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:Marc Henrion [aut, cre]

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'))

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 5 scripts 288 downloads 1 mentions 11 exports 1 dependencies

Last updated 3 years agofrom:3cc937aa34. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 10 2024
R-4.5-linuxOKOct 10 2024

Exports:adjPrevSensSpecadjPrevSensSpecCIbootCombgetBetaFromCIgetExpFromCIgetGammaFromCIgetNegBinFromCIgetNormFromCIgetPoisFromCIsimScenPrevSensSpecsimScenProductTwoPrevs

Dependencies:MASS

Readme and manuals

Help Manual

Help pageTopics
Adjust a prevalence point estimate for a given assay sensitivity and specificity.adjPrevSensSpec
Adjust a prevalence point estimate and confidence interval for a given assay sensitivity and specificity (also known only imprecisely).adjPrevSensSpecCI
Combine parameter estimates via bootstrapbootComb
Find the best-fit beta distribution for a given confidence interval for a probability parameter.getBetaFromCI
Find the best-fit exponential distribution for a given confidence interval.getExpFromCI
Find the best-fit gamma distribution for a given confidence interval.getGammaFromCI
Find the best-fit negative binomial distribution for a given confidence interval.getNegBinFromCI
Find the best-fit normal / Gaussian distribution for a given confidence interval.getNormFromCI
Find the best-fit Poisson distribution for a given confidence interval.getPoisFromCI
Determine the parameters of the best-fit beta distribution for a given confidence interval for a probability parameter.identifyBetaPars
Determine the parameters of the best-fit exponential distribution for a given confidence interval.identifyExpPars
Determine the parameters of the best-fit gamma distribution for a given confidence interval.identifyGammaPars
Determine the parameters of the best-fit negative binomial distribution for a given confidence interval.identifyNegBinPars
Determine the parameters of the best-fit normal / Gaussian distribution for a given confidence interval.identifyNormPars
Determine the parameters of the best-fit Poisson distribution for a given confidence interval.identifyPoisPars
Simulation scenario for adjusting a prevalence for sensitivity and specificity.simScenPrevSensSpec
Simulation scenario for the product of two prevlaence estimates.simScenProductTwoPrevs
Compute the sum of squares between the theoretical and observed quantiles of a beta distribution.ssBetaPars
Compute the sum of squares between the theoretical and observed quantiles of an exponential distribution.ssExpPars
Compute the sum of squares between the theoretical and observed quantiles of a gamma distribution.ssGammaPars
Compute the sum of squares between the theoretical and observed quantiles of a negative binomial distribution.ssNegBinPars
Compute the sum of squares between the theoretical and observed quantiles of a normal / Gaussian distribution.ssNormPars
Compute the sum of squares between the theoretical and observed quantiles of a Poisson distribution.ssPoisPars