diff --git a/ruby-statistics/.gitignore b/ruby-statistics/.gitignore
deleted file mode 100644
index 73540b9aab..0000000000
--- a/ruby-statistics/.gitignore
+++ /dev/null
@@ -1,65 +0,0 @@
-*.gem
-*.rbc
-/.config
-/coverage/
-/InstalledFiles
-/pkg/
-/spec/reports/
-/spec/examples.txt
-/test/tmp/
-/test/version_tmp/
-/tmp/
-
-# Used by dotenv library to load environment variables.
-# .env
-
-## Specific to RubyMotion:
-.dat*
-.repl_history
-build/
-*.bridgesupport
-build-iPhoneOS/
-build-iPhoneSimulator/
-
-## Specific to RubyMotion (use of CocoaPods):
-#
-# We recommend against adding the Pods directory to your .gitignore. However
-# you should judge for yourself, the pros and cons are mentioned at:
-# https://guides.cocoapods.org/using/using-cocoapods.html#should-i-check-the-pods-directory-into-source-control
-#
-# vendor/Pods/
-
-## Documentation cache and generated files:
-/.yardoc/
-/_yardoc/
-/doc/
-/rdoc/
-
-## Environment normalization:
-/.bundle/
-/vendor/bundle
-/lib/bundler/man/
-
-# for a library or gem, you might want to ignore these files since the code is
-# intended to run in multiple environments; otherwise, check them in:
-# Gemfile.lock
-.ruby-version
-.ruby-gemset
-
-# unless supporting rvm < 1.11.0 or doing something fancy, ignore this:
-.rvmrc
-/.bundle/
-/.yardoc
-/Gemfile.lock
-/_yardoc/
-/coverage/
-/doc/
-/pkg/
-/spec/reports/
-/tmp/
-
-# rspec failure tracking
-.rspec_status
-
-# byebug
-.byebug_history
diff --git a/ruby-statistics/.rspec b/ruby-statistics/.rspec
deleted file mode 100644
index 8c18f1abdd..0000000000
--- a/ruby-statistics/.rspec
+++ /dev/null
@@ -1,2 +0,0 @@
---format documentation
---color
diff --git a/ruby-statistics/.travis.yml b/ruby-statistics/.travis.yml
deleted file mode 100644
index 606201e265..0000000000
--- a/ruby-statistics/.travis.yml
+++ /dev/null
@@ -1,8 +0,0 @@
-sudo: false
-language: ruby
-rvm:
-  - 2.3.7
-  - 2.4.4
-  - 2.5.1
-  - 2.6.0
-before_install: gem update --system && gem install bundler
diff --git a/ruby-statistics/CODE_OF_CONDUCT.md b/ruby-statistics/CODE_OF_CONDUCT.md
deleted file mode 100644
index 9889d44c59..0000000000
--- a/ruby-statistics/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to making participation in our project and
-our community a harassment-free experience for everyone, regardless of age, body
-size, disability, ethnicity, gender identity and expression, level of experience,
-nationality, personal appearance, race, religion, or sexual identity and
-orientation.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment
-include:
-
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
-
-Examples of unacceptable behavior by participants include:
-
-* The use of sexualized language or imagery and unwelcome sexual attention or
-advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
-  address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned to this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies both within project spaces and in public spaces
-when an individual is representing the project or its community. Examples of
-representing a project or community include using an official project e-mail
-address, posting via an official social media account, or acting as an appointed
-representative at an online or offline event. Representation of a project may be
-further defined and clarified by project maintainers.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the project team at ezapata@altavistaed.com. All
-complaints will be reviewed and investigated and will result in a response that
-is deemed necessary and appropriate to the circumstances. The project team is
-obligated to maintain confidentiality with regard to the reporter of an incident.
-Further details of specific enforcement policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at [http://contributor-covenant.org/version/1/4][version]
-
-[homepage]: http://contributor-covenant.org
-[version]: http://contributor-covenant.org/version/1/4/
diff --git a/ruby-statistics/CONTRIBUTING.md b/ruby-statistics/CONTRIBUTING.md
deleted file mode 100644
index 7315d24786..0000000000
--- a/ruby-statistics/CONTRIBUTING.md
+++ /dev/null
@@ -1 +0,0 @@
-Bug reports and pull requests are welcome on GitHub at https://github.com/estebanz01/ruby-statistics. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the [Contributor Covenant code of conduct](https://www.contributor-covenant.org/).
