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COUNT (version 1.3.2)

Functions, data and code for count data.

Description

Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2-nd 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

839

Version

1.3.2

License

GPL-2

Maintainer

Andrew Robinson Joseph M Hilbe hilbeasuedu

Last Published

August 15th, 2014

Functions in COUNT (1.3.2)

titanic

ml.pois

NB2: maximum likelihood Poisson regression

medpar

probit_syn

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

rwm

titanicgrp

ml.nb2

NB2: maximum likelihood linear negative binomial regression

lbw

rwm1984

poisson_syn

Poisson : generic synthetic Poisson data and model

Fit Statistics for generalized linear models

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

nb2_syn

Negative binomial (NB2): generic synthetic negative binomial data and model

azcabgptca

poi.obs.pred

Table of Poisson counts: observed vs predicted proportions and difference

nb1_syn

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

azprocedure

fishing

ml.nb1

NB1: maximum likelihood linear negative binomial regression

nbc_syn

Negative binomial (NB-C): generic synthetic canonical negative binomial data and model

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

azdrg112

rwm5yr

ml.nbc

NBC: maximum likelihood linear negative binomial regression

affairs

smoking

lbwgrp