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#' Continuous Effectiveness as Truncated Gaussian Kernel-smoothed Distribution.
#'
#' Fits a kernel-smoothed distribution to the given sample of scores, truncated between 0 and 1, and
#' using a gaussian kernel.
#'
#' @param x a sample of effectiveness scores between 0 and 1.
#' @return an object of class \code{eff.cont.nks}, which inherits from
#' \code{\link[=eff.cont-class]{eff.cont}}.
#' @seealso \code{\link{deff}}, \code{\link{peff}}, \code{\link{qeff}} and \code{\link{reff}}.
#' @examples
#' e <- effCont_nks(web2010ap[,1])
#' c(e$mean, e$var)
#' plot(e, plot.data = TRUE)
#' @export
effCont_nks <- function(x) {
# estimate
k <- ks::kde(x)
tk <- effContTrunc(ks::dkde, ks::pkde, ks::qkde, fhat = k)
df <- mean(stats::dnorm(0, sd = k$h) / ks::dkde(x, fhat = k))
E <- effContMean(tk$q) # expected value
Var <- effContVar(tk$q, E) # variance
# prepare eff object and return
e <- effCont_new(E, Var, df, x)
e$model <- list(type = "nks", kde = k, d = tk$d, p = tk$p, q = tk$q)
class(e) <- c("eff.cont.nks", class(e))
e
}
#' @export
deff.eff.cont.nks <- function(x, .eff) {
.eff$model$d(x)
}
#' @export
peff.eff.cont.nks <- function(q, .eff) {
.eff$model$p(q)
}
#' @export
qeff.eff.cont.nks <- function(p, .eff) {
.eff$model$q(p)
}
#' @export
reff.eff.cont.nks <- function(n, .eff) {
r <- stats::runif(n)
.eff$model$q(r)
}
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