This package implements random generation procedures for sampling from kernel densities and smoothed bootstrap, that is an extension of standard bootstrap procedure, where instead of drawing samples with replacement from the empirical distribution, they are drawn from kernel density estimate of the distribution.
Three functions are provided to sample from univariate kernel
densities (ruvk), multivariate product kernel densities
(rmvk) and multivariate Gaussian kernel densities
(rmvg). The ruvk function samples from the
kernel densities as estimated using the base R density
function. It offers possibility of sampling from kernel densities with
Gaussian, Epanechnikov, rectangular, triangular, biweight, cosine, and
optcosine kernels. The rmvk offers sampling from a
multivariate kernel density constructed from independent univariate
kernel densities. It is also possible to sample from multivariate
Gaussian kernel density using the rmvg function, that
allows for correlation between the variables.
Smooth bootstrap is possible by using the kernelboot
function, that draws with replacement samples from the empirical
distribution, enhances them using noise drawn from the kernel density
and evaluates the user-provided statistic on the samples. This procedure
can be thought as an extension of the basic bootstrap procedure.