dupiR: 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) <doi:10.1371/journal.pone.0074388>.

Version: 1.2.1
Depends: R (≥ 2.15.1), methods
Imports: graphics, plotrix, stats, utils
Suggests: testthat (≥ 3.0.0)
Published: 2024-03-21
DOI: NA
Author: Federico Comoglio [aut, cre], Maurizio Rinaldi [aut]
Maintainer: Federico Comoglio <federico.comoglio at gmail.com>
License: GPL-2
NeedsCompilation: no
Citation: dupiR citation info
Materials: README NEWS
CRAN checks: dupiR results

Documentation:

Reference manual: dupiR.pdf

Downloads:

Package source: dupiR_1.2.1.tar.gz
Windows binaries: r-devel: dupiR_1.2.1.zip, r-release: dupiR_1.2.1.zip, r-oldrel: dupiR_1.2.1.zip
macOS binaries: r-release (arm64): dupiR_1.2.1.tgz, r-oldrel (arm64): dupiR_1.2.1.tgz, r-release (x86_64): dupiR_1.2.1.tgz, r-oldrel (x86_64): dupiR_1.2.1.tgz
Old sources: dupiR archive

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