bootComb

Description

Propagate uncertainty from several estimates when combining these estimates via a function. This is done by using the parametric bootstrap to simulate values from the distribution of each estimate to build up an empirical distribution of the combined parameter. Finally either the percentile method is used or the highest density interval is chosen to derive a confidence interval for the combined parameter with the desired coverage. Gaussian copulas are used for when parameters are assumed to be dependent / correlated. References: Davison and Hinkley (1997,ISBN:0-521-57471-4) for the parametric bootstrap and percentile method, Gelman et al. (2014,ISBN:978-1-4398-4095-5) for the highest density interval, Stockdale et al. (2020)<doi:10.1016/j.jhep.2020.04.008> for an example of combining conditional prevalences.

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Maintainer: ‘Marc Henrion <mhenrion@mlw.mw>’

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Maintainer: ‘Marc Henrion <mhenrion@mlw.mw>’

Package CITATION file contains call(s) to old-style personList() or
as.personList().  Please use c() on person objects instead.
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use bibentry() instead.

Maintainer: ‘Marc Henrion <mhenrion@mlw.mw>’

Package CITATION file contains call(s) to old-style personList() or
as.personList().  Please use c() on person objects instead.
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use bibentry() instead.

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