Fitting latent class mixed multinomial probit (LCMMNP) models to simulated or empirical data.
Estimation takes place in a Bayesian framework using a Gibbs sampler.
The number of latent classes can be updated within the algorithm on a weight-based strategy.
For a reference on the method see Oelschl\"ager and Bauer (2021) <https://trid.trb.org/view/1759753>.

c(
person("Lennart", "Oelschl\u00e4ger",
email = "lennart.oelschlaeger@uni-bielefeld.de",
role = c("aut", "cre")),
person("Dietmar", "Bauer",
email = "dietmar.bauer@uni-bielefeld.de",
role = "aut"),
person("Sebastian", "B\u00fcscher",
email = "sebastian.buescher@uni-bielefeld.de",
role = "ctb"),
person("Manuel", "Batram",
email = "manuel.batram@uni-bielefeld.de",
role = "ctb"))
Fitting latent class mixed multinomial probit (LCMMNP) models to simulated
or empirical choice data via Bayesian estimation. The number of latent
classes can be updated within the algorithm on a weight-based strategy.
For a reference on the method see Oelschlaeger and Bauer (2021)
<https://trid.trb.org/view/1759753>.
knitr, rmarkdown, mlogit, vdiffr, testthat (>= 3.0.0)

c(
person("Lennart", "Oelschl\u00e4ger",
email = "oelschlaeger.lennart@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-5421-9313")),
person("Dietmar", "Bauer",
email = "dietmar.bauer@uni-bielefeld.de",
role = "aut",
comment = c(ORCID = "0000-0003-2920-7032")),
person("Sebastian", "B\u00fcscher",
email = "sebastian.buescher@uni-bielefeld.de",
role = "ctb"),
person("Manuel", "Batram",
email = "manuel.batram@uni-bielefeld.de",
role = "ctb"))
Bayes estimation of probit choice models, both in the cross-sectional and
panel setting. The package can analyze binary, multivariate, ordered, and
ranked choices, and places a special focus on modeling heterogeneity of
choice behavior among deciders. The main functionality includes model
fitting via Markov chain Monte Carlo methods, tools for convergence
diagnostic, choice data simulation, in-sample and out-of-sample choice
prediction, and model selection using information criteria and Bayes
factors. The latent class model extension facilitates preference-based
decider classification, where the number of latent classes can be inferred
via the Dirichlet process or a weight-based updating scheme. This allows for
flexible modeling of choice behavior without the need to impose structural
constraints. For a reference on the method see Oelschlaeger and Bauer (2021)
<https://trid.trb.org/view/1759753>.
Rcpp, mvtnorm, viridis, ggplot2, rlang, mixtools, doSNOW,
foreach, progress, gridExtra, crayon, plotROC, MASS
knitr, rmarkdown, mlogit, testthat (>= 3.0.0), covr
Lennart Oelschläger [aut, cre]
(<https://orcid.org/0000-0001-5421-9313>),
Dietmar Bauer [aut] (<https://orcid.org/0000-0003-2920-7032>),
Sebastian Büscher [ctb],
Manuel Batram [ctb]