NeighCoeff <- function(y, wavelet = "s8", shrink.rule = "soft", j0 = NULL, extension = NULL)
{
	n <- length(y)
	J <- ceiling(log(n, 2))
	if(is.null(j0))
		j0 <- ceiling(log(log(2^J), 2)) + 1
	j0 <- max(j0, 2)	#Use MAD to estimate the noise level of the signal. 
	noise.level <- median(abs(diff(y) - median(diff(y))))/(sqrt(2) * 0.6745
		)
	thresh <- 2 * log(n) * noise.level^2
	nlevels <- J - j0
	add <- 2^J - n
	if(add > 0) {
add.left_floor(add/2); add.right_add-add.left
		if(is.null(extension))
			extension <- "reflection"
		if(extension == "reflection"){
                if(add.left==0)
y <- c(y, y[n:(n - add.right + 1)])
                 else
			y <- c(y[add.left:1], y, y[n:(n - add.right + 1)])
}
                 else if(extension == "periodic"){
if(add.left==0)
y <- c(y, y[1:add.right])
                 else
			y <- c(y[(n-add.right+1):n], y, y[1:add.left])
}
	else if(extension == "zero"){
add.left_0; add.right_2^J - n
y <- c(y, rep(0, add.right))
}	
		else stop("Available extension rule: periodic, reflection, zero")
	}
	ydwt <- dwt(y, wavelet, n.levels = nlevels)
	for(level in 1:nlevels) {
		y <- as.vector(ydwt[[paste("d", level, sep = "")]])
		m <- length(y)
		yy <- c(y[m], y, y[1])
		SS <- rep(0, m)
		for(b in 1:m) {
			SS[b] <- sum((yy[b:(b + 2)])^2)
		}
		if(shrink.rule == "soft") {
			ydwt[[paste("d", level, sep = "")]] <- y * pmax(0, 1 - 
				thresh/SS)
		}
		else if(shrink.rule == "hard") {
			y[SS <= thresh] <- 0
			ydwt[[paste("d", level, sep = "")]] <- y
		}
		else stop("Available shrink.rule: soft, hard")
	}
	if(2^J >n)
	output_reconstruct(ydwt)[(add.left+1):(add.left+n)]
else
output_reconstruct(ydwt)
output
}