  Background. The bootComb R package allows researchers to derive confidence
intervals with correct target coverage for arbitrary combinations of arbitrary
numbers of independently estimated parameters. Previous versions (< 1.1.0) of
bootComb used independent bootstrap sampling and required that the parameters
themselves are independent - an unrealistic assumption in some real-world
applications.
  Findings. Using Gaussian copulas to define the dependence between parameters,
the bootComb package has been extended to allow for dependent parameters.
  Implications. The updated bootComb package can now handle cases of dependent
parameters, with users specifying a correlation matrix defining the dependence
structure. While in practice it may be difficult to know the exact dependence
structure between parameters, `bootComb` allows running sensitivity analyses to
assess the impact of parameter dependence on the resulting confidence interval
for the combined parameter.
  Availability. bootComb is available from the Comprehensive R Archive Network
(https://CRAN.R-project.org/package=bootComb).