diff --git a/ruby-statistics/Gemfile b/ruby-statistics/Gemfile
deleted file mode 100644
index d006398a9e..0000000000
--- a/ruby-statistics/Gemfile
+++ /dev/null
@@ -1,6 +0,0 @@
-source "https://rubygems.org"
-
-git_source(:github) {|repo_name| "https://github.com/#{repo_name}" }
-
-# Specify your gem's dependencies in statistics.gemspec
-gemspec
diff --git a/ruby-statistics/LICENSE b/ruby-statistics/LICENSE
deleted file mode 100644
index d58962a2a8..0000000000
--- a/ruby-statistics/LICENSE
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2017 Esteban Zapata Rojas
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/ruby-statistics/LICENSE.txt b/ruby-statistics/LICENSE.txt
deleted file mode 100644
index b2d0a42d7f..0000000000
--- a/ruby-statistics/LICENSE.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-The MIT License (MIT)
-
-Copyright (c) 2017 esteban zapata
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
diff --git a/ruby-statistics/README.md b/ruby-statistics/README.md
deleted file mode 100644
index b85f775e03..0000000000
--- a/ruby-statistics/README.md
+++ /dev/null
@@ -1,79 +0,0 @@
-# Ruby Statistics
-
-![](https://travis-ci.org/estebanz01/ruby-statistics.svg?branch=master)
-
-A basic ruby gem that implements some statistical methods, functions and concepts to be used in any ruby environment without depending on any mathematical software like `R`, `Matlab`, `Octave` or similar.
-
-Unit test runs under the following ruby versions:
-* Ruby 2.2.
-* Ruby 2.3.1.
-* Ruby 2.4.0.
-* Ruby 2.5.0.
-
-We got the inspiration from the folks at [JStat](https://github.com/jstat/jstat) and some interesting lectures about [Keystroke dynamics](http://www.biometric-solutions.com/keystroke-dynamics.html).
-
-Some logic and algorithms are extractions or adaptations from other authors, which are referenced in the comments.
-This software is released under the MIT License.
-
-## Installation
-
-Add this line to your application's Gemfile:
-
-```ruby
-gem 'ruby-statistics'
-```
-
-And then execute:
-
-    $ bundle
-
-Or install it yourself as:
-
-    $ gem install ruby-statistics
-
-## Basic Usage
-
-just require the `statistics` gem in order to load it. If you don't have defined the `Distribution` namespace, the gem will assign an alias, reducing the number of namespaces needed to use a class.
-
-Right now you can load:
-
-* The whole statistics gem. `require 'statistics'`
-* A namespace. `require 'statistics/distribution'`
-* A class. `require 'statistics/distribution/normal'`
-
-Feel free to use the one that is more convenient to you.
-
-### Hello-World Example
-```ruby
-require 'statistics'
-
-poisson = Distribution::Poisson.new(l) # Using Distribution alias.
-normal = Statistics::Distribution::StandardNormal.new # Using all namespaces.
-```
-
-## Documentation
-You can find a bit more detailed documentation of all available distributions, tests and functions in the [Documentation Index](https://github.com/estebanz01/ruby-statistics/wiki)
-
-## Development
-
-After checking out the repo, run `bin/setup` to install dependencies. Then, run `rake spec` to run the tests. You can also run `bin/console` for an interactive prompt that will allow you to experiment.
-
-To install this gem onto your local machine, run `bundle exec rake install`. To release a new version, update the version number in `version.rb`, and then run `bundle exec rake release`, which will create a git tag for the version, push git commits and tags, and push the `.gem` file to [rubygems.org](https://rubygems.org).
-
-## Contributing
-
-Bug reports and pull requests are welcome on GitHub at https://github.com/estebanz01/ruby-statistics. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the [Contributor Covenant](http://contributor-covenant.org) code of conduct.
-
-## License
-
-The gem is available as open source under the terms of the [MIT License](http://opensource.org/licenses/MIT).
-
-## Code of Conduct
-
-Everyone interacting in the Statistics project’s codebases, issue trackers, chat rooms and mailing lists is expected to follow the [code of conduct](https://github.com/estebanz01/ruby-statistics/blob/master/CODE_OF_CONDUCT.md).
-
-## Contact
-
-You can contact me via:
-* [Github](https://github.com/estebanz01)
-* [Twitter](https://twitter.com/estebanz01)
diff --git a/ruby-statistics/Rakefile b/ruby-statistics/Rakefile
deleted file mode 100644
index b7e9ed549b..0000000000
--- a/ruby-statistics/Rakefile
+++ /dev/null
@@ -1,6 +0,0 @@
-require "bundler/gem_tasks"
-require "rspec/core/rake_task"
-
-RSpec::Core::RakeTask.new(:spec)
-
-task :default => :spec
diff --git a/ruby-statistics/bin/console b/ruby-statistics/bin/console
deleted file mode 100755
index 35213b9e9e..0000000000
--- a/ruby-statistics/bin/console
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/usr/bin/env ruby
-
-require "bundler/setup"
-require "statistics"
-
-# You can add fixtures and/or initialization code here to make experimenting
-# with your gem easier. You can also use a different console, if you like.
