Proportion Estimation with Marginal Proxy Information

A system contains easy-to-use tools for the conditional estimation of the prevalence of an emerging or rare infectious diseases using the methods proposed in Guerrier et al. (2023) .

pempi Overview

The proportion estimation with marginal proxy information (pempi) package, allows to estimate and build confidence intervals for proportions, from random or stratified samples and census data with participation bias. Measurement errors in the form of false positive and false negative are also included in the inferential procedure. The pempi package also contains code for simulation studies and sensitivity analysis reported in the companion paper Guerrier et al. (2023), as well as the Austrian dataset on COVID-19 prevalence in November 2020.

Remark on notation

The notation and conventions used in Guerrier et al. (2023) are slightly amended for convenience in this package. In particular, we use R1 instead R11, R2 instead of R10, R3 instead of R01 and R4 instead of R00.

Package installation

The pempi package can be installed from GitHub as follows:

# Install devtools
install.packages("devtools")

# Install the package from GitHub
devtools::install_github("stephaneguerrier/pempi")

Note that Windows users are assumed that have Rtools installed (if this is not the case, please visit this link).

How to cite

@Manual{guerrier2023cape,
    title = {{pempi}: Proportion estimation with marginal proxy information},
    author = {Guerrier, S and Kuzmics, C and Victoria-Feser, M.-P.},
    year = {2023},
    note = {R package},
    url = {https://github.com/stephaneguerrier/pempi}
}

License

The license this source code is released under is the GNU AFFERO GENERAL PUBLIC LICENSE (AGPL) v3.0. Please see the LICENSE file for full text. Otherwise, please consult GNU which will provide a synopsis of the restrictions placed upon the code.

References

Guerrier, Stéphane, Christoph Kuzmics, and Maria-Pia Victoria-Feser. 2023. “Assessing COVID-19 Prevalence in Austria with Infection Surveys and Case Count Data as Auxiliary Information”, https://arxiv.org/abs/2012.10745.