hmmm-package {hmmm}

package hmmm

Description

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, 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.

Author(s)

Roberto Colombi, Sabrina Giordano and Manuela Cazzaro. Joseph B. Lang is the author of functions ‘num.deriv.fct’, ‘create.U’ for mph models.

References

Bergsma WP, Rudas T (2002) Marginal models for categorical data. The Annals of Statistics, 30, 140-159

Bartolucci F, Colombi R, Forcina A (2007) An extended class of marginal link functions for modelling contingency tables by equality and inequality constraints. Statistica Sinica, 17, 691-711

Lang JB (2004) Multinomial Poisson homogeneous models for contingency tables. The Annals of Statistics, 32, 340-383

Lang JB (2005) Homogeneous linear predictor models for contingency tables. Journal of the American Statistical Association, 100, 121-134.