plotPosterior
(Package: dupiR) :
Plot posterior probability distributions
Produces publication-level plots of posterior probability distributions computed using computePosterior. A data summary, credible intervals at a given confidence level, maximum a posteriori (and more) are indicated.
getPosteriorParam
(Package: dupiR) :
Compute posterior probability distribution parameters
Obtain statistical parameters from the posterior probability distribution. Particularly, this function computes credible intervals at a given confidence level (default to 95%).
Counts
(Package: dupiR) :
Class "Counts" -- a container for measurements and dupiR inference results
Definition of an object of this class requires a set of measurements, i.e. a collection of counts and sampling fractions. Inference of the posterior distribution by dupiR (computePosterior) and subsequent call to getPosteriorParam will fill all additional slots.
● Data Source:
CranContrib
● Keywords: class
● Alias: Counts, Counts-class, summary,Counts-method
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dupiR-package
(Package: dupiR) :
Bayesian inference using discrete uniform priors with R
This package implements a Bayesian approach to infer population sizes from count data. The package takes a set of sample counts obtained by sampling fractions of a finite volume containing an homogeneously dispersed population of identical objects and returns the posterior probability distribution of the population size. The algorithm makes use of a binomial likelihood and non-conjugate, discrete uniform priors. dupiR can be applied to both sampling with or without replacement.
computePosterior
(Package: dupiR) :
Compute the posterior probability distribution of the population size
Compute the posterior probability distribution of the population size using a discrete uniform prior and a binomial likelihood (DUP method). When applicable, an approximation using a Gamma prior and a Poisson likelihood is used instead (GP method, see Clough et al).
getCounts
(Package: dupiR) :
Accessors for the 'counts' and 'fractions' slots of a Counts object.
Each measurement consists of an integer count and a corresponding sampling fraction. These values are required to defined an object of class Counts and are subsequently stored in the counts and fractions slots. The counts slot is an integer vector of counts. The fractions slot is a numeric vector of matched sampling fractions.
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