Tools for Choice Model Estimation and Application

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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0.0.6 by David Palma, a month ago

Browse source code at

Authors: Stephane Hess [aut] , David Palma [aut, cre]

Documentation:   PDF Manual  

GPL-2 license

Imports Rcpp, maxLik, mnormt, graphics, coda, sandwich, randtoolbox, numDeriv, RSGHB, parallel

Depends on stats, utils

Suggests knitr, rmarkdown, testthat

Linking to Rcpp, RcppArmadillo, RcppEigen

See at CRAN