Rcpp (>= 1.0.0), maxLik, mnormt, mvtnorm, graphics, coda,
sandwich, randtoolbox, numDeriv, RSGHB, parallel, Deriv

Rcpp (>= 1.0.0), maxLik, mnormt, mvtnorm, graphics,
randtoolbox, numDeriv, parallel, Deriv, matrixStats, RSGHB,
coda

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
<http://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.

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")),
person("Thomas", "Hancock", email="T.O.Hancock@leeds.ac.uk", role="ctb"))
Rcpp (>= 1.0.0), maxLik, mnormt, mvtnorm, graphics,
randtoolbox, numDeriv, parallel, Deriv, matrixStats, RSGHB,
coda, tibble
Stephane Hess [aut],
David Palma [aut, cre],
Thomas Hancock [ctb]

c(
person("Stephane", "Hess", email="S.Hess@leeds.ac.uk", role=c("aut","cre")),
person("David", "Palma", email="D.Palma@leeds.ac.uk", role="aut"),
person("Thomas", "Hancock", email="T.O.Hancock@leeds.ac.uk", role="ctb"))
Choice models are a widely used technique across numerous scientific disciplines. The Apollo package is a very flexible tool 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
<http://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,
randtoolbox, numDeriv, parallel, Deriv, matrixStats, RSGHB,
coda, tibble, stringr
Stephane Hess [aut, cre],
David Palma [aut],
Thomas Hancock [ctb]