Colombi Roberto and Sabrina Giordano and Manuela Cazzaro, with  contributions from Joseph Lang
Functions for specifying and fitting marginal models for contingency tables proposed
by Bergsma and Rudas (2002) here called hierarchical multinomial marginal models (hmmm) and their extensions presented by Bartolucci et al.
(2007); multinomial Poisson homogeneous (mph) models and homogeneous linear predictor (hlp) models for contingency
tables proposed by Lang (2004) and (2005); hidden Markov models where the distribution of the observed variables
is described by a marginal model.
Inequality constraints on the parameters are allowed and can be tested.

Colombi Roberto and Sabrina Giordano and Manuela Cazzaro, with contributions from Joseph Lang

Roberto Colombi, Sabrina Giordano, Manuela Cazzaro

"Mixture and Hidden Markov Models with R" Datasets and Example
Code
c(person("Ingmar", "Visser",role=c("aut","cre"), email="i.visser@uva.nl"),
person("Maarten", "Speekenbrink", role="aut", email="m.speekenbrink@ucl.ac.uk"))
Datasets and code examples that accompany our book Visser & Speekenbrink (2021), "Mixture and Hidden Markov Models with R", <https://depmix.github.io/hmmr/>.
Ingmar Visser [aut, cre],
Maarten Speekenbrink [aut]

Implements an S3 class for storing and formatting time-of-day
values, based on the 'difftime' class.

c(
person("Kirill", "Müller", role = c("aut", "cre"), email = "krlmlr+r@mailbox.org"),
person("The R Consortium", role = "cph")
)
Kirill Müller [aut, cre],
The R Consortium [cph]

c(
person("Kirill", "Müller", role = c("aut", "cre"), email = "krlmlr+r@mailbox.org"),
person("The R Consortium", role = "fnd")
)
crayon, lubridate, pillar (>= 1.1.0), testthat
Kirill Müller [aut, cre],
The R Consortium [fnd]

c(
person("Kirill", "Müller", role = c("aut", "cre"), email = "krlmlr+r@mailbox.org"),
person("The R Consortium", role = "fnd"),
person("RStudio", role = "fnd")
)
methods, pkgconfig, rlang, vctrs (>= 0.2.0)
Kirill Müller [aut, cre],
The R Consortium [fnd],
RStudio [fnd]

methods, pkgconfig, rlang, vctrs (>= 0.2.1)