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COUNT (version 1.3.4)

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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Install

install.packages('COUNT')

Monthly Downloads

968

Version

1.3.4

License

GPL-2

Maintainer

Andrew Robinson Joseph M Hilbe hilbeasuedu

Last Published

October 19th, 2016

Functions in COUNT (1.3.4)

NB1: maximum likelihood linear negative binomial regression

myTable

Frequency table

ml.nbc

NBC: maximum likelihood linear negative binomial regression

modelfit

Fit Statistics for generalized linear models

ml.pois

NB2: maximum likelihood Poisson regression

nuts

ml.nb2

NB2: maximum likelihood linear negative binomial regression

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

nbc_syn

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

nb2_syn

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

nb1_syn

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

probit_syn

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

poisson_syn

Poisson : generic synthetic Poisson data and model

poi.obs.pred

Table of Poisson counts: observed vs predicted proportions and difference

nb2.obs.pred

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