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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.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.