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math/p5MathRandomISAAC,
Perl interface to the ISAAC PRNG algorithm
Branch: CURRENT,
Version: 1.004nb4,
Package name: p5MathRandomISAAC1.004nb4,
Maintainer: pkgsrcusersAs with other PseudoRandom Number Generator (PRNG) algorithms like the
Mersenne Twister (see Math::Random::MT), this algorithm is designed to
take some seed information and produce seemingly random results as output.
However, ISAAC (Indirection, Shift, Accumulate, Add, and Count) has
different goals than these commonly used algorithms. In particular, it's
really fast  on average, it requires only 18.75 machine cycles to generate
a 32bit value. This makes it suitable for applications where a significant
amount of random data needs to be produced quickly, such solving using the
Monte Carlo method or for games.
The results are uniformly distributed, unbiased, and unpredictable unless
you know the seed. The algorithm was published by Bob Jenkins in the late
90s and despite the best efforts of many security researchers, no feasible
attacks have been found to date.
Required to run:[
math/p5MathRandomISAACXS]
Required to build:[
devel/p5TestNoWarnings] [
pkgtools/cwrappers]
Master sites: (Expand)
SHA1: 0d2e423559ed28d842e6907e3944d6c5b6f2705f
RMD160: 6a77fc0f57b92e9331c233a0f59d77acae7d8fe0
Filesize: 33.638 KB
Version history: (Expand)
 (20170605) Updated to version: p5MathRandomISAAC1.004nb4
 (20160609) Updated to version: p5MathRandomISAAC1.004nb3
 (20150613) Updated to version: p5MathRandomISAAC1.004nb2
 (20140530) Updated to version: p5MathRandomISAAC1.004nb1
 (20130703) Package added to pkgsrc.se, version p5MathRandomISAAC1.004 (created)
CVS history: (Expand)
20170605 16:25:36 by Ryo ONODERA  Files touched by this commit (2298) 
Log message:
Recursive revbump from lang/perl5 5.26.0

20160608 21:25:20 by Thomas Klausner  Files touched by this commit (2236)  
Log message:
Bump PKGREVISION for perl5.24.

20151104 00:33:46 by Alistair G. Crooks  Files touched by this commit (262) 
Log message:
Add SHA512 digests for distfiles for math category
Problems found locating distfiles:
Package dfftpack: missing distfile dfftpack20001209.tar.gz
Package eispack: missing distfile eispack20001130.tar.gz
Package fftpack: missing distfile fftpack20001130.tar.gz
Package linpack: missing distfile linpack20010510.tar.gz
Package minpack: missing distfile minpack20001130.tar.gz
Package odepack: missing distfile odepack20001130.tar.gz
Package pynetworkx: missing distfile networkx1.10.tar.gz
Package pysympy: missing distfile sympy0.7.6.1.tar.gz
Package quadpack: missing distfile quadpack20001130.tar.gz
Otherwise, existing SHA1 digests verified and found to be the same on
the machine holding the existing distfiles (morden). All existing
SHA1 digests retained for now as an audit trail.

20150612 12:52:19 by Thomas Klausner  Files touched by this commit (3152) 
Log message:
Recursive PKGREVISION bump for all packages mentioning 'perl',
having a PKGNAME of p5*, or depending such a package,
for perl5.22.0.

20140530 01:38:20 by Thomas Klausner  Files touched by this commit (3049) 
Log message:
Bump for perl5.20.0.
Do it for all packages that
* mention perl, or
* have a directory name starting with p5*, or
* depend on a package starting with p5
like last time, for 5.18, where this didn't lead to complaints.
Let me know if you have any this time.

20130703 16:33:32 by Jens Rehsack  Files touched by this commit (3) 
Log message:
Adding package for CPAN distribution MathRandomISAAC version 1.004 into
math/p5MathRandomISAAC.
As with other PseudoRandom Number Generator (PRNG) algorithms like the
Mersenne Twister (see Math::Random::MT), this algorithm is designed to
take some seed information and produce seemingly random results as output.
However, ISAAC (Indirection, Shift, Accumulate, Add, and Count) has
different goals than these commonly used algorithms. In particular, it's
really fast  on average, it requires only 18.75 machine cycles to generate
a 32bit value. This makes it suitable for applications where a significant
amount of random data needs to be produced quickly, such solving using the
Monte Carlo method or for games.
The results are uniformly distributed, unbiased, and unpredictable unless
you know the seed. The algorithm was published by Bob Jenkins in the late
90s and despite the best efforts of many security researchers, no feasible
attacks have been found to date.
