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  "Title": "Conditional Inference Trees with Stacked Multiple Imputation",
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  "Authors@R": "person(\"Phillip\", \"Sherlock\",\nemail = \"phillip.sherlock@ufl.edu\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-0433-3681\"))",
  "Description": "Implements the stacked-imputation workflow for conditional\ninference trees ('ctree') described in Sherlock et al. (2026)\n<doi:10.1080/00273171.2026.2661244>. When data contain missing\nvalues, multiply imputed datasets (e.g., from 'mice') are\nstacked vertically and a single 'ctree' is fit on the combined\ndata. To correct for the artificially inflated sample size\nintroduced by stacking, the pruning significance threshold is\ndivided by the number of imputations M (the Stack/M\ncorrection), producing a conservative but interpretable single\ntree that incorporates imputation uncertainty without requiring\npooling of structurally different trees. Also exports\nstack_imputations() and rescale_alpha() as standalone\nutilities. The underlying 'ctree' algorithm is provided by\n'partykit' (Hothorn & Zeileis, 2015; Hothorn, Hornik & Zeileis,\n2006 <doi:10.1198/106186006X133933>).",
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  "URL": "https://github.com/Phillip-Sherlock/ctreeMI",
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  "Date/Publication": "2026-05-31 20:36:53 UTC",
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      "title": "Conditional Inference Trees with Stacked Multiple Imputation",
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        "ctreeMI"
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