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 |
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:
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