```
# NOT RUN {
N <- 5000; x <- 1:N; y <- runif(N)
true.rho <- -0.8
ymat <- rbinorm(N, cov12 = true.rho) # Bivariate normal, aka N_2
x <- ymat[, 1]
y <- ymat[, 2]
# }
# NOT RUN {
plot(x, y, col = "blue")
# }
# NOT RUN {
kendall.tau(x, y) # A random sample is taken here
kendall.tau(x, y) # A random sample is taken here
kendall.tau(x, y, exact = TRUE) # Costly if length(x) is large
kendall.tau(x, y, max.n = N) # Same as exact = TRUE
(rhohat <- sin(kendall.tau(x, y) * pi / 2)) # This formula holds for N_2 actually
true.rho # rhohat should be near this value
# }
```

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