ctreeMI-package: Conditional Inference Trees with Stacked Multiple Imputation

In ctreeMI: Conditional Inference Trees with Stacked Multiple Imputation

Conditional Inference Trees with Stacked Multiple Imputation

Description

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>).

Details

The main function is ctree_stacked, which accepts a mids object from mice, a list of imputed data frames, or a plain data frame. It returns a ctreeMI object that inherits from partykit's constparty class, so all standard methods (plot, predict, nodeids, etc.) work without modification.

Two utility functions are also exported: stack_imputations (stack a list of data frames) and rescale_alpha (compute alpha / M).

Author(s)

Phillip Sherlock [aut, cre] (ORCID: <https://orcid.org/0000-0003-0433-3681>)

Maintainer: Phillip Sherlock <[e-mail]>

References

Sherlock, P., Mansolf, M., Hofheimer, J., Hockett, C. W., O'Connor, T. G., Roubinov, D., Graff, J. C., Lai, J.-S., Bush, N. R., Wright, R. J., & Chiu, Y.-H. M. (2026). Beyond linear risk: A machine learning approach to understanding perinatal depression in context. Multivariate Behavioral Research, 1–16. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00273171.2026.2661244")}

Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics, 15(3), 651–674. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/106186006X133933")}

Hothorn, T., & Zeileis, A. (2015). partykit: A modular toolkit for recursive partitioning in R. Journal of Machine Learning Research, 16, 3905–3909.

See Also

ctree_stacked, stack_imputations, rescale_alpha, ctree, mice

ctreeMI documentation built on July 26, 2026, 1:06 a.m.