The ContionAl Prevalence Estimation (cape) 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 cape package also contains code for simulation studies and sensitivity analysis reported in the companion paper Guerrier et al. (2022), 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. (2022) 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 cape package can be installed from GitHub as follows:

# Install devtools

# Install the package from GitHub

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

How to cite

    title = {{cape}: Conditional Prevalence Estimation using Random and Non-Random Sample Information},
    author = {Guerrier, S and Kuzmics, C and Victoria-Feser, M.-P.},
    year = {2022},
    note = {R package},
    url = {}


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 TLDR Legal or GNU which will provide a synopsis of the restrictions placed upon the code.


Guerrier, Stéphane, Christoph Kuzmics, and Maria-Pia Victoria-Feser. 2022. “Assessing Coronavirus SARS-CoV-2 Prevalence in Austria in 2020, with Sample Surveys and Census Data with Participation Bias”,