Nothing

```
`poolaccum` <-
function(x, permutations = 100, minsize = 3)
{
x <- as.matrix(x)
n <- nrow(x)
m <- ncol(x)
N <- seq_len(n)
## specpool() is slow, but the vectorized versions below are
## pretty fast. We do not set up parallel processing, but use
## permute API.
permat <- getPermuteMatrix(permutations, n)
nperm <- nrow(permat)
S <- chao <- boot <- jack1 <- jack2 <-
matrix(0, nrow=n, ncol=nperm)
for (i in 1:nperm) {
## It is a bad practice to replicate specpool equations here:
## if we change specpool, this function gets out of sync. You
## should be ashamed, Jari Oksanen!
take <- permat[i,]
tmp <- apply(x[take,] > 0, 2, cumsum)
S[,i] <- rowSums(tmp > 0)
## All-zero species are taken as *known* to be missing in
## subsamples, and in the following we subtract them (as
## 2*S-m) from the bootstrap samples to give a more unbiased
## estimate.
boot[,i] <- 2*S[,i] - m + rowSums(exp(sweep(log1p(-sweep(tmp, 1, N, "/")), 1, N, "*") ))
a1 <- rowSums(tmp == 1)
a2 <- rowSums(tmp == 2)
chao[, i] <- S[,i] + ifelse(a2 > 0, (N-1)/N*a1*a1/2/a2,
(N-1)/N*a1*(a1-1)/2)
jack1[,i] <- S[,i] + a1 * (N-1)/N
jack2[,i] <- S[,i] + a1*(2*N-3)/N - a2*(N-2)^2/N/(N-1)
}
means <- cbind(`N` = N, `S` = rowMeans(S), `Chao` = rowMeans(chao),
`Jackknife 1` = rowMeans(jack1),
`Jackknife 2` = rowMeans(jack2),
`Bootstrap` = rowMeans(boot))
take <- N >= minsize
out <- list(S = S[take,], chao = chao[take,], jack1 = jack1[take,],
jack2 = jack2[take,], boot = boot[take,], N = N[take],
means = means[take,])
attr(out, "control") <- attr(permat, "control")
class(out) <- "poolaccum"
out
}
```

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