R package hmmm: hierarchical multinomial marginal models. 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.
Keywords for this software
References in zbMATH (referenced in 10 articles , 1 standard article )
Showing results 1 to 10 of 10.
- Colombi, Roberto; Giordano, Sabrina: Likelihood-based tests for a class of misspecified finite mixture models for ordinal categorical data (2019)
- Nicolussi, Federica; Cazzaro, Manuela: Context-specific independencies embedded in chain graph models of type I (2019)
- Colombi, Roberto; Forcina, A.: Testing order restrictions in contingency tables (2016)
- Colombi, R.; Giordano, S.: Multiple hidden Markov models for categorical time series (2015)
- Roberto Colombi; Sabrina Giordano; Manuela Cazzaro: hmmm: An R Package for Hierarchical Multinomial Marginal Models (2014) not zbMATH
- Colombi, Roberto; Giordano, Sabrina: Monotone dependence in graphical models for multivariate Markov chains (2013)
- Lang, Joseph B.; Iannario, Maria: Improved tests of independence in singly-ordered two-way contingency tables (2013)
- Colombi, R.; Giordano, S.: Graphical models for multivariate Markov chains (2012)
- Colombi, Roberto; Giordano, Sabrina: Testing lumpability for marginal discrete hidden Markov models (2011)
- Colombi, Roberto; Giordano, Sabrina: Monotone graphical multivariate Markov chains (2010)