The Serial test for testing random number generators.

1 | ```
serial.test(u , d = 8, echo = TRUE)
``` |

`u` |
sample of random numbers in ]0,1[. |

`echo` |
logical to plot detailed results, default |

`d` |
a numeric for the dimension, see details. When necessary
we assume that |

We consider a vector `u`

, realisation of i.i.d. uniform random
variables *U1... Un*.

The serial test computes a serie of integer pairs *(p_i,p_{i+1})*
from the sample `u`

with *
p_i = floor(u_i d)* (`u`

must have an even length).
Let *n_j* be the number of pairs such that
*j=p_i d + p_{i+1}*. If `d=2`

, we count
the number of pairs equals to *00, 01, 10* and *11*. Since
all the combination of two elements in *{0, ..., d-1}*
are equiprobable, the chi-squared statistic is

*
S = ∑_{j=0}^{d-1} [n_j - n/(2 d^2)]^2/[n/(2 d^2)].
*

a list with the following components :

`statistic`

the value of the chi-squared statistic.

`p.value`

the p-value of the test.

`observed`

the observed counts.

`expected`

the expected counts under the null hypothesis.

`residuals`

the Pearson residuals, (observed - expected) / sqrt(expected).

Christophe Dutang.

Planchet F., Jacquemin J. (2003), *L'utilisation de methodes de
simulation en assurance*. Bulletin Francais d'Actuariat, vol. 6, 11, 3-69. (available online)

L'Ecuyer P. (2001), *Software for uniform random number
generation distinguishing the good and the bad*. Proceedings of the 2001
Winter Simulation Conference. (available online)

L'Ecuyer P. (2007), *Test U01: a C library for empirical testing of
random number generators.* ACM Trans. on Mathematical
Software 33(4), 22.

other tests of this package `freq.test`

, `gap.test`

, `poker.test`

,
`order.test`

and `coll.test`

`ks.test`

for the Kolmogorov Smirnov test and `acf`

for
the autocorrelation function.

1 2 3 4 5 6 7 8 | ```
# (1)
#
serial.test(runif(1000))
print( serial.test( runif(1000000), d=2, e=FALSE) )
# (2)
#
serial.test(runif(5000), 5)
``` |

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