ctreeMI 0.3.0

Correctness fix – please refit any tree produced by 0.1.0 or 0.2.0

Versions 0.1.0 and 0.2.0 did not implement the Stack/M correction of Sherlock et al. (2026). They divided the significance threshold by M and passed alpha / M to ctree_control(). The published and validated method divides the node-level chi-square statistic by M and recomputes the p-value.

These are not equivalent. Writing q(p, df) for the chi-square quantile function, the statistic rule rejects when X > M * q(1 - alpha, df) while the threshold rule rejects when X > q(1 - alpha / M, df). They coincide only at M = 1. At df = 1 and alpha = 0.05:

Threshold rescaling therefore under-corrects severely, and trees fitted with 0.1.0-0.2.0 contain more splits than the published method supports. Refit any such tree with 0.3.0 and re-check any reported subgroups.

Changes

• ctree_stacked() now grows the tree at the nominal alpha and then applies the correction post hoc: node statistics are divided by M, p-values recomputed, the multiplicity adjustment reapplied, and failing nodes collapsed. Split selection in partykit does not depend on alpha, and the correction is strictly stricter than the nominal threshold, so this yields the same tree as growing under the corrected criterion.
• New rescale_statistic(): divides a statistic by M and recomputes its p-value.
• New prune_stackM(): applies the correction to any party object fitted on stacked data; returns the pruned tree and a per-node table of raw and rescaled statistics.
• New check_stackM_extraction(): diagnostic confirming node statistics and degrees of freedom can be recovered from the installed partykit.
• rescale_alpha() is deprecated and now warns. It never implemented the published correction.
• ctreeMI_info replaces alpha_nominal/alpha_applied with alpha, correction, node_stats, n_splits_before and n_splits_after.
• report_ctreeMI() now describes the statistic-based correction and reports how many candidate splits survived it.

ctreeMI 0.2.0

• New node_table(): returns a data frame of tree nodes with the split path defining each node, its size in the stacked data, and its effective sample size on the original scale (stacked size / M).
• New report_ctreeMI(): generates a methods paragraph describing the fitted model, populated with the actual M, sample sizes, significance thresholds, and tree size.

ctreeMI 0.1.0

• Initial CRAN release.
• ctree_stacked(): fit a conditional inference tree on stacked multiply imputed data with the Stack/M significance-threshold correction (Sherlock et al., 2026).
• stack_imputations(): stack a list of imputed data frames vertically.
• rescale_alpha(): compute the Stack/M corrected significance threshold.
• print.ctreeMI() and summary.ctreeMI() S3 methods.
• Accepts mids objects from mice, lists of data frames, or plain data frames (falls back to standard ctree with a warning).