"get.at.surv.times"<-
function(surv.cens, times)
{
#
# surv.cens is an object created by survfit
# times is a vector of times at which you want 
# an estimate of the survival function
#
	nt <- length(times)
	outs <- rep(0, nt)
	survv <- surv.cens$surv
	ns <- length(survv)
	timev <- surv.cens$time
	for(i in 1:nt) {
		if(times[i] < timev[1]) {
			outs[i] <- 1
		}
		else if(times[i] >= timev[ns]) {
			outs[i] <- survv[ns]
		}
		else {
			outs[i] <- survv[timev == max(timev[timev <= times[i]])
				][1]
		}
	}
	no <- length(outs[outs == 0])
	outs[outs == 0] <- rep(survv[ns - 1], no)
	return(outs)
}

"get.init.am"<-
function(surv.cens, coeff.cox, w, delta, ttilde)
{
        w <- ifelse(is.na(w),0,w)
        if(!is.matrix(w)){
          w <- matrix(w,nrow=length(w),ncol=1)
        }
	nn <- length(ttilde)
        coeff.cox <- matrix(coeff.cox,nrow=length(coeff.cox),ncol=1)
        coeff.cox[is.na(coeff.cox)] <- 0
	linpred <- w %*%coeff.cox
 
	sum.surv.cond <- surv.cens^(exp(linpred))
        sum.surv.cond <- ifelse(sum.surv.cond<.2,.2,sum.surv.cond)
        if(is.na(min(sum.surv.cond))){
          if(delta[is.na(sum.surv.cond)]==0){
          sum.surv.cond[is.na(sum.surv.cond)] <- 1
          } 
        }
	B <- (delta)/sum.surv.cond
	return(B)
}