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bootComb  

Combine Parameter Estimates via Parametric Bootstrap
View on CRAN: Click here


Download and install bootComb package within the R console
Install from CRAN:
install.packages("bootComb")

Install from Github:
library("remotes")
install_github("cran/bootComb")

Install by package version:
library("remotes")
install_version("bootComb", "1.1.2")



Attach the package and use:
library("bootComb")
Maintained by
Marc Henrion
[Scholar Profile | Author Map]
First Published: 2020-08-20
Latest Update: 2022-01-30
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) for an example of combining conditional prevalences.
How to cite:
Marc Henrion (2020). bootComb: Combine Parameter Estimates via Parametric Bootstrap. R package version 1.1.2, https://cran.r-project.org/web/packages/bootComb. Accessed 10 Apr. 2025.
Previous versions and publish date:
0.2.0 (2020-08-20 10:50), 1.0.0 (2020-09-07 17:50), 1.0.1 (2020-11-18 21:00), 1.0.2 (2021-07-13 00:00), 1.1.1 (2022-01-04 16:30)
Other packages that cited bootComb R package
View bootComb citation profile
Other R packages that bootComb depends, imports, suggests or enhances
Complete documentation for bootComb
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