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 www.ApolloChoiceModelling.com. For more information on choice models see Train, K. (2009) and Hess, S. & Daly, A.J. (2014) for an overview of the field.


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install.packages("apollo")

0.0.7 by David Palma, a month ago


Browse source code at https://github.com/cran/apollo


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


Documentation:   PDF Manual  


Task views: Econometrics


GPL-2 license


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

Depends on stats, utils

Suggests knitr, rmarkdown, testthat

Linking to Rcpp, RcppArmadillo, RcppEigen


See at CRAN