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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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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: 1.5.0
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, and gamma 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), corrected AIC (AICc), Sawa Bayesian Information Criterion (BIC), Schwarz
  Bayesian Information Criterion (SBC), Significant Levels (SL), among others. Moreover,
  it facilitates the concurrent selection of multiple methods and criteria for variable
  selection. For user-friendly exploration and analysis, StepReg provides an intuitive
  R Shiny app.
authors:
- family-names: Li
  given-names: Junhui
  email: junhui.li11@umassmed.edu
  orcid: https://orcid.org/0000-0003-3973-1700
- 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://remlapmot.r-universe.dev
commit: bc83fdaa96752abbf1ec2e43968ec3e798b7707a
date-released: '2024-03-17'
contact:
- family-names: Li
  given-names: Junhui
  email: junhui.li11@umassmed.edu
  orcid: https://orcid.org/0000-0003-3973-1700