package for discrete choice (GEV) models It is designed for the estimation of discrete choice models. It allows the estimation of the parameters of the following models:Logit, Binary probit, Nested logit, Cross-nested logit, Multivariate Extreme Value models, Discrete and continuous mixtures of Multivariate Extreme Value models, Models with nonlinear utility functions, Models designed for panel data, Heteroscedastic models (Source:

References in zbMATH (referenced in 10 articles )

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  1. Jalali, Hamed; Carmen, Raïsa; van Nieuwenhuyse, Inneke; Boute, Robert: Quality and pricing decisions in production/inventory systems (2019)
  2. de Grange, Louis; González, Felipe; Vargas, Ignacio; Troncoso, Rodrigo: A logit model with endogenous explanatory variables and network externalities (2015)
  3. Baur, Alexander; Klein, Robert; Steinhardt, Claudius: Model-based decision support for optimal brochure pricing: applying advanced analytics in the tour operating industry (2014)
  4. Hurtubia, Ricardo; Bierlaire, Michel: Estimation of bid functions for location choice and price modeling with a latent variable approach (2014)
  5. Martinetti, Davide; Lucadamo, Antonio; Montes, Susana: How to introduce fuzzy rationality measures and fuzzy revealed preferences into a discrete choice model (2013)
  6. Chorus, Caspar G.: Random regret-based discrete choice modeling. A tutorial. (2012)
  7. Rusmevichientong, Paat; Shen, Zuo-Jun Max; Shmoys, David B.: Dynamic assortment optimization with a multinomial logit choice model and capacity constraint (2010)
  8. Kristoffersson, Ida; Engelson, Leonid: A dynamic transportation model for the Stockholm area: Implementation issues regarding departure time choice and OD-pair reduction (2009)
  9. Natarajan, Karthik; Song, Miao; Teo, Chung-Piaw: Persistency model and its applications in choice modeling (2009)
  10. Bierlaire, Michel: A theoretical analysis of the cross-nested logit model (2006)