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, each node-level test statistic is divided by the number of imputations M and its p-value recomputed before nodes are pruned (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(), rescale_statistic() and prune_stackM() as standalone utilities. The underlying 'ctree' algorithm is provided by 'partykit' (Hothorn & Zeileis, 2015; Hothorn, Hornik & Zeileis, 2006 <doi:10.1198/106186006X133933>).

Version:	0.3.0
Depends:	R (≥ 4.0.0)
Imports:	partykit (≥ 1.2-0), mice (≥ 3.0.0), stats, methods
Suggests:	testthat (≥ 3.0.0)
Published:	2026-07-25
DOI:	10.32614/CRAN.package.ctreeMI
Author:	Phillip Sherlock [aut, cre]
Maintainer:	Phillip Sherlock <phillip.sherlock at ufl.edu>
BugReports:	https://github.com/Phillip-Sherlock/ctreeMI/issues
License:	GPL (≥ 3)
URL:	https://github.com/Phillip-Sherlock/ctreeMI
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	ctreeMI results

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