Random number generation based on modeling random
variables in two complementary ways: first, by the
parameters of standard mathematical distributions and,
second, by an abstract type (RVar) which can be
composed and manipulated monadically and sampled in
either monadic or "pure" styles.

The primary purpose of this library is to support
defining and sampling a wide variety of high quality
random variables. Quality is prioritized over speed,
but performance is an important goal too.

In my testing, I have found it capable of speed
comparable to other Haskell libraries, but still
a fair bit slower than straight C implementations of
the same algorithms.

Changes

Changes in 0.2.4.0: Added a Lift instance that resolves a common
overlapping-instance issue in user code.

Changes in 0.2.3.1: Should now build on GHC 7.6

Changes in 0.2.3.0: Added stretched exponential distribution,
contributed by Ben Gamari.

Changes in 0.2.2.0: Bug fixes in
Data.Random.Distribution.Categorical.

Changes in 0.2.1.1: Changed some one-field data types to newtypes,
updated types for GHC 7.4’s removal of Eq and Show from the context
of Num, and added RVarT versions of random variables in
Data.Random.List

Changes in 2.6.1: now supports probability density functions and log
probability density functions via the PDF class, similar to R and
initially just for the Beta, Binomial, Normal and Uniform
distributions. The log Binomial probability density function uses
Fast and Accurate Computation of Binomial Probabilities by
Catherine Loader (this is what is implemented in R and Octave) to
minimize the occurrence of underflow.