Context-Dependent Discrete Choice Models // Implements a class of context-dependent discrete choice models, including the standard random utility maximization (RUM) model, classical and generalized random regret minimization (RRM) models, the random advantage maximization (RAM) model, as well as the Emergent Value (EV) model and the contextual random utility maximization (CRUM) model, which explicitly incorporate measures of context dependence. In addition, pairwise normalization (PN) and range normalization (RN) models are supported. Both fixed-parameter and random-parameter specifications can be estimated, the latter allowing for preference heterogeneity. All models are formulated within a logit framework under the assumption of independently and identically distributed (i.i.d.) Gumbel error terms. The implemented methods are based on Tversky and Simonson (1993) doi:10.1287/mnsc.39.10.1179, Chorus et al. (2014) doi:10.1016/j.jbusres.2014.02.010, Rooderkerk et al. (2011) doi:10.1509/jmkr.48.4.767, Guevara and Fukushi (2016) doi:10.1016/j.trb.2016.07.012, Landry and Webb (2021) doi:10.1016/j.jet.2021.105221, and Daviet and Webb (2023) doi:10.1016/j.jmp.2022.102741.