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 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- 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)
- Colombi, Roberto; Giordano, Sabrina: Monotone dependence in graphical models for multivariate Markov chains (2013)
- Colombi, Roberto; Giordano, Sabrina: Testing lumpability for marginal discrete hidden Markov models (2011)