'Rpolyhedra-package.R' 'polyhedra-lib.R' 'ledger-lib.R'
'db-lib.R' 'env-lib.R' 'package-lib.R' 'serialization-lib.R'
'public-lib.R' 'test-lib.R' 'zzz.R'

c(
person("Alejandro", "Baranek",,"abaranek@dc.uba.ar", c("aut","com","cre", "cph")),
person("Leonardo","Belen",,"leobelen@gmail.com", c("aut","com","cph")),
person("Barret","Schloerke",,"schloerke@gmail.com", c("rev")),
person("Lijia","Yu",,"yu@lijiayu.net", c("rev"))
)
R6, geometry, rgl, stringr, dplyr, XML, digest, testthat,
futile.logger, git2r,
Alejandro Baranek [aut, com, cre, cph],
Leonardo Belen [aut, com, cph],
Barret Schloerke [rev],
Lijia Yu [rev]

c(
person("Alejandro", "Baranek",,"abaranek@dc.uba.ar", c("aut","com","cre", "cph")),
person("Leonardo","Belen",,"leobelen@gmail.com", c("aut","com","cph")),
person(,"qbotics",,"qbotics6@gmail.com", c("cph")),
person("Barret","Schloerke",,"schloerke@gmail.com", c("rev")),
person("Lijia","Yu",,"yu@lijiayu.net", c("rev")))
R6, geometry, rgl, stringr, XML, digest, futile.logger, git2r
testthat, dplyr, knitr, pkgdown, rmarkdown, covr, codemetar
Alejandro Baranek [aut, com, cre, cph],
Leonardo Belen [aut, com, cph],
qbotics [cph],
Barret Schloerke [rev],
Lijia Yu [rev]

R6, geometry, rgl, stringr, XML, digest, lgr, git2r

Poppler Glib interface headers and libraries
(<http://poppler.freedesktop.org/>) [Debian/Ubuntu:
libpoppler-glib-dev, Fedora: poppler-glib-devel]

PDF tools based on the Poppler PDF rendering library.
See <http://poppler.freedesktop.org/> for more information on Poppler.

Bayes Estimation of Latent Class Mixed Multinomial Probit Models
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 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, see Oelschl\u00e4ger and Bauer (2021) <https://trid.trb.org/view/1759753>.
Lennart Oelschläger [aut, cre],
Dietmar Bauer [aut],
Sebastian Büscher [ctb],
Manuel Batram [ctb]