-
-# (If you use this, don't forget to add pry to your Gemfile!)
-# require "pry"
-# Pry.start
-
-require "irb"
-IRB.start(__FILE__)
diff --git a/ruby-statistics/bin/setup b/ruby-statistics/bin/setup
deleted file mode 100755
index dce67d860a..0000000000
--- a/ruby-statistics/bin/setup
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-IFS=$'\n\t'
-set -vx
-
-bundle install
-
-# Do any other automated setup that you need to do here
diff --git a/ruby-statistics/lib/enumerable.rb b/ruby-statistics/lib/enumerable.rb
deleted file mode 100644
index 00627afc76..0000000000
--- a/ruby-statistics/lib/enumerable.rb
+++ /dev/null
@@ -1,15 +0,0 @@
-# TODO: Avoid monkey-patching.
-module Enumerable
-  def mean
-    self.reduce(:+) / self.length.to_f
-  end
-
-  def variance
-    mean = self.mean
-    self.reduce(0) { |memo, value| memo + ((value - mean) ** 2) } / (self.length - 1).to_f
-  end
-
-  def standard_deviation
-    Math.sqrt(self.variance)
-  end
-end
diff --git a/ruby-statistics/lib/math.rb b/ruby-statistics/lib/math.rb
deleted file mode 100644
index 9be39ba446..0000000000
--- a/ruby-statistics/lib/math.rb
+++ /dev/null
@@ -1,120 +0,0 @@
-module Math
-  def self.factorial(n)
-    return if n < 0
-
-    n = n.to_i # Only integers.
-
-    return 1 if n == 0 || n == 1
-    Math.gamma(n + 1) # Math.gamma(x) == (n - 1)! for integer values
-  end
-
-  def self.combination(n, r)
-    self.factorial(n)/(self.factorial(r) * self.factorial(n - r)).to_f # n!/(r! * [n - r]!)
-  end
-
-  def self.permutation(n, k)
-    self.factorial(n)/self.factorial(n - k).to_f
-  end
-
-  # Function adapted from the python implementation that exists in https://en.wikipedia.org/wiki/Simpson%27s_rule#Sample_implementation
-  # Finite integral in the interval [a, b] split up in n-intervals
-  def self.simpson_rule(a, b, n, &block)
-    unless n.even?
-      puts "The composite simpson's rule needs even intervals!"
-      return
-    end
-
-    h = (b - a)/n.to_f
-    resA = yield(a)
-    resB = yield(b)
-
-    sum = resA + resB
-
-    (1..n).step(2).each do |number|
-      res = yield(a + number * h)
-      sum += 4 * res
-    end
-
-    (1..(n-1)).step(2).each do |number|
-      res = yield(a + number * h)
-      sum += 2 * res
-    end
-
-    return sum * h / 3.0
-  end
-
-  def self.lower_incomplete_gamma_function(s, x)
-    # The greater the iterations, the better. That's why we are iterating 10_000 * x times
-    self.simpson_rule(0, x, (10_000 * x.round).round) do |t|
-      (t ** (s - 1)) * Math.exp(-t)
-    end
-  end
-
-  def self.beta_function(x, y)
-    return 1 if x == 1 && y == 1
-
-    (Math.gamma(x) * Math.gamma(y))/Math.gamma(x + y)
-  end
-
-  ### This implementation is an adaptation of the incomplete beta function made in C by
-  ### Lewis Van Winkle, which released the code under the zlib license.
-  ### The whole math behind this code is described in the following post: https://codeplea.com/incomplete-beta-function-c
-  def self.incomplete_beta_function(x, alp, bet)
-    return if x < 0.0
-    return 1.0 if x > 1.0
-
-    tiny = 1.0E-50
-
-    if x > ((alp + 1.0)/(alp + bet + 2.0))
-      return 1.0 - self.incomplete_beta_function(1.0 - x, bet, alp)
-    end
-
-    # To avoid overflow problems, the implementation applies the logarithm properties
-    # to calculate in a faster and safer way the values.
