Multidimensional forced choice (MFC) formats have emerged as a promising alternative to traditional single statement Likert-type measures for assessing noncognitive traits while reducing response biases. As MFC formats become more widely used, there is a growing need for tools to support MFC analysis, which motivated the development of the fcirt package. The fcirt package estimates forced choice model parameters using Bayesian methods. It currently enables estimation of the Generalized Graded Unfolding Model (GGUM; Roberts et al., 2000)-based Multi-Unidimensional Pairwise Preference (MUPP) model using rstan, which implements the Hamiltonian Monte Carlo (HMC) sampling algorithm. fcirt also includes functions for computing item and test information functions to evaluate the quality of MFC assessments, as well as functions for Bayesian diagnostic plotting to assist with model evaluation and convergence assessment.
