Bayesian Inference from Count Data using Discrete Uniform Priors R package dupiR version 1.2.1 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) <doi:10.1371/journal.pone.0074388>. Software Depends: R (>= 2.15.1), methods Imports: graphics, plotrix, stats, utils Suggests: testthat (>= 3.0.0) Federico Comoglio <federico.comoglio@gmail.com> Comprehensive R Archive Network (CRAN) Federico Comoglio [aut, cre], Maurizio Rinaldi [aut] GPL-2 2024-03-21 application/tgz https://CRAN.R-project.org/package=dupiR doi:10.32614/CRAN.package.dupiR