-    lbet_ab = (Math.lgamma(alp)[0] + Math.lgamma(bet)[0] - Math.lgamma(alp + bet)[0]).freeze
-    front = (Math.exp(Math.log(x) * alp + Math.log(1.0 - x) * bet - lbet_ab) / alp.to_f).freeze
-
-    # This is the non-log version of the left part of the formula (before the continuous fraction)
-    # down_left = alp * self.beta_function(alp, bet)
-    # upper_left = (x ** alp) * ((1.0 - x) ** bet)
-    # front = upper_left/down_left
-
-    f, c, d = 1.0, 1.0, 0.0
-
-    returned_value = nil
-
-    # Let's do more iterations than the proposed implementation (200 iters)
-    (0..500).each do |number|
-      m = number/2
-
-      numerator = if number == 0
-                    1.0
-                  elsif number % 2 == 0
-                    (m * (bet - m) * x)/((alp + 2.0 * m - 1.0)* (alp + 2.0 * m))
-                  else
-                    top = -((alp + m) * (alp + bet + m) * x)
-                    down = ((alp + 2.0 * m) * (alp + 2.0 * m + 1.0))
-
-                    top/down
-                  end
-
-      d = 1.0 + numerator * d
-      d = tiny if d.abs < tiny
-      d = 1.0 / d
-
-      c = 1.0 + numerator / c
-      c = tiny if c.abs < tiny
-
-      cd = (c*d).freeze
-      f = f * cd
-
-
-      if (1.0 - cd).abs < 1.0E-10
-        returned_value = front * (f - 1.0)
-        break
-      end
-    end
-
-    returned_value
-  end
-end
diff --git a/ruby-statistics/lib/statistics.rb b/ruby-statistics/lib/statistics.rb
deleted file mode 100644
index 2e443c214a..0000000000
--- a/ruby-statistics/lib/statistics.rb
+++ /dev/null
@@ -1,7 +0,0 @@
-require File.dirname(__FILE__) + '/enumerable'
-require File.dirname(__FILE__) + '/math'
-Dir[ File.dirname(__FILE__) + '/statistics/**/*.rb'].each {|file| require file }
-
-module Statistics
-  # Your code goes here...
-end
diff --git a/ruby-statistics/lib/statistics/distribution.rb b/ruby-statistics/lib/statistics/distribution.rb
deleted file mode 100644
index 291eab9cc7..0000000000
--- a/ruby-statistics/lib/statistics/distribution.rb
+++ /dev/null
@@ -1,11 +0,0 @@
-Dir[File.dirname(__FILE__) + '/distribution/**/*.rb'].each {|file| require file }
-
-module Statistics
-  module Distribution
-  end
-end
-
-# If Distribution is not defined, setup alias.
-if defined?(Statistics) && !(defined?(Distribution))
-  Distribution = Statistics::Distribution
-end
diff --git a/ruby-statistics/lib/statistics/distribution/bernoulli.rb b/ruby-statistics/lib/statistics/distribution/bernoulli.rb
deleted file mode 100644
index 30ab789386..0000000000
--- a/ruby-statistics/lib/statistics/distribution/bernoulli.rb
+++ /dev/null
@@ -1,35 +0,0 @@
-module Statistics
-  module Distribution
-    class Bernoulli
-      def self.density_function(n, p)
-        return if n != 0 && n != 1 # The support of the distribution is n = {0, 1}.
-
-        case n
-        when 0 then 1.0 - p
-        when 1 then p
-        end
-      end
-
-      def self.cumulative_function(n, p)
-        return if n != 0 && n != 1 # The support of the distribution is n = {0, 1}.
-
-        case n
-        when 0 then 1.0 - p
-        when 1 then 1.0
-        end
-      end
-
-      def self.variance(p)
-        p * (1.0 - p)
-      end
-
-      def self.skewness(p)
-        (1.0 - 2.0*p).to_f / Math.sqrt(p * (1.0 - p))
-      end
-
-      def self.kurtosis(p)
-        (6.0 * (p ** 2) - (6 * p) + 1) / (p * (1.0 - p))
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/distribution/beta.rb b/ruby-statistics/lib/statistics/distribution/beta.rb
deleted file mode 100644
index 90970954b7..0000000000
--- a/ruby-statistics/lib/statistics/distribution/beta.rb
+++ /dev/null
@@ -1,36 +0,0 @@
-module Statistics
-  module Distribution
-    class Beta
-      attr_accessor :alpha, :beta
-
-      def initialize(alp, bet)
-        self.alpha = alp.to_f
-        self.beta = bet.to_f
-      end
-
-      def cumulative_function(value)
-        Math.incomplete_beta_function(value, alpha, beta)
-      end
-
-      def density_function(value)
-        return 0 if value < 0 || value > 1 # Density function defined in the [0,1] interval
-
-        num = (value**(alpha - 1)) * ((1 - value)**(beta - 1))
-        den = Math.beta_function(alpha, beta)
-
-        num/den
-      end
-
-      def mode
-        return unless alpha > 1 && beta > 1
-
-        (alpha - 1)/(alpha + beta - 2)
-      end
-
-      def mean
-        return if alpha + beta == 0
-        alpha / (alpha + beta)
