# from ch. 9
sim.samp<-function(nrep,n,DIST,...){
# simulates nrep samples from DIST of size n
data <- matrix(DIST(n * nrep, ...), ncol = n, nrow = nrep)
}
set.seed(346)                   # sets the random number seed
sim.samp(1000,15,rnorm)->z      # 1000 N(0,1) samples, n=15
apply(z,1,mean)->out.m          # mean for each sample
trim20<-function(x){mean(x,.2)} # 20% trimmed mean function
apply(z,1,trim20)->out.t20      # trim20 for each sample
apply(z,1,median)->out.med      # median for each sample
# Save all 1000 blocks of 3 estimators in a data frame
data.frame(mean=out.m,trim20=out.t20,median=out.med)->norm15

# from ch. 10
jack.var<-function(x, theta, ...){
        call <- match.call()
        n <- length(x)
        u <- rep(0, n)
        for(i in 1:n) {
                u[i] <- theta(x[ - i], ...)
        }
        jack.var <- ((n - 1)/n) * sum((u - mean(u))^2)
        return(jack.var)
}

jack.se<-function(x, theta, ...){
        call <- match.call()
        n <- length(x)
        u <- rep(0, n)
        for(i in 1:n) {
                u[i] <- theta(x[ - i], ...)
        }
        jack.se <- sqrt(((n - 1)/n) * sum((u - mean(u))^2))
        return(jack.se)
}