Nothing
dke.fun.default <-
function(Vec,h,type_data=c("discrete","continuous"),ker=c("BE","GA","LN","RIG"),x=NULL,a0=0,a1=1,...)
# INPUTS:
# "ker" kernel function: "GA" Gamma, "BE" extended beta,
# "LN" lognormal, RIG "reciprocal inverse Gaussian"
# "Vec" sample of data
# "h" bandwidth
# "x" single value or grid where the kernel estimation is computed
# "a0" left bound of the support
# "a1" right bound of the support
# OUTPUT:Returns a list containing:
# "C_n" the normalizing constant
# "f_n" vector containing the estimated function
# in the grid values
{
n <- length(Vec)
if(is.null(x)) {
x=seq(min(Vec),max(Vec),length.out=100)}
aux <- matrix(data=Vec,nrow=length(x),ncol=length(Vec),byrow=TRUE)
#for(i in 1:n){
# aux[i,]= kef(x[i],vec_data,bw,ker,a,b)
# }
aux <- kef(x,aux,h,"continuous",ker,a0,a1)
res<- apply(aux,1,mean) # density without normalization
C<-simp_int(x,res) # Normalizant constant
result<-res/C # density normalized
structure(list(data=Vec,n=length(Vec),hist=hist(Vec,prob=TRUE),eval.points= x,h=h, kernel=ker,C_n=C,est.fn=result),class="dke.fun")
}
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