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bayescount (version 0.9.0-7)

Bayesian analysis of count distributions using MCMC

Description

A set of functions to allow analysis of count data (such as faecal egg count data) using Bayesian MCMC methods. Data is analysed using any of the (zero-inflated) gamma Poisson (equivalent to a negative binomial), (zero-inflated) simple or independant Poisson, (zero-inflated) lognormal Poisson or (zero-inflated) Weibull Poisson models. Returns information on the possible values for mean count, coefficient of variation and zero inflation (true prevalence) present in the data. A complete faecal egg count reduction test (FECRT) model is also implemented, which returns inference on the true efficacy of the drug from the pre and post treatment data provided, using non-parametric bootstrapping as well as using Bayesian MCMC. Requires Just Another Gibbs Sampler (JAGS) for most functions, see: http://www-fis.iarc.fr/~martyn/software/jags/. *THIS SOFTWARE IS INTENDED FOR EDUCATIONAL PURPOSES ONLY AND SHOULD NOT BE RELIED UPON FOR REAL WORLD APPLICATIONS*

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Version

Install

install.packages('bayescount')

Monthly Downloads

274

Version

0.9.0-7

License

GPL

Maintainer

Matthew Denwood

Last Published

December 8th, 2023

Functions in bayescount (0.9.0-7)

maximise.likelihood

Calculate the Maximum Likelihood Parameters of a Continuous or Count Distribution
fecrt

Analyse FECRT Data Using Mcmc to Give a Probability Distribution of Values for the Mean Egg Count Reduction
likelihood

Calculate the (Log) Likelihood of Obtaining Data from a Distribution
bayescount

Analyse Count data using MCMC
run.model

Analyse Count Data Using Jags
lnormal.params

Calculate the Log-Normal Mean and Standard Deviation Using the Normal Mean and Standard Deviation
normal.params

Calculate the Normal Mean and Standard Deviation Using the Log-Normal Mean and Standard Deviation
bayescount.single

Analyse Count data using MCMC