compute_ecdf

Compute ECDF (empirical cumulative distribution function)

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>.

Federico Comoglio

dupiR

Bayesian Inference from Count Data using Discrete Uniform Priors

Maurizio Rinaldi

compute_ecdf function

<dl><dt>posterior</dt>
<dd>numeric vector of posterior probabilities over the prior support</dd></dl>

Arguments

Compute ECDF (empirical cumulative distribution function) — compute_ecdf

<dl>

<dt>posterior</dt>
<dd>numeric vector of posterior probabilities over the prior support</dd>

</dl>

compute_ecdf: Compute ECDF (empirical cumulative distribution function)

Description

Usage

Value

Arguments