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/distribution/binomial.rb b/ruby-statistics/lib/statistics/distribution/binomial.rb
deleted file mode 100644
index 3ce513ca00..0000000000
--- a/ruby-statistics/lib/statistics/distribution/binomial.rb
+++ /dev/null
@@ -1,49 +0,0 @@
-module Statistics
-  module Distribution
-    class Binomial
-      attr_accessor :number_of_trials, :probability_per_trial
-      def initialize(n, p)
-        self.number_of_trials = n.to_i
-        self.probability_per_trial = p
-      end
-
-      def probability_mass_function(k)
-        return if k < 0 || k > number_of_trials
-        k = k.to_i
-
-        Math.combination(number_of_trials, k) *
-          (probability_per_trial ** k) * ((1 - probability_per_trial) ** (number_of_trials - k))
-      end
-
-      def cumulative_function(k)
-        return if k < 0 || k > number_of_trials
-        k = k.to_i
-
-        p = 1 - probability_per_trial
-        Math.incomplete_beta_function(p, number_of_trials - k, 1 + k)
-      end
-
-      def mean
-        number_of_trials * probability_per_trial
-      end
-
-      def variance
-        mean * (1 - probability_per_trial)
-      end
-
-      def mode
-        test = (number_of_trials + 1) * probability_per_trial
-
-        returned = if test == 0 || (test % 1 != 0)
-                     test.floor
-                   elsif (test % 1 == 0)  && (test >= 1 && test <= number_of_trials)
-                     [test, test - 1]
-                   elsif test == number_of_trials + 1
-                     number_of_trials
-                   end
-
-        returned
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/distribution/chi_squared.rb b/ruby-statistics/lib/statistics/distribution/chi_squared.rb
deleted file mode 100644
index 0996999036..0000000000
--- a/ruby-statistics/lib/statistics/distribution/chi_squared.rb
+++ /dev/null
@@ -1,37 +0,0 @@
-module Statistics
-  module Distribution
-    class ChiSquared
-      attr_accessor :degrees_of_freedom
-
-      alias_method :mean, :degrees_of_freedom
-
-      def initialize(k)
-        self.degrees_of_freedom = k
-      end
-
-      def cumulative_function(value)
-        k = degrees_of_freedom/2.0
-        Math.lower_incomplete_gamma_function(k, value/2.0)/Math.gamma(k)
-      end
-
-      def density_function(value)
-        return 0 if value < 0
-
-        common = degrees_of_freedom/2.0
-
-        left_down = (2 ** common) * Math.gamma(common)
-        right = (value ** (common - 1)) * Math.exp(-(value/2.0))
-
-        (1.0/left_down) * right
-      end
-
-      def mode
-        [degrees_of_freedom - 2, 0].max
-      end
-
-      def variance
-        degrees_of_freedom * 2
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/distribution/empirical.rb b/ruby-statistics/lib/statistics/distribution/empirical.rb
deleted file mode 100644
index 63d72bdfed..0000000000
--- a/ruby-statistics/lib/statistics/distribution/empirical.rb
+++ /dev/null
@@ -1,26 +0,0 @@
-module Statistics
-  module Distribution
-    class Empirical
-      attr_accessor :samples
-
-      def initialize(samples:)
-        self.samples = samples
-      end
-
-      # Formula grabbed from here: https://statlect.com/asymptotic-theory/empirical-distribution
-      def cumulative_function(x:)
-        cumulative_sum = samples.reduce(0) do |summation, sample|
-          summation += if sample <= x
-                         1
-                       else
-                         0
-                       end
-
-          summation
-        end
-
-        cumulative_sum / samples.size.to_f
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/distribution/f.rb b/ruby-statistics/lib/statistics/distribution/f.rb
deleted file mode 100644
index af3af9bc09..0000000000
--- a/ruby-statistics/lib/statistics/distribution/f.rb
+++ /dev/null
@@ -1,46 +0,0 @@
-module Statistics
-  module Distribution
-    class F
-      attr_accessor :d1, :d2 # Degrees of freedom #1 and #2
-
-      def initialize(k, j)
-        self.d1 = k
-        self.d2 = j
-      end
-
-      # Formula extracted from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3665.htm#CDF
-      def cumulative_function(value)
-        k = d2/(d2 + d1 * value.to_f)
-
-        1 - Math.incomplete_beta_function(k, d2/2.0, d1/2.0)
-      end
-
-      def density_function(value)
-        return if d1 < 0 || d2 < 0 # F-pdf is well defined for the [0, +infinity) interval.
