PoolTestR-package: PoolTestR: Prevalence and Regression for Pool-Tested...
In PoolTestR: Prevalence and Regression for Pool-Tested (Group-Tested) Data
PoolTestR-package R Documentation
PoolTestR: Prevalence and Regression for Pool-Tested (Group-Tested) Data
Description
An easy-to-use tool for working with presence/absence tests on 'pooled' or 'grouped' samples. The primary application is for estimating prevalence of a marker in a population based on the results of tests on pooled specimens. This sampling method is often employed in surveillance of rare conditions in humans or animals (e.g. molecular xenomonitoring). The package was initially conceived as an R-based alternative to the molecular xenomonitoring software, 'PoolScreen' https://sites.uab.edu/statgenetics/software/. However, it goes further, allowing for estimates of prevalence to be adjusted for hierarchical sampling frames, and perform flexible mixed-effect regression analyses (McLure et al. Environmental Modelling and Software. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.envsoft.2021.105158")}). The package is currently in early stages, however more features are planned or in the works: e.g. adjustments for imperfect test specificity/sensitivity, functions for helping with optimal experimental design, and functions for spatial modelling.
This is a package for working with presence/absence tests on pooled
or grouped samples. The primary application is for estimating prevalence of
a marker in a population based on the results of tests on pooled specimens.
This sampling method is often employed in surveillance of rare conditions in
humans or animals (e.g. livestock or xenomonitoring). The package was initially
conceived as an R-based alternative to the xenomonitoring software,
PoolScreen. However, it goes further, allowing for estimates of prevalence to
adjusted for hierarchy in sampling frames, and perform flexible (mixed effect)
regression analyses. The package is currently in early stages, however more
features are planned or in the works: e.g. adjustments for imperfect test
specificity/sensitivity, functions for helping with optimal experimental
design, and functions for spatial modelling.
Author(s)
Maintainer: Angus McLure angus.mclure@anu.edu.au (ORCID)
Other contributors:
Caitlin Cherryh (ORCID) [contributor]
References
Angus McLure, Ben O'Neill, Helen Mayfield, Colleen Lau, Brady McPherson (2021). PoolTestR: An R package for estimating prevalence and regression modelling for molecular xenomonitoring and other applications with pooled samples. Environmental Modelling & Software, 145:105158. <DOI:10.1016/j.envsoft.2021.105158>
Stan Development Team (2019). RStan: the R interface to Stan. R package version 2.19.2. https://mc-stan.org
Paul-Christian Bürkner (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1-28. <DOI:10.18637/jss.v080.i01>
See Also
Useful links:
PoolTestR documentation built on April 3, 2025, 9:28 p.m.
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| PoolTestR-package | R Documentation |
PoolTestR: Prevalence and Regression for Pool-Tested (Group-Tested) Data
Description
An easy-to-use tool for working with presence/absence tests on 'pooled' or 'grouped' samples. The primary application is for estimating prevalence of a marker in a population based on the results of tests on pooled specimens. This sampling method is often employed in surveillance of rare conditions in humans or animals (e.g. molecular xenomonitoring). The package was initially conceived as an R-based alternative to the molecular xenomonitoring software, 'PoolScreen' https://sites.uab.edu/statgenetics/software/. However, it goes further, allowing for estimates of prevalence to be adjusted for hierarchical sampling frames, and perform flexible mixed-effect regression analyses (McLure et al. Environmental Modelling and Software. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.envsoft.2021.105158")}). The package is currently in early stages, however more features are planned or in the works: e.g. adjustments for imperfect test specificity/sensitivity, functions for helping with optimal experimental design, and functions for spatial modelling.
This is a package for working with presence/absence tests on pooled or grouped samples. The primary application is for estimating prevalence of a marker in a population based on the results of tests on pooled specimens. This sampling method is often employed in surveillance of rare conditions in humans or animals (e.g. livestock or xenomonitoring). The package was initially conceived as an R-based alternative to the xenomonitoring software, PoolScreen. However, it goes further, allowing for estimates of prevalence to adjusted for hierarchy in sampling frames, and perform flexible (mixed effect) regression analyses. The package is currently in early stages, however more features are planned or in the works: e.g. adjustments for imperfect test specificity/sensitivity, functions for helping with optimal experimental design, and functions for spatial modelling.
Author(s)
Maintainer: Angus McLure angus.mclure@anu.edu.au (ORCID)
Other contributors:
Caitlin Cherryh (ORCID) [contributor]
References
Angus McLure, Ben O'Neill, Helen Mayfield, Colleen Lau, Brady McPherson (2021). PoolTestR: An R package for estimating prevalence and regression modelling for molecular xenomonitoring and other applications with pooled samples. Environmental Modelling & Software, 145:105158. <DOI:10.1016/j.envsoft.2021.105158>
Stan Development Team (2019). RStan: the R interface to Stan. R package version 2.19.2. https://mc-stan.org
Paul-Christian Bürkner (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1-28. <DOI:10.18637/jss.v080.i01>
See Also
Useful links:
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