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.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).prepare_longitudinal_data() to construct the
wide-format response matrix required by
longitudinal_grmtree() from separate baseline and follow-up
item columns.threshpar_longitudinal_grmtree(),
discrpar_longitudinal_grmtree(),
itempar_longitudinal_grmtree(),
fscores_longitudinal_grmtree(), and
latentpar_longitudinal_grmtree().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.grmtree_long_data dataset
(longitudinal MOS-SS emotional domain, two time points) for examples,
tests, and the new vignette.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.This is the first official release of the grmtree
package, providing methods for fitting and analyzing graded response
model (GRM) trees and forests.
grmtree() for fitting tree-based graded
response models.grmforest() for building forests of GRM
trees.print() and plot()
methods for GRM tree/forest objects.