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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.2.1 |
rolling linux/jammy R-4.5 | dupiR_1.2.1.tar.gz |
177.1 KiB |
1.2.1 |
rolling linux/noble R-4.5 | dupiR_1.2.1.tar.gz |
176.7 KiB |
1.2.1 |
rolling source/ R- | dupiR_1.2.1.tar.gz |
13.0 KiB |
1.2.1 |
latest linux/jammy R-4.5 | dupiR_1.2.1.tar.gz |
177.1 KiB |
1.2.1 |
latest linux/noble R-4.5 | dupiR_1.2.1.tar.gz |
176.7 KiB |
1.2.1 |
latest source/ R- | dupiR_1.2.1.tar.gz |
13.0 KiB |
1.2.1 |
2026-04-26 source/ R- | dupiR_1.2.1.tar.gz |
13.0 KiB |
1.2.1 |
2026-04-23 source/ R- | dupiR_1.2.1.tar.gz |
13.0 KiB |
1.2.1 |
2026-04-09 windows/windows R-4.5 | dupiR_1.2.1.zip |
178.3 KiB |
1.2.1 |
2025-04-20 source/ R- | dupiR_1.2.1.tar.gz |
13.0 KiB |