Changes in 0.15.0.1
* Bug in generation of Int/Word in both uniform and uniformR is
fixed. (#75)
Changes in 0.15.0.0
* withSystemRandomST and createSystemSeed are added.
* withSystemRandom is deprecated.
* random>=1.2 is dependency of mwc-random.
* Instances for type classes StatefulGen & FrozenGen defined in
random-1.2 are added for Gen.
* Functions in System.Random.MWC.Distributions and
System.Random.MWC.CondensedTable now work with arbitrary StatefulGen
* System.Random.MWC.uniformVector now works with arbitrary StatefulGen
as well and uses in-place initialization instead of generateM. It
should be faster for anything but IO and ST (those shoud remain
same).
Changes in 0.14.0.0
* Low level functions for acquiring random data for initialization of
PRGN state is moved to System.Random.MWC.SeedSource module
* Ensure that carry is always correct when restoring PRNG state from
seed. Only affects users who create 258 element seed manually. (#63,
#65)
Changes in 0.13.6.0
* tablePoisson now can handle λ>1923, see #59 for details. That
required intoduction of dependency on math-functions.
Changes in 0.13.5.0
* logCategorical added
Changes in 0.13.4.0
* withSystemRandom uses RtlGenRandom for seeding generator on windows
ChangeLog:
Changes in 0.13.3.1
* primitive-0.6 compatibility
Changes in 0.13.3.0
* Monadic variant of vector shuffle added: `uniformShuffleM`
* Context on `uniformShuffle` loosened
Changes in 0.13.2.2
* Fixed crash during gen. initialization on Windows when stderr
is not available (#36).
packaged for wip.
This package contains code for generating high quality random numbers that
follow either a uniform or normal distribution. The generated numbers are
suitable for use in statistical applications.
The uniform PRNG uses Marsaglia's MWC256 (also known as MWC8222)
multiply-with-carry generator, which has a period of 2^8222 and fares well
in tests of randomness. It is also extremely fast, between 2 and 3 times
faster than the Mersenne Twister.
Compared to the mersenne-random package, this package has a more convenient
API, is faster, and supports more statistical distributions.
