NEWS.md
In grmtree: Recursive Partitioning for Graded Response Models

grmtree 0.3.0

grmforest() gains tree_fun and tree_args arguments so one forest engine serves both trees. The default (tree_fun = grmtree) grows a cross-sectional GRM forest exactly as before; tree_fun = longitudinal_grmtree with tree_args = list(n_items = ...) grows a longitudinal GRM forest for response-shift detection. varimp() detects the tree type and scores out-of-bag respondents with the appropriate marginal likelihood, the two-occasion correlated-factor likelihood for longitudinal forests, so the same varimp() call ranks covariates for either forest.
longitudinal_grmtree() now freely estimates the follow-up latent variance (previously fixed to 1), so the two-factor model captures change in trait dispersion across occasions as well as in the mean. The variance remains a structural parameter held out of the split test.

grmtree 0.2.2 (2026)

grmforest() now runs reliably in parallel (n_cores > 1) on all platforms, including PSOCK/Windows workers, and results are identical regardless of the number of cores. Out-of-bag membership is stored as row indices, and forests can be grown in chunks and combined with c().
grmforest() coerces character partitioning variables to factors, avoiding tree failures on such covariates.
varimp() now scores out-of-bag respondents with the marginal (latent- integrated) log-likelihood using the correct GRM parameterization, and averages paired within-tree permutation differences.
grmfit() no longer computes standard errors during fitting, which removes a chol() failure on hard resamples and speeds up tree/forest construction; results are unchanged.
Restricted the split test in longitudinal_grmtree() to the ITEM parameters (measurement invariance) and not include the structural parameters.
Corrected item-level testing in rs_characterize(). Item-level tests now run only in nodes with omnibus response shift detected after the across-node adjustment (RS_detected), instead of the unadjusted p-value, so no item rows are produced for nodes that are not significant family-wise. The shift type is now read from the estimated item-parameter changes (occasion-general discrimination and thresholds) rather than a magnitude cut-point: a significant item is always classified as recalibration, reprioritization, or both, the "Significant (small effect)" label is removed, and the effect-size magnitude is reported separately in a new RS_magnitude column.
Corrected the response-shift visualizations plot_rs_tree() and plot_rs_heatmap(). Both now display p-values in fixed notation (e.g. "p < 0.001") rather than scientific notation, label the latent parameters as "Mean shift (T2)" and "Cor(T1, T2)", spell out the response-shift types in full, and restrict the RS-type legend to Recalibration, Reprioritization, Both, and None (the effect-size annotation is no longer shown as a shift type).

varimp() scores out-of-bag respondents with the marginal (latent-integrated) log-likelihood and averages paired within-tree permutation differences; out-of-bag membership is stored as row indices and forests can be combined with c().

grmtree 0.2.0 (2026)

Added longitudinal_grmtree() for response shift (RS) detection in patient-reported outcome measures (PROMs) measured at two time points. The method embeds a constrained two-factor longitudinal graded response model within model-based recursive partitioning to identify patient subgroups whose longitudinal measurement model differs.
Added rs_characterize(), with a print() method, for Phase 2 response shift characterization. Within each terminal node it performs an omnibus likelihood ratio test (constrained vs unconstrained model) and, where significant, item-level tests that classify each item as recalibration, reprioritization, or both. Supports hierarchical p-value correction both across nodes (global_p_adjust) and within nodes (p_adjust).
Added prepare_longitudinal_data() to construct the wide-format response matrix required by longitudinal_grmtree() from separate baseline and follow-up item columns.
Added longitudinal parameter-extraction functions: threshpar_longitudinal_grmtree(), discrpar_longitudinal_grmtree(), itempar_longitudinal_grmtree(), fscores_longitudinal_grmtree(), and latentpar_longitudinal_grmtree().
Added a plot() method for longitudinal_grmtree objects (threshold region plots showing the unique items), and two response shift visualizations, plot_rs_tree() and plot_rs_heatmap().
generate_node_scores_dataset() now supports both cross-sectional (grmtree) and longitudinal (longitudinal_grmtree) trees, and merges node assignments and factor scores back onto the original data frame.
Added the synthetic grmtree_long_data dataset (longitudinal MOS-SS emotional domain, two time points) for examples, tests, and the new vignette.
Added the vignette "Response Shift Detection with the Longitudinal GRMTree".

Corrected the post-hoc multiple-comparison adjustment methods in grmtree.control() (Holm, Benjamini-Hochberg, Benjamini-Yekutieli, Hochberg, and Hommel). The previous implementation reduced each node to its minimum p-value before applying the adjustment, which collapsed the within-node multiplicity across covariates. The internal .adjust_and_prune_tree() now collects all covariate-by-node p-values, applies the adjustment globally, and then prunes non-significant nodes. This properly accounts for both within-node (multiple covariates) and across-node (multiple splits) multiplicity.

grmtree 0.1.0 (2025)

This is the first official release of the grmtree package, providing methods for fitting and analyzing graded response model (GRM) trees and forests.

Introduced grmtree() for fitting tree-based graded response models.
Added grmforest() for building forests of GRM trees.
Implemented custom print() and plot() methods for GRM tree/forest objects.
Enabled node-level GRM parameter extraction (thresholds, loadings, discrimination).
Added support for extracting and analyzing factor scores per terminal node.
Included helper functions for working with terminal nodes and fitted models.
Vignette walkthrough provided using a sample data.