Bayesian Inference from Count Data using Discrete Uniform Priors

We consider a set of sample counts obtained by sampling arbitrary fractions of a finite volume containing an homogeneously dispersed population of identical objects. This package implements a Bayesian derivation of the posterior probability distribution of the population size using a binomial likelihood and non-conjugate, discrete uniform priors under sampling with or without replacement. This can be used for a variety of statistical problems involving absolute quantification under uncertainty. See Comoglio et al. (2013) .

dupiR: an R package for Bayesian inference using discrete uniform priors

This R package implements a Bayesian approach to infer population sizes from count data. The package takes a set of sample counts obtained by sampling fractions of a finite volume containing an homogeneously dispersed population of identical objects and returns the posterior probability distribution of the population size. The algorithm makes use of a binomial likelihood and non-conjugate, discrete uniform priors. dupiR can be applied to both sampling with or without replacement.

Further details on the statistical framework can be found in:

• Comoglio F, Fracchia L, Rinaldi M (2013) Bayesian Inference from Count Data Using Discrete Uniform Priors. PLoS ONE 8(10): e74388

Please cite this article if you are using dupiR for your research.

Installation

You can install the latest package release from CRAN:

install.packages("dupiR")

or the development version from GitHub:

# install.packages("devtools")
devtools::install_github("FedericoComoglio/dupiR")