ctreeMI: Conditional Inference Trees with Stacked Multiple Imputation
Implements the stacked-imputation workflow for conditional
inference trees ('ctree') described in Sherlock et al. (2026)
<doi:10.1080/00273171.2026.2661244>. When data contain missing values,
multiply imputed datasets (e.g., from 'mice') are stacked vertically
and a single 'ctree' is fit on the combined data. To correct for the
artificially inflated sample size introduced by stacking, the pruning
significance threshold is divided by the number of imputations M
(the Stack/M correction), producing a conservative but interpretable
single tree that incorporates imputation uncertainty without requiring
pooling of structurally different trees. Also exports
stack_imputations() and rescale_alpha() as standalone utilities. The
underlying 'ctree' algorithm is provided by 'partykit'
(Hothorn & Zeileis, 2015; Hothorn, Hornik & Zeileis, 2006
<doi:10.1198/106186006X133933>).
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