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prevalence

The prevalence package provides Frequentist and Bayesian methods useful in prevalence assessment studies. Several methods are available for estimating True Prevalence (TP) from Apparent Prevalence (AP).

Available functions

Install

To download and install the latest released version from CRAN:

install.packages("prevalence")

To download and install the latest development version from GitHub:

devtools::install_github("brechtdv/prevalence")

IMPORTANT: the truePrev functions in the prevalence package call on JAGS (Just Another Gibbs Sampler), which therefore has to be available on the user's system. JAGS can be downloaded from http://mcmc-jags.sourceforge.net/.

More

Function truePrev is also available as an online Shiny application: https://cbra.shinyapps.io/truePrev/

More information and tutorials are available at http://prevalence.cbra.be/

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Version

Install

install.packages('prevalence')

Monthly Downloads

328

Version

0.4.1

License

GPL (>= 2)

Maintainer

Brecht Devleesschauwer

Last Published

June 3rd, 2022

Functions in prevalence (0.4.1)

propCI

Calculate confidence intervals for prevalences and other proportions
print-methods

Methods for Function print in Package prevalence
plot-methods-coda

Plotting functions from package coda
plot-methods

Methods for Function plot in Package prevalence
prevalence-package

Tools for prevalence assessment studies
prev-class

Class "prev"
convert-methods

Methods for Function as.matrix in Package prevalence
define

Definition of truePrevMulti and truePrevMulti2 model
summary-methods

Methods for Function summary in Package prevalence
show-methods

Methods for Function show in Package prevalence
truePrev

Estimate true prevalence from individuals samples
truePrevMulti

Estimate true prevalence from individuals samples using multiple tests -- conditional probability scheme
betaPERT

Calculate the parameters of a Beta-PERT distribution
truePrevPools

Estimate true prevalence from pooled samples
truePrevMulti2

Estimate true prevalence from individuals samples using multiple tests -- covariance scheme
betaExpert

Calculate the parameters of a Beta distribution based on expert information