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\name{bkfe}\alias{bkfe}\title{Compute a Binned Kernel Functional Estimate}\description{Returns an estimate of a binned approximation tothe kernel estimate of the specified density functional.The kernel is the standard normal density.}\usage{bkfe(x, drv, bandwidth, gridsize = 401L, range.x, binned = FALSE,truncate = TRUE)}\arguments{\item{x}{numeric vector of observations from the distribution whose density is tobe estimated.Missing values are not allowed.}\item{drv}{order of derivative in the density functional. Must be anon-negative even integer.}\item{bandwidth}{the kernel bandwidth smoothing parameter. Must be supplied.}\item{gridsize}{the number of equally-spaced points over which binning isperformed.}\item{range.x}{vector containing the minimum and maximum values of \code{x}at which to compute the estimate.The default is the minimum and maximum data values, extended by thesupport of the kernel.}\item{binned}{logical flag: if \code{TRUE}, then \code{x} and \code{y} are taken to be grid countsrather than raw data.}\item{truncate}{logical flag: if \code{TRUE}, data with \code{x} values outside therange specified by \code{range.x} are ignored.}}\value{the (scalar) estimated functional.}\details{The density functional of order \code{drv} is the integral of theproduct of the density and its \code{drv}th derivative.The kernel estimatesof such quantities are computed using a binned implementation,and the kernel is the standard normal density.}\section{Background}{Estimates of this type were proposed by Sheather andJones (1991).}\references{Sheather, S. J. and Jones, M. C. (1991).A reliable data-based bandwidth selection method forkernel density estimation.\emph{Journal of the Royal Statistical Society, Series B},\bold{53}, 683--690.Wand, M. P. and Jones, M. C. (1995).\emph{Kernel Smoothing.}Chapman and Hall, London.}\examples{data(geyser, package="MASS")x <- geyser$durationest <- bkfe(x, drv=4, bandwidth=0.3)}\keyword{smooth}% Converted by Sd2Rd version 0.2-a5.