  Kernel smoothing represents a useful approach in the graduation of mortality
rates. Though there exist several options for performing kernel smoothing in
statistical software packages, there have been very few contributions to date
that have focused on applications of these techniques in the graduation
context. Also, although it has been shown that the use of a variable or
adaptive smoothing parameter, based on the further information provided by the
exposed to the risk of death, provides additional benefits, specific
computational tools for this approach are essentially absent. Furthermore,
little attention has been given to providing methods in available software for
any kind of subsequent analysis with respect to the graduated mortality rates.
To facilitate analyses in the field, the R package DBKGrad is introduced. Among
the available kernel approaches, it considers a recent discrete beta kernel
estimator, in both its fixed and adaptive variants. In this approach, boundary
bias is automatically reduced and age is pragmatically considered as a discrete
variable. The bandwidth, fixed or adaptive, is allowed to be manually given by
the user or selected by cross-validation. Pointwise confidence intervals, for
each considered age, are also provided. An application to mortality rates from
the Sicily Region (Italy) for the year 2008 is also presented to exemplify the
use of the package.
