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  "Date": "2024-03-17",
  "Description": "The stepwise regression analysis is a statistical\ntechnique used to identify a subset of predictor variables\nessential for constructing predictive models. This package\nperforms stepwise regression analysis across various regression\nmodels such as linear, logistic, Cox proportional hazards,\nPoisson, and gamma regression. It incorporates diverse stepwise\nregression algorithms like forward selection, backward\nelimination, and bidirectional elimination alongside the best\nsubset method. Additionally, it offers a wide range of\nselection criteria, including Akaike Information Criterion\n(AIC), corrected AIC (AICc), Sawa Bayesian Information\nCriterion (BIC), Schwarz Bayesian Information Criterion (SBC),\nSignificant Levels (SL), among others. Moreover, it facilitates\nthe concurrent selection of multiple methods and criteria for\nvariable selection. For user-friendly exploration and analysis,\nStepReg provides an intuitive R Shiny app.",
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