Student Publications [Scholarly]

Document Type

Article

Abstract

Stepwise regression remains widely used for model selection, yet the field lacks a comprehensive, well-documented tool that supports diverse model families and selection strategies, implements multiple information criteria, and addresses overfitting and post-selection inference. We present StepReg, an R package that unifies stepwise selection across linear, generalized linear (e.g., logistic, Poisson, Gamma, negative binomial), and Cox models, supporting forward, backward, bidirectional, and best-subset search under various information criteria. StepReg also supports multivariate multiple linear stepwise regression, enabling simultaneous modeling of multiple dependent variables. Users can explore multiple strategies and information criteria within a single function call, and optionally combine them for more efficient and flexible model selection. To enhance robustness, StepReg provides an optional randomized forward selection mode to mitigate overfitting and a data-splitting workflow to improve the reliability of post-selection inference. The package further provides logging and visualization of the selection path, along with exporting results in common formats. A companion R package, StepRegShiny, has also been developed to provide a Shiny-based GUI for point-and-click analysis. Accuracy was assessed on public datasets by cross checking results against SAS. Together, these features provide a transparent, extensible framework that streamlines stepwise regression while promoting best practices. © (2026), (Technische Universitaet Wien). All Rights Reserved.

Publication Title

R Journal

Publication Date

3-2026

Volume

18

Issue

1

First Page

188

Last Page

205

ISSN

2073-4859

DOI

10.32614/RJ-2026-005

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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