  Pooled testing (also known as group testing), where diagnostic tests are
performed on pooled samples, has broad applications in the surveillance of
diseases in animals and humans. An increasingly common use case is molecular
xenomonitoring (MX), where surveillance of vector-borne diseases is conducted
by capturing and testing large numbers of vectors (e.g. mosquitoes). The R
package PoolTestR was developed to meet the needs of increasingly large and
complex molecular xenomonitoring surveys but can be applied to analyse any data
involving pooled testing. PoolTestR includes simple and flexible tools to
estimate prevalence and fit fixed- and mixed-effect generalised linear models
for pooled data in frequentist and Bayesian frameworks. Mixed-effect models
allow users to account for the hierarchical sampling designs that are often
employed in surveys, including MX. We demonstrate the utility of PoolTestR by
applying it to a large synthetic dataset that emulates a MX survey with a
hierarchical sampling design.
