useful in statistics. Our focus is on high performance, numerical
robustness, and use of good algorithms. Where possible, we provide
references to the statistical literature.
WWW: http://darcs.serpentine.com/statistics
PR: ports/142294
Submitted by: Jacula Modyun <jacula(at)gmail.com>
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.
WWW: http://darcs.serpentine.com/mwc-random
PR: ports/142293
Submitted by: Jacula Modyun <jacula(at)gmail.com>
in terms of their distributions by using a monad.
The monad is similar to the List monad for non-deterministic computations,
but extends the List monad by a measure of probability.
Small interface to R plotting.
WWW: http://haskell.org/haskellwiki/Probabilistic_Functional_Programming
PR: ports/142292
Submitted by: Jacula Modyun <jacula(at)gmail.com>
compiling Agda programs. The program can also generate hyperlinked,
highlighted HTML files from Agda sources.
WWW: http://wiki.portal.chalmers.se/agda/
PR: ports/142141
Submitted by: Jacula Modyun <jacula(at)gmail.com>
families, which are similar to Haskell's GADTs, but they can be indexed by
values and not just types. It also has parameterised modules, mixfix operators,
Unicode characters, and an interactive Emacs interface (the type checker can
assist in the development of your code).
Agda is also a proof assistant: It is an interactive system for writing and
checking proofs. Agda is based on intuitionistic type theory, a foundational
system for constructive mathematics developed by the Swedish logician Per
Martin-Lof. It has many similarities with other proof assistants based on
dependent types, such as Coq, Epigram and NuPRL.
WWW: http://wiki.portal.chalmers.se/agda/
PR: ports/142141
Submitted by: Jacula Modyun <jacula(at)gmail.com>
ideas from category theory. Notable bits include: comonads,
adjunctions, and various recursion schemes a'la the research paper
"Functional Programming with Bananas, Lenses, Envelopes and
Barbed Wire".
WWW: http://comonad.com/reader/
PR: ports/142142
Submitted by: Jacula Modyun <jacula(at)gmail.com>
- Teach about amd64, to detect Not a Number flag, thus enabling Qhull
support with CSA;
- Disable detection of f77 by cmake unless WITH_FORTRAN is defined.
Reported by: pointyhat via Pav
Notes:
- support of Gnome2 and VGAlib are now deprecated;
- support of Qt added;
- plrender is also deprecated, but we force it to build.
Many more options could be enabled, if someone needs them.
- Bug fixes:
. acosh, asinh, atanh: swap of precisions between real and imaginary parts
. atan: memory leak
. log: wrong ternary value in data file; masked by bug in Mpfr 2.4.1
PR: 141825
Submitted by: bf1783@gmail.com
e-mail addresses from the pkg-descr file that could reasonably
be mistaken for maintainer contact information in order to avoid
confusion on the part of users looking for support. As a pleasant
side effect this also avoids confusion and/or frustration for people
who are no longer maintaining those ports.
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 32-bit 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.
WWW: http://search.cpan.org/dist/Math-Random-ISAAC-XS/
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 32-bit 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.
WWW: http://search.cpan.org/dist/Math-Random-ISAAC/
