minfconf=function(x,y){

#this will spit out the quantiles of the MI from the bootstrap..

library(sm)

air3  <- cbind(x,y)
result.12 <- sm.density(air3,   eval.points = air3,   display = "none")
result.1  <- sm.density(x,  eval.points = x,  display = "none")
result.2  <- sm.density(y, eval.points = y, display = "none")
tobs <- mean(log(result.12$estimate / 
  			(result.1$estimate * result.2$estimate)))

print(c(tobs))


n=length(x)
nsim   <- 500
#500 bootstrap samples..

simval <- rep(0,nsim)
for (i in 1:nsim) {
xx=1:n
xy=sample(xx,replace=TRUE)
   samp      <- cbind(y1 = x, y2 = y[xy])	
#   samp      <- cbind(y1 = x[xy], y2 = y[xy])	
   result.12 <- sm.density(samp,     eval.points = samp,     display = "none")
   result.1  <- sm.density(samp[,1], eval.points = samp[,1], display = "none")
   result.2  <- sm.density(samp[,2], eval.points = samp[,2], display = "none")
   simval[i] <- mean(log(result.12$estimate / 
  			(result.1$estimate * result.2$estimate)))
   }

quantile(simval,c(0.05,0.1,.50,.90,.95))

}