Nothing

```
## This modifies the approach in (R)DieHarder which does
## take N draws from a U(0,1)
## repeat M times
## and for large enough N, then the sum of all N draws goes to
## mean --> N/2
## stddev --> sqrt(N/12)
## which is known as the Irwin-Hall distribution
## then for each of these M values use the inverse of normal to obtain a p-value
## that p value should be uniformly distributed across these M draws
## so use Kuiper's K/S test variant to test for uniform U(0,1)
##
## Here we don't need Irwin-Hall: the sum of N vars drawn as N(0,1) will be N(0,sqrt(N))
## So we compute a p value from that and assemple M such p values
normalTest <- function(N=1e5, # individual draws
M=1e2, # repeats
seed=123456789,
generators=c("Ziggurat", "MT", "LZLLV",
"GSL", "QL", "Gretl"),
showplot=interactive()) {
res <- mclapply(generators, FUN=function(g, seed) {
res <- ziggsum(M, N, g, seed)
v <- pnorm(res, sd=sqrt(N))
}, seed, mc.cores=getOption("mc.cores", 2L))
names(res) <- generators
res <- as.data.frame(res)
attr(res, "testtype") <- "Normal"
attr(res, "draws") <- N
attr(res, "repeats") <- M
attr(res, "seed") <- seed
attr(res, "created") <- format(Sys.time())
attr(res, "version") <- packageVersion("RcppZiggurat")
if (showplot) {
plotAll(res)
}
invisible(res)
}
```

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