Recursive Partitioning for Graded Response Models
Internal Function: Apply Function to Models in Nodes
Extract Discrimination Parameters from GRM Tree
Compute Latent Factor Scores for Each Terminal Node in a GRM Tree
Generate Combined Dataset with Node Assignments and Factor Scores
Internal Function: Fit Graded Response Model
Control Parameters for GRM Forest
Fit a Forest of Graded Response Model Trees for Ensemble-Based DIF Det...
Control Parameters for GRM Trees
Fit a Graded Response Model Tree for Differential Item Functioning Det...
Extract Item Parameters from GRM Tree
Plot Method for GRM Tree Objects
Plot Variable Importance
Print Method for GRM Forest
Print Method for GRM Tree Objects
Extract Threshold Parameters from GRM Tree
Calculate Variable Importance for GRM Forest
Provides methods for recursive partitioning based on the 'Graded Response Model' ('GRM'), extending the 'MOB' algorithm from the 'partykit' package. The package allows for fitting 'GRM' trees that partition the population into homogeneous subgroups based on item response patterns and covariates. Includes specialized plotting functions for visualizing 'GRM' trees with different terminal node displays (threshold regions, parameter profiles, and factor score distributions). For more details on the methods, see Samejima (1969) <doi:10.1002/J.2333-8504.1968.TB00153.X>, Komboz et al. (2018) <doi:10.1177/0013164416664394> and Arimoro et al. (2025) <doi:10.1007/s11136-025-04018-6>.