-
-        val = value.to_f
-        upper = ((d1 * val) ** d1) * (d2**d2)
-        lower = (d1 * val + d2) ** (d1 + d2)
-        up = Math.sqrt(upper/lower.to_f)
-        down = val * Math.beta_function(d1/2.0, d2/2.0)
-
-        up/down.to_f
-      end
-
-      def mean
-        return if d2 <= 2
-
-        d2/(d2 - 2).to_f
-      end
-
-      def mode
-        return if d1 <= 2
-
-        left = (d1 - 2)/d1.to_f
-        right = d2/(d2 + 2).to_f
-
-        left * right
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/distribution/geometric.rb b/ruby-statistics/lib/statistics/distribution/geometric.rb
deleted file mode 100644
index fe59eb505b..0000000000
--- a/ruby-statistics/lib/statistics/distribution/geometric.rb
+++ /dev/null
@@ -1,76 +0,0 @@
-module Statistics
-  module Distribution
-    class Geometric
-      attr_accessor :probability_of_success, :always_success_allowed
-
-      def initialize(p, always_success: false)
-        self.probability_of_success = p.to_f
-        self.always_success_allowed = always_success
-      end
-
-      def density_function(k)
-        k = k.to_i
-
-        if always_success_allowed
-          return if k < 0
-
-          ((1.0 - probability_of_success) ** k) * probability_of_success
-        else
-          return if k <= 0
-
-          ((1.0 - probability_of_success) ** (k - 1.0)) * probability_of_success
-        end
-      end
-
-      def cumulative_function(k)
-        k = k.to_i
-
-        if always_success_allowed
-          return if k < 0
-
-          1.0 - ((1.0 - probability_of_success) ** (k + 1.0))
-        else
-          return if k <= 0
-
-          1.0 - ((1.0 - probability_of_success) ** k)
-        end
-      end
-
-      def mean
-        if always_success_allowed
-          (1.0 - probability_of_success) / probability_of_success
-        else
-          1.0 / probability_of_success
-        end
-      end
-
-      def median
-        if always_success_allowed
-          (-1.0 / Math.log2(1.0 - probability_of_success)).ceil - 1.0
-        else
-          (-1.0 / Math.log2(1.0 - probability_of_success)).ceil
-        end
-      end
-
-      def mode
-        if always_success_allowed
-          0.0
-        else
-          1.0
-        end
-      end
-
-      def variance
-        (1.0 - probability_of_success) / (probability_of_success ** 2)
-      end
-
-      def skewness
-        (2.0 - probability_of_success) / Math.sqrt(1.0 - probability_of_success)
-      end
-
-      def kurtosis
-        6.0 + ((probability_of_success ** 2) / (1.0 - probability_of_success))
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/distribution/logseries.rb b/ruby-statistics/lib/statistics/distribution/logseries.rb
deleted file mode 100644
index 359f9c619e..0000000000
--- a/ruby-statistics/lib/statistics/distribution/logseries.rb
+++ /dev/null
@@ -1,51 +0,0 @@
-module Statistics
-  module Distribution
-    class LogSeries
-      def self.density_function(k, p)
-        return if k <= 0
-        k = k.to_i
-
-        left = (-1.0 / Math.log(1.0 - p))
-        right = (p ** k).to_f
-
-        left * right / k
-      end
-
-      def self.cumulative_function(k, p)
-        return if k <= 0
-
-        # Sadly, the incomplete beta function is converging
-        # too fast to zero and breaking the calculation on logs.
-        # So, we default to the basic definition of the CDF which is
-        # the integral (-Inf, K) of the PDF, with P(X <= x) which can
-        # be solved as a summation of all PDFs from 1 to K. Note that the summation approach
-        # only applies to discrete distributions.
-        #
-        # right = Math.incomplete_beta_function(p, (k + 1).floor, 0) / Math.log(1.0 - p)
-        # 1.0 + right
-
-        result = 0.0
-        1.upto(k) do |number|
-          result += self.density_function(number, p)
-        end
-
-        result
-      end
-
-      def self.mode
-        1.0
-      end
-
-      def self.mean(p)
-        (-1.0 / Math.log(1.0 - p)) * (p / (1.0 - p))
-      end
-
-      def self.variance(p)
-        up = p + Math.log(1.0 - p)
-        down = ((1.0 - p) ** 2) * (Math.log(1.0 - p) ** 2)
-
-        (-1.0 * p) * (up / down.to_f)
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/distribution/negative_binomial.rb b/ruby-statistics/lib/statistics/distribution/negative_binomial.rb
deleted file mode 100644
index 34dc451cae..0000000000
--- a/ruby-statistics/lib/statistics/distribution/negative_binomial.rb
+++ /dev/null
@@ -1,51 +0,0 @@
-module Statistics
-  module Distribution
-    class NegativeBinomial
-      attr_accessor :number_of_failures, :probability_per_trial
-
-      def initialize(r, p)
-        self.number_of_failures = r.to_i
-        self.probability_per_trial = p
-      end
-
-      def probability_mass_function(k)
-        return if number_of_failures < 0 || k < 0 || k > number_of_failures
-
-        left = Math.combination(k + number_of_failures - 1, k)
