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hmmm (version 1.0-4)

Hierarchical Multinomial Marginal Models

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) 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.

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Version

Install

install.packages('hmmm')

Monthly Downloads

614

Version

1.0-4

License

GPL (>= 2)

Maintainer

Colombi Roberto

Last Published

March 14th, 2018

Functions in hmmm (1.0-4)

lists of marginal sets

hmm model with covariates effect on parameters

recursive marginal interactions

print for the class hmmmfit

religion, political orientation and teenage birth control data

religion and political orientation data

political orientation and teenage birth control data

print for the class hidden

fit mph models under inequality constraints

define a hmm model

summary.hmmmfit

summary for the class hmmmfit

summary.hmmmmod

summary and print for the class hmmmmod

summary for the class hidden

summary.hmmmchibar

summary for hmmm.chibar

summary and print for the class mphfit

print.hmmmchibar

print for hmmm.chibar

soft-drinks data

longitudinal study of mental depression

vector of frequencies from a data frame

akaike criterium

factory accident data

anova for the class hidden

ML estimation of a multinomial hidden Markov model

anova for the class hmmmfit

design matrix for a hmm model

function to compute the generalized marginal interactions associated to a hierarchical family of marginal sets

chi-bar statistic test for hmm models

function to compute a vector of joint probabilities from a vector of generalized marginal interactions (gmi)

define a log-linear model

Kentucky traffic accident data

fit a hmm model