{ "_id": "6a11e31dacfb0bcc41d02a34", "Package": "StepRegShiny", "Version": "1.6.1", "Title": "Graphical User Interface for 'StepReg'", "Authors@R": "c(\nperson(\"Junhui\", \"Li\", , \"junhui.li11@umassmed.edu\", role = c(\"cre\"),\ncomment = c(ORCID = \"0000-0003-3973-1700\")),\nperson(\"Junhui\", \"Li\", , \"junhui.li11@umassmed.edu\", role = c(\"aut\")),\nperson(\"Kai\", \"Hu\", , \"kai.hu@umassmed.edu\", role = c(\"aut\")),\nperson(\"Xiaohuan\", \"Lu\", , role = c(\"aut\")),\nperson(\"Sushmita\", \"N Nayak\", , role = c(\"ctb\")),\nperson(\"Cesar\", \"Bautista Sotelo\", , role = c(\"ctb\")),\nperson(\"Michael\", \"A Lodato\", , role = c(\"ctb\")),\nperson(\"Wenxin\", \"Liu\", , role = c(\"aut\")),\nperson(\"Lihua\", \"Julie Zhu\", , role = c(\"aut\"))\n)", "Date": "2025-10-22", "Description": "A web-based 'shiny' interface for the 'StepReg' package\nenables stepwise regression analysis across linear, generalized\nlinear (including logistic, Poisson, Gamma, and negative\nbinomial), and Cox models. It supports forward, backward,\nbidirectional, and best-subset selection under a range of\ncriteria. The package also supports stepwise regression to\nmultivariate settings, allowing multiple dependent variables to\nbe modeled simultaneously. Users can explore and combine\nmultiple selection strategies and criteria to optimize model\nselection. For enhanced robustness, the package offers optional\nrandomized forward selection to reduce overfitting, and a\ndata-splitting workflow for more reliable post-selection\ninference. 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