-        right = ((1 - probability_per_trial) ** number_of_failures) * (probability_per_trial ** k)
-
-        left * right
-      end
-
-      def cumulative_function(k)
-        return if k < 0 || k > number_of_failures
-        k = k.to_i
-
-        1.0 - Math.incomplete_beta_function(probability_per_trial, k + 1, number_of_failures)
-      end
-
-      def mean
-        (probability_per_trial * number_of_failures)/(1 - probability_per_trial).to_f
-      end
-
-      def variance
-        (probability_per_trial * number_of_failures)/((1 - probability_per_trial) ** 2).to_f
-      end
-
-      def skewness
-        (1 + probability_per_trial).to_f / Math.sqrt(probability_per_trial * number_of_failures)
-      end
-
-      def mode
-        if number_of_failures > 1
-          up = probability_per_trial * (number_of_failures - 1)
-          down = (1 - probability_per_trial).to_f
-
-          (up/down).floor
-        elsif number_of_failures <= 1
-          0.0
-        end
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/distribution/normal.rb b/ruby-statistics/lib/statistics/distribution/normal.rb
deleted file mode 100644
index 8feef0247f..0000000000
--- a/ruby-statistics/lib/statistics/distribution/normal.rb
+++ /dev/null
@@ -1,139 +0,0 @@
-module Statistics
-  module Distribution
-    class Normal
-      attr_accessor :mean, :standard_deviation, :variance
-      alias_method :mode, :mean
-
-      def initialize(avg, std)
-        self.mean = avg.to_f
-        self.standard_deviation = std.to_f
-        self.variance = std.to_f**2
-      end
-
-      def cumulative_function(value)
-        (1/2.0) * (1.0 + Math.erf((value - mean)/(standard_deviation * Math.sqrt(2.0))))
-      end
-
-      def density_function(value)
-        return 0 if standard_deviation <= 0
-
-        up_right = (value - mean)**2.0
-        down_right = 2.0 * variance
-        right = Math.exp(-(up_right/down_right))
-        left_down = Math.sqrt(2.0 * Math::PI * variance)
-        left_up = 1.0
-
-        (left_up/(left_down) * right)
-      end
-
-      ## Marsaglia polar method implementation for random gaussian (normal) number generation.
-      # References:
-      # https://en.wikipedia.org/wiki/Marsaglia_polar_method
-      # https://math.stackexchange.com/questions/69245/transform-uniform-distribution-to-normal-distribution-using-lindeberg-l%C3%A9vy-clt
-      # https://www.projectrhea.org/rhea/index.php/The_principles_for_how_to_generate_random_samples_from_a_Gaussian_distribution
-
-      def random(elements: 1, seed: Random.new_seed)
-        results = []
-
-        # Setup seed
-        srand(seed)
-
-        # Number of random numbers to be generated.
-        elements.times do
-          x, y, r = 0.0, 0.0, 0.0
-
-          # Find an (x, y) point in the x^2 + y^2 < 1 circumference.
-          loop do
-            x = 2.0 * rand - 1.0
-            y = 2.0 * rand - 1.0
-
-            r = (x ** 2) + (y ** 2)
-
-            break unless r >= 1.0 || r == 0
-          end
-
-          # Project the random point to the required random distance
-          r = Math.sqrt(-2.0 * Math.log(r) / r)
-
-          # Transform the random distance to a gaussian value and append it to the results array
-          results << mean + x * r * standard_deviation
-        end
-
-        if elements == 1
-          results.first
-        else
-          results
-        end
-      end
-    end
-
-    class StandardNormal < Normal
-      def initialize
-        super(0, 1) # Mean = 0, Std = 1
-      end
-
-      def density_function(value)
-        pow = (value**2)/2.0
-        euler = Math.exp(-pow)
-
-        euler/Math.sqrt(2 * Math::PI)
-      end
-    end
-
-    # Inverse Standard Normal distribution:
-    # References:
-    # https://en.wikipedia.org/wiki/Inverse_distribution
-    # http://www.source-code.biz/snippets/vbasic/9.htm
-    class InverseStandardNormal < StandardNormal
-      A1 = -39.6968302866538
-      A2 = 220.946098424521
-      A3 = -275.928510446969
-      A4 = 138.357751867269
-      A5 = -30.6647980661472
-      A6 = 2.50662827745924
-      B1 = -54.4760987982241
-      B2 = 161.585836858041
-      B3 = -155.698979859887
-      B4 = 66.8013118877197
-      B5 = -13.2806815528857
-      C1 = -7.78489400243029E-03
-      C2 = -0.322396458041136
-      C3 = -2.40075827716184
-      C4 = -2.54973253934373
-      C5 = 4.37466414146497
-      C6 = 2.93816398269878
-      D1 = 7.78469570904146E-03
-      D2 = 0.32246712907004
-      D3 = 2.445134137143
-      D4 = 3.75440866190742
-      P_LOW = 0.02425
-      P_HIGH = 1 - P_LOW
-
-      def density_function(_)
-        raise NotImplementedError
-      end
-
-      def random(elements: 1, seed: Random.new_seed)
-        raise NotImplementedError
-      end
-
-      def cumulative_function(value)
-        return if value < 0.0 || value > 1.0
-        return -1.0 * Float::INFINITY if value.zero?
