Tools for Estimating Discrete Choice Models

The Choice Modelling Centre at the University of Leeds has developed flexible estimation code for choice models in R. Users are able to write their own likelihood functions or use a mix of already available ones. Mixing, in the form of random coefficients and components is allowed for all models. Both classical and Bayesian estimation are available. Multi-threading processing is supported. For more information on discrete choice models see Train, K. (2009) .


Reference manual

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0.0.1 by David Palma, a month ago

Browse source code at

Authors: Stephane Hess , David Palma

Documentation:   PDF Manual  

GPL-2 license

Imports maxLik, sandwich, numDeriv, randtoolbox, RSGHB, parallel, stats, utils, coda

Suggests testthat, knitr, rmarkdown

See at CRAN