Bayesian Inference from Count Data using Discrete Uniform Priors
Compute ECDF (empirical cumulative distribution function)
Compute normalization constant
Compute the posterior probability distribution of the population size ...
Compute posterior probability with replacement
Compute sum of terms (function F, Comoglio et al.)
Compute single term (function F, Comoglio et al.)
An S4 class to store measurements (count data, sampling fractions), pr...
Bayesian inference from count data using discrete uniform priors
Compute posterior probability using a Gamma-Poisson model (Clough et a...
Get counts slot for an object of class Counts
Get fractions slot for an object of class Counts
Compute posterior probability distribution parameters (e.g. credible i...
Initialize Counts class
Constructor for Counts class
Plot method for Counts class
Plot posterior probability distribution and display posterior paramete...
Set counts slot for an object of class Counts
Set fractions slot for an object of class Counts
Print method for Counts class
Summary method for Counts class
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>.