bootComb: Combine Parameter Estimates via Parametric Bootstrap

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.

Version:	1.1.2
Imports:	MASS (≥ 7.3.54)
Suggests:	HDInterval (≥ 0.2.2)
Published:	2022-01-30
Author:	Marc Henrion [aut, cre]
Maintainer:	Marc Henrion <mhenrion at mlw.mw>
License:	GPL-3
NeedsCompilation:	no
Citation:	bootComb citation info
Materials:	README NEWS
CRAN checks:	bootComb results

Documentation:

Reference manual:

bootComb.pdf

Downloads:

Package source:	bootComb_1.1.2.tar.gz
Windows binaries:	r-devel: bootComb_1.1.2.zip, r-release: bootComb_1.1.2.zip, r-oldrel: bootComb_1.1.2.zip
macOS binaries:	r-release (arm64): bootComb_1.1.2.tgz, r-oldrel (arm64): bootComb_1.1.2.tgz, r-release (x86_64): bootComb_1.1.2.tgz, r-oldrel (x86_64): bootComb_1.1.2.tgz
Old sources:	bootComb archive

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