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) <isbn:978-0-521-74738-7>.
maxLik, sandwich, numDeriv, randtoolbox, RSGHB, parallel,
stats, utils, coda

Tools for Choice Model Estimation and Application
c(
person("Stephane", "Hess", email="S.Hess@leeds.ac.uk", role="aut"),
person("David", "Palma", email="D.Palma@leeds.ac.uk", role=c("aut","cre")) )
Rcpp (>= 1.0.0), maxLik, mnormt, graphics, coda, sandwich,
randtoolbox, numDeriv, RSGHB, parallel
Stephane Hess [aut],
David Palma [aut, cre]

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) <isbn:978-0-521-74738-7> and Hess,
S. & Daly, A.J. (2014) <isbn:978-1-781-00314-5> for an overview
of the field.
Rcpp (>= 1.0.0), maxLik, mnormt, mvtnorm, graphics, coda,
sandwich, randtoolbox, numDeriv, RSGHB, parallel