demo/standardTest.R

## This follows the approach in (R)DieHarder which does
##   take N draws from the N(0,1) we test here
##   convert into U(0,1) by using the inverse of the normal
##   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)

library(RcppZiggurat)

stdres <- RcppZiggurat:::standardTest(N=1e5,      		# individual draws
                                      M=1e2,  		# repeats pre draw
                                      seed=123456789,
                                      generators=c("Ziggurat", "MT", "LZLLV", "GSL", "V1", "QL"),
                                      showplot=interactive())

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RcppZiggurat documentation built on Sept. 28, 2017, 1:02 a.m.