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COUNT (version 1.3.5)

Functions, Data and Code for Count Data

Description

Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).

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Version

Install

install.packages('COUNT')

Monthly Downloads

968

Version

1.3.5

License

GPL-2

Maintainer

Andrew Robinson Joseph M Hilbe hilbeasuedu

Last Published

April 28th, 2025

Functions in COUNT (1.3.5)

logit_syn

Logistic regression : generic synthetic binary/binomial logistic data and model
medpar

medpar
ml.nb2

NB2: maximum likelihood linear negative binomial regression
rwm

rwm
myTable

Frequency table
modelfit

Fit Statistics for generalized linear models
ml.nb1

NB1: maximum likelihood linear negative binomial regression
ml.nbc

NBC: maximum likelihood linear negative binomial regression
nb2.obs.pred

Table of negative binomial counts: observed vs predicted proportions and difference
nb1_syn

Negative binomial (NB1): generic synthetic linear negative binomial data and model
rwm1984

rwm1984
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
rwm5yr

rwm5yr
ships

ships
poisson_syn

Poisson : generic synthetic Poisson data and model
poi.obs.pred

Table of Poisson counts: observed vs predicted proportions and difference
titanicgrp

titanicgrp
titanic

titanic
smoking

smoking
probit_syn

Probit regression : generic synthetic binary/binomial probit data and model
affairs

affairs
lbwgrp

lbwgrp
azcabgptca

azcabgptca
lbw

lbw
azdrg112

azdrg112
azpro

azpro
fishing

fishing
fasttrakg

fasttrakg
badhealth

badhealth
azprocedure

azprocedure
loomis

loomis
ml.pois

NB2: maximum likelihood Poisson regression
mdvis

mdvis