Tools for Choice Model Estimation and Application

The Choice Modelling Centre (CMC) at the University of Leeds has developed flexible code for the estimation and application of choice models in R. Users are able to write their own model functions or use a mix of already available ones. Random heterogeneity, both continuous and discrete and at the level of individuals and choices, can be incorporated for all models. There is support for both standalone models and hybrid model structures. Both classical and Bayesian estimation is available, and multiple discrete continuous models are covered in addition to discrete choice. Multi-threading processing is supported for estimation and a large number of pre and post-estimation routines, including for computing posterior (individual-level) distributions are available. For examples, a manual, and a support forum, visit <>. For more information on choice models see Train, K. (2009) and Hess, S. & Daly, A.J. (2014) for an overview of the field.


Reference manual

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0.2.6 by David Palma, 3 months ago

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Authors: Stephane Hess [aut] , David Palma [aut, cre] , Thomas Hancock [ctb]

Documentation:   PDF Manual  

Task views: Econometrics

GPL-2 license

Imports Rcpp, maxLik, mnormt, mvtnorm, graphics, randtoolbox, numDeriv, parallel, Deriv, matrixStats, RSGHB, coda, tibble

Depends on stats, utils

Suggests knitr, rmarkdown, testthat

Linking to Rcpp, RcppArmadillo, RcppEigen

Suggested by support.BWS.

See at CRAN