IFTPredictor - Predictions Using Item-Focused Tree Models

This function predicts item response probabilities and item responses using the item-focused tree (IFT) model. The IFT model combines logistic regression with recursive partitioning to detect Differential Item Functioning (DIF) in dichotomous items. The model applies partitioning rules to the data, splitting it into homogeneous subgroups, and uses logistic regression within each subgroup to explain the data. DIF detection is achieved by examining potential group differences in item response patterns. This method is useful for understanding how different covariates, such as demographic or psychological factors, influence item responses across subpopulations.