Package: bootComb 1.1.2
Marc Henrion
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.
Authors:
bootComb_1.1.2.tar.gz
bootComb_1.1.2.tar.gz(r-4.5-noble)bootComb_1.1.2.tar.gz(r-4.4-noble)
bootComb_1.1.2.tgz(r-4.4-emscripten)bootComb_1.1.2.tgz(r-4.3-emscripten)
bootComb.pdf |bootComb.html✨
bootComb/json (API)
NEWS
# Install 'bootComb' in R: |
install.packages('bootComb', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:3cc937aa34. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 10 2024 |
R-4.5-linux | OK | Oct 10 2024 |
Exports:adjPrevSensSpecadjPrevSensSpecCIbootCombgetBetaFromCIgetExpFromCIgetGammaFromCIgetNegBinFromCIgetNormFromCIgetPoisFromCIsimScenPrevSensSpecsimScenProductTwoPrevs
Dependencies:MASS