statistics

A statistical library to perform descriptive statistics and generate random values based on popular probability distributions.

Installation

Add the dependency to your shard.yml:

dependencies:
  statistics:
    github: lbarasti/statistics

Run shards install

Usage

require "statistics"

Descriptive statistics

You can compute mean, variance and standard deviation of a collection as follows.

include Statistics

x = [1, 10, 7]
mean(x) # 6
var(x) # 14
std(x) # 3.7416...

If you'd like to know a bit more about your dataset, you can simply describe it

x = (1..1000).map { rand }.to_a # a uniformely distributed dataset
describe(x)
# {
#   mean: 0.48, var: 0.08, std: 0.28, 
#   skewness: 0.04, kurtosis: 1.81, 
#   min: 0.01, middle: 0.49, max: 0.99, 
#   q1: 0.24, median: 0.49, q3: 0.73
# }

Statistics.describe returns a NamedTuple, so you can extract any value via indexing:

stats = describe(x)
stats[:q1] # returns the first quartile of your sample

For a complete list of the statistical functions provided, including quantile, moment and skew, check out the docs.

Sampling

To work with distributions, import the Distributions namespace as follows.

include Statistics::Distributions

Now, here is how we sample values from a normal distribution with mean = 1.5 and std = 0.2.

Normal.new(1.5, 0.2).rand

We can generate an iterable of normally distributed random values as follows.

gen = Normal.new(1.5, 0.2)
1000.times.map { gen.rand }

Supported distributions

The following distributions are supported:

Constant
Exponential
Normal
Poisson
Uniform

Don't see your favourite one on the list? Just fork the repo, add your distribution to the distributions.cr file, and open a PR.

Development

This shard is a work in progress. Everyone's contribution is welcome.

The guiding principle at this stage is

make it work before you make it right

Which in this context means: let's not focus on benchmarks and performance, but rather on usability and correctness.

References

numpy.random: distributions and random sampling
numpy statistics: order statistics, averages and variances
scipy stats module and related tests tests
julia random module
julia statistics module
julia distributions package.
on skewness and kurtosis, by Stan Brown
more on skewness and kurtosis, from NIST.

Contributing

Fork it (<https://github.com/lbarasti/statistics/fork>)
Create your feature branch (git checkout -b my-new-feature)
Commit your changes (git commit -am 'Add some feature')
Push to the branch (git push origin my-new-feature)
Create a new Pull Request

Contributors

lbarasti - creator and maintainer