klue - Hybrid Machine Learning and Random-Utility Workflow for Latent Class Multinomial Logit Model Specification

Implements the three-step workflow of Frings (2026, working paper) for specifying latent class multinomial logit (LCMNL) models. The maximum-likelihood multi-start is initialised from six clusterings of respondents' revealed-preference signatures (k-means, Gaussian mixture, hierarchical clustering with Ward, complete and average linkage, and partitioning around medoids). LCMNL models are estimated across a user-specified range of class counts, and a mixed multinomial logit (MMNL) benchmark, estimated with 'apollo', is reported alongside BIC, AIC, ICL and a classification-entropy diagnostic. For large samples the clustering starts are computed on a capped respondent subsample, and all starts are screened at a loose optimiser tolerance before the best is refined. Accepts long- or wide-format discrete-choice data with optional availability columns.