Package: dupiR
Type: Package
Title: Bayesian Inference from Count Data using Discrete Uniform Priors
Version: 1.2.1
Date: 2024-03-17
Depends: R (>= 2.15.1), methods
Authors@R: 
    c(person(given = "Federico",
           family = "Comoglio",
           role = c("aut", "cre"),
           email = "federico.comoglio@gmail.com"),
    person(given = "Maurizio",
           family = "Rinaldi",
           role = c("aut"),
           email = "maurizio.rinaldi@uniupo.it")
    )
Author: Federico Comoglio [aut, cre], Maurizio Rinaldi [aut]
Maintainer: Federico Comoglio <federico.comoglio@gmail.com>
Description: 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>.
License: GPL-2
LazyLoad: yes
RoxygenNote: 7.3.1
Encoding: UTF-8
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
Imports: graphics, plotrix, stats, utils
Repository: https://federicocomoglio.r-universe.dev
Date/Publication: 2024-03-20 08:24:08 UTC
RemoteUrl: https://github.com/federicocomoglio/dupir
RemoteRef: HEAD
RemoteSha: ddb65a941a2b43308290ef1f5030ffae0ca3d2c5
NeedsCompilation: no
Packaged: 2026-10-15 06:40:50 UTC; root