Distribution is a gem with several probabilistic distributions. Pure Ruby is used by default, C (GSL) or Java extensions are used if available. Some facts:
- Very fast ruby 1.9.3+ implementation, with improved method to calculate factorials and other common functions.
- All methods tested on several ranges. See
- Code for normal, Student’s t and chi square is lifted from the statistics2 gem. Originally at this site.
- The code for some functions and RNGs was lifted from Julia’s Rmath-julia, a patched version of R’s standalone math library.
The following table lists the available distributions and the methods available for each one. If a field is marked with an x, that distribution doesn’t have that method implemented.
$ gem install distribution
You can install GSL for better performance:
- For Mac OS X:
brew install gsl
- For Ubuntu / Debian:
sudo apt-get install libgsl0-dev
After successfully installing the library:
$ gem install rb-gsl
You can find automatically generated documentation on RubyDoc.
``` # Returns Gaussian PDF for x. pdf = Distribution::Normal.pdf(x)
Returns Gaussian CDF for x.
cdf = Distribution::Normal.cdf(x)
Returns inverse CDF (or p-value) for x.
pv = Distribution::Normal.p_value(x)
You would normally use the following
p = Distribution::T.cdf(x)
to get the cumulative probability of
x. However, you can also:
include Distribution::Shorthand tdist_cdf(x) ```
On discrete distributions, exact Ruby implementations of pdf, cdf and p_value could be provided, using
module Distribution::Shorthand provides (you guess?) shortands method to call all methods
On discrete distributions, exact cdf, pdf and p_value are
Shortnames for distributions:
- Normal: norm
- Bivariate Normal: bnor
- T: tdist
- F: fdist
- Chi Square: chisq
- Binomial: bino
- Hypergeometric: hypg
- Exponential: expo
- Poisson: pois
- Beta: beta
- Gamma: gamma
- LogNormal: lognormal
- Uniform: unif
This gem wasn’t updated for a long time before I started working on it, so there are a lot of work to do. The first priority is cleaning the interface and removing cruft whenever possible. After that, I want to implement more distributions and make sure that each one has a RNG.
- Define a minimal interface for continuous and discrete distributions (e.g. mean, variance, mode, skewness, kurtosis, pdf, cdf, quantile, cquantile).
Distribution::Uniformwith the default Ruby
- Clean up the implementation of normal distribution. Implement the necessary functions.
- The same for Student’s t, chi square, Fisher-Snedecor, beta, gamma, lognormal, logistic.
- The same for discrete distributions: binomial, hypergeometric, bernoulli (still missing), etc.
- Implement DSFMT for the uniform random generator.
- Cauchy distribution.
- Implementing everything in the distributions x functions table above.
- On JRuby and Rubinius, BivariateNormal returns incorrect pdf
For current issues see the issue tracker pages.
OMG! I want to help!
Everyone is welcome to help! Please, test these distributions with your own use cases and give a shout on the issue tracker if you find a problem or something is strange or hard to use. Documentation pull requests are totally welcome. More generally, any ideas or suggestions are welcome – even by private e-mail.
If you want to provide a new distribution, run
$ distribution --new your_distribution
This should create the main distribution file, the directory with Ruby and GSL engines and specs on the spec/ directory.