Nothing

```
# Nonparametric resampling
# x: vector of observation times in radians
# nb: number of bootstrap samples required
# smooth: if TRUE, smoothed resamples are returned
# kmax, adjust, n.grid: arguments passed to densityFit
resample <-
function(x, nb, smooth=TRUE, kmax=3, adjust=1, n.grid=512) {
n <- length(x)
if(smooth) {
bw0 <- getBandWidth(x, kmax=kmax)
if(is.na(bw0)) # if Uniroot Error
return(NA)
dA <- densityFit(x, seq(0, 2*pi, length=n.grid), bw0 / adjust)
res <- matrix(rejectSampleRad(n * nb, dA), n, nb)
} else {
res <- matrix(sample(x, n * nb, replace=TRUE), n, nb)
}
return(res)
}
rejectSampleRad <-
function(n, dA) {
# Uses rejection sampling to draw a sample from a circular density.
# Args:
# n = sample size required
# dA = a density on a regular grid from 0 to 2*pi
# Returns:
# A sample length n in [0, 2*pi)
#
uplim <- max(dA)
nx <- length(dA) - 1 # First & last entries both refer to midnight
iCand <- sample.int(nx, 2 * n, replace=TRUE) # these are INDICES
accept <- dA[iCand] > runif(2 * n, 0, uplim)
iSamp <- iCand[accept]
while(length(iSamp) < n) {
iCand <- sample.int(nx, n, replace=TRUE)
accept <- dA[iCand] > runif(n, 0, uplim)
iSamp <- c(iSamp, iCand[accept])
}
grid <- seq(0, 2*pi, length=length(dA))
return(grid[iSamp[1:n]])
}
```

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