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 |
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