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>).