# RDieHarder 0.1.2

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The package still comes with a vignette describing both DieHarder and the RDieHarder package. And because pictures speak louder than a thousand (blogged) words, here is the first chart from the vignette:

`ran0`

function. The histogram illustrating the distribution of
test scores is somewhat uneven. An ideal (and asymptotic) outcode is a
uniform distribution of p-values from the test. The empirical cumulative
distribution function (ECDF) below indicates a somewhat pronounced departure from
the diagonal. Informally speaking, this is what the
(Kuiper-)Kolmogorov-Smirnov test quantifies, and we see (in the text in the
chart) that the null of can be rejected an conventional levels. Based on
this example (which had a short run-time with few samples) we would indeed mistrust
this (known bad) RNG.
On the right, we have a more recent and trusted RNG, the well-known Mersenne Twister. The ten histogram buckets are all closer to the expected value of one-tenth, the estimated density is closer to flat, the ECDF is closer to the diagonal and the tests don’t reject—so no reason to mistrust this RNG based on this test alone.

RDieHarder lets you run a battery of such tests against a boatload of known RNGs. Here is a second example, comparing the six RNGs built into R itself:

Courtesy of CRANberries, there is also a diffstat report for 0.1.2 relative to the older 0.1.1 release. More detailed information is on the RDieHarder page.

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