##########################################################################
#Compute the hazard function from a possibly right censored sample.
#Using the method of estimating the sub-density f(t)[1-G(t)] (by kernal
#function density() ); then devided by the estimate of [1-F(t)][1-G(t)] use 
#linearized empirical distribution (approx).  Written by Mai Zhou, April 1999
#modified 3/2001
###########################################################################

hazard <- function(times,status,...){ 
       samplesize <- length(times)
       if(length(status) != samplesize) 
                       stop("time and status must be of same length")
       if(any((status!=0)&(status!=1))) 
                        stop("status must be 0/1's for censor/not-censor")
       xd1 <- times[status==1] 
       if(length(xd1)<2) stop("too few uncensored observations")

       fit2 <- density(xd1,...)  
       fit2$y <- fit2$y*length(xd1)/samplesize

       fit1 <- approx(x=sort(times), y=(samplesize:1)/samplesize,  
                       xout=fit2$x, rule=1, f=0.5) 

#####       list(x=fit2$x,y=fit2$y/fit1$y) 
       fit2$y<-fit2$y/fit1$y 
       return(fit2)
}