
Started: 2026-07-22

Descriptive Tables
Column Percentages | Row Percentages | Word-Friendly Output | What To Inspect

Last update: 2026-07-22
Started: 2026-07-22

Diagnostics and Model Selection
Convergence | Collinearity | Stepwise Model Selection | What To Inspect

Last update: 2026-07-22
Started: 2026-07-22

Regression Tables
Univariable Models | Adjusted Models | Other Effect Measures | Continuous Outcomes | What To Inspect

Last update: 2026-07-22
Started: 2026-07-22

Start Here: Model to Manuscript
gtregression | What You Can Make | Install | Prepare Example Data | Five-Minute Workflow | Describe | Model | Visualise | Where To Go Next

Last update: 2026-07-22
Started: 2025-05-25

Stratified Analysis
Univariable by Stratum | Adjusted by Stratum | What To Inspect

Last update: 2026-07-22
Started: 2026-07-22

Visualise Regression Results
One Regression Plot | Compare Crude and Adjusted Effects | Publication-Style Forest Table | What To Inspect

Last update: 2026-07-22
Started: 2026-07-22

Readme and manuals

Help Manual

Help pageTopics
Access fields on gtregression objects with `$`$.gtregression
Check collinearity using VIF for fitted modelscheck_collinearity
Check Convergence for a Regression Modelcheck_convergence
Birth Weight Datadata_birthwt
Epilepsy Treatment and Seizure Countsdata_epilepsy
Student Absenteeism in Rural Schoolsdata_gt_quin
Infertility Matched Case-Control Studydata_infertility
Lung Cancer Trial Datadata_lungcancer
PimaIndians2 Diabetes Datasetdata_PimaIndiansDiabetes
Descriptive Summary Table (no gtsummary) using gt/flextabledescriptive_table
Dissect a dataset before regressiondissect
Build a compatible data frame for forest plotsforest_df
Draw a publication-ready forest plotforest_reg
Identify confounders and effect modifiersidentify_confounder
Compare Models With and Without an Interaction Terminteraction_models
Merge gtregression tables and preserve structure and notesmerge_tables
Modify Regression/Descriptive Tables (labels, headers, caption, notes)modify_table
Multivariable regressionmulti_reg
Visualize a regression model as a forest plotplot_reg
Side-by-side forest plots: univariate vs multivariableplot_reg_combine
Print gtregression objects (unified)print.gtregression
Save multiple tables and plots to a Word documentsave_docx
Save a single plotsave_plot
Save a single regression or summary tablesave_table
Stepwise Model Selection with Evaluation Metricsselect_models
Stratified multivariable regressionstratified_multi_reg
Stratified univariable regressionstratified_uni_reg
Univariate regressionuni_reg