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cff-version: 1.2.0
message: 'To cite package "StepReg" in publications use:'
type: software
license: MIT
title: 'StepReg: Stepwise Regression Analysis'
version: 1.5.5
doi: 10.32614/CRAN.package.StepReg
abstract: The stepwise regression analysis is a statistical technique used to identify
  a subset of predictor variables essential for constructing predictive models. This
  package performs stepwise regression analysis across various regression models such
  as linear, logistic, Cox proportional hazards, Poisson, Gamma, and negative binomial
  regression. It incorporates diverse stepwise regression algorithms like forward
  selection, backward elimination, and bidirectional elimination alongside the best
  subset method. Additionally, it offers a wide range of selection criteria, including
  Akaike Information Criterion (AIC), Sawa Bayesian Information Criterion (BIC), and
  Significance Levels (SL). We validated the output accuracy of StepReg using public
  datasets within the SAS software environment. To facilitate efficient model comparison
  and selection, StepReg allows for multiple strategies and selection metrics to be
  executed in a single function call. Moreover, StepReg integrates a Shiny application
  for interactive regression analysis, broadening its accessibility.
authors:
- family-names: Li
  given-names: Junhui
  email: junhui.li11@umassmed.edu
  orcid: https://orcid.org/0000-0003-3973-1700
- family-names: Li
  given-names: Junhui
  email: junhui.li11@umassmed.edu
- family-names: Hu
  given-names: Kai
  email: kai.hu@umassmed.edu
- family-names: Lu
  given-names: Xiaohuan
- family-names: Liu
  given-names: Wenxin
- family-names: Julie Zhu
  given-names: Lihua
repository: https://CRAN.R-project.org/package=StepReg
repository-code: https://github.com/JunhuiLi1017/StepReg
url: https://github.com/JunhuiLi1017/StepReg
date-released: '2024-10-29'
contact:
- family-names: Li
  given-names: Junhui
  email: junhui.li11@umassmed.edu
  orcid: https://orcid.org/0000-0003-3973-1700