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ctreeMI  

Conditional Inference Trees with Stacked Multiple Imputation
View on CRAN: Click here


Download and install ctreeMI package within the R console
Install from CRAN:
install.packages("ctreeMI")

Install from Github:
library("remotes")
install_github("cran/ctreeMI")

Install by package version:
library("remotes")
install_version("ctreeMI", "0.1.0")



Attach the package and use:
library("ctreeMI")
Maintained by
Phillip Sherlock
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-07-10
Latest Update: 2026-07-10
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, 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>).
How to cite:
Phillip Sherlock (2026). ctreeMI: Conditional Inference Trees with Stacked Multiple Imputation. R package version 0.1.0, https://cran.r-project.org/web/packages/ctreeMI. Accessed 29 Sep. 2026.
Previous versions and publish date:
(2026-09-18 21:32), 0.1.0 (2026-07-10 21:00), 0.2.0 (2026-07-23 19:50), 0.3.0 (2026-07-25 23:50), 1.0.0 (2026-07-27 15:30), 1.0.1 (2026-08-26 15:10)
Other packages that cited ctreeMI R package
View ctreeMI citation profile
Other R packages that ctreeMI depends, imports, suggests or enhances
Complete documentation for ctreeMI
Functions, R codes and Examples using the ctreeMI R package
Full ctreeMI package functions and examples
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