Functions, Data and Code for Count Data

Documentation for package ‘COUNT’ version 1.3.4

affairs	affairs
azcabgptca	azcabgptca
azdrg112	azdrg112
azpro	azpro
azprocedure	azprocedure
badhealth	badhealth
fasttrakg	fasttrakg
fishing	fishing
lbw	lbw
lbwgrp	lbwgrp
logit_syn	Logistic regression : generic synthetic binary/binomial logistic data and model
loomis	loomis
mdvis	mdvis
medpar	medpar
ml.nb1	NB1: maximum likelihood linear negative binomial regression
ml.nb2	NB2: maximum likelihood linear negative binomial regression
ml.nbc	NBC: maximum likelihood linear negative binomial regression
ml.pois	NB2: maximum likelihood Poisson regression
modelfit	Fit Statistics for generalized linear models
myTable	Frequency table
nb1_syn	Negative binomial (NB1): generic synthetic linear negative binomial data and model
nb2.obs.pred	Table of negative binomial counts: observed vs predicted proportions and difference
nb2_syn	Negative binomial (NB2): generic synthetic negative binomial data and model
nbc_syn	Negative binomial (NB-C): generic synthetic canonical negative binomial data and model
nuts	nuts
poi.obs.pred	Table of Poisson counts: observed vs predicted proportions and difference
poisson_syn	Poisson : generic synthetic Poisson data and model
probit_syn	Probit regression : generic synthetic binary/binomial probit data and model
rwm	rwm
rwm1984	rwm1984
rwm5yr	rwm5yr
ships	ships
smoking	smoking
titanic	titanic
titanicgrp	titanicgrp