-        return Float::INFINITY if value == 1.0
-
-        if value < P_LOW
-          q = Math.sqrt((Math.log(value) * -2.0))
-          (((((C1 * q + C2) * q + C3) * q + C4) * q + C5) * q + C6) / ((((D1 * q + D2) * q + D3) * q + D4) * q + 1.0)
-        elsif value <= P_HIGH
-          q = value - 0.5
-          r = q ** 2
-          (((((A1 * r + A2) * r + A3) * r + A4) * r + A5) * r + A6) * q / (((((B1 * r + B2) * r + B3) * r + B4) * r + B5) * r + 1.0)
-        else
-          q = Math.sqrt((Math.log(1 - value) * -2.0))
-          - (((((C1 * q + C2) * q + C3) * q + C4) * q + C5) * q + C6) / ((((D1 * q + D2) * q + D3) * q + D4) * q + 1)
-        end
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/distribution/poisson.rb b/ruby-statistics/lib/statistics/distribution/poisson.rb
deleted file mode 100644
index b317624ad9..0000000000
--- a/ruby-statistics/lib/statistics/distribution/poisson.rb
+++ /dev/null
@@ -1,38 +0,0 @@
-module Statistics
-  module Distribution
-    class Poisson
-      attr_accessor :expected_number_of_occurrences
-
-      alias_method :mean, :expected_number_of_occurrences
-      alias_method :variance, :expected_number_of_occurrences
-
-      def initialize(l)
-        self.expected_number_of_occurrences = l
-      end
-
-      def probability_mass_function(k)
-        return if k < 0 || expected_number_of_occurrences < 0
-
-        k = k.to_i
-
-        upper = (expected_number_of_occurrences ** k) * Math.exp(-expected_number_of_occurrences)
-        lower = Math.factorial(k)
-
-        upper/lower.to_f
-      end
-
-      def cumulative_function(k)
-        return if k < 0 || expected_number_of_occurrences < 0
-
-        k = k.to_i
-
-        upper = Math.lower_incomplete_gamma_function((k + 1).floor, expected_number_of_occurrences)
-        lower = Math.factorial(k.floor)
-
-        # We need the right tail, i.e.: The upper incomplete gamma function. This can be
-        # achieved by doing a substraction between 1 and the lower incomplete gamma function.
-        1 - (upper/lower.to_f)
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/distribution/t_student.rb b/ruby-statistics/lib/statistics/distribution/t_student.rb
deleted file mode 100644
index 66b05780ba..0000000000
--- a/ruby-statistics/lib/statistics/distribution/t_student.rb
+++ /dev/null
@@ -1,82 +0,0 @@
-module Statistics
-  module Distribution
-    class TStudent
-      attr_accessor :degrees_of_freedom
-      attr_reader :mode
-
-      def initialize(v)
-        self.degrees_of_freedom = v
-        @mode = 0
-      end
-
-      ### Extracted from https://codeplea.com/incomplete-beta-function-c
-      ### This function is shared under zlib license and the author is Lewis Van Winkle
-      def cumulative_function(value)
-        upper = (value + Math.sqrt(value * value + degrees_of_freedom))
-        lower = (2.0 * Math.sqrt(value * value + degrees_of_freedom))
-
-        x = upper/lower
-
-        alpha = degrees_of_freedom/2.0
-        beta = degrees_of_freedom/2.0
-
-        Math.incomplete_beta_function(x, alpha, beta)
-      end
-
-      def density_function(value)
-        return if degrees_of_freedom <= 0
-
-        upper = Math.gamma((degrees_of_freedom + 1)/2.0)
-        lower = Math.sqrt(degrees_of_freedom * Math::PI) * Math.gamma(degrees_of_freedom/2.0)
-        left = upper/lower
-        right = (1 + ((value ** 2)/degrees_of_freedom.to_f)) ** -((degrees_of_freedom + 1)/2.0)
-
-        left * right
-      end
-
-      def mean
-        0 if degrees_of_freedom > 1
-      end
-
-      def variance
-        if degrees_of_freedom > 1 && degrees_of_freedom <= 2
-          Float::INFINITY
-        elsif degrees_of_freedom > 2
-          degrees_of_freedom/(degrees_of_freedom - 2.0)
-        end
-      end
-
-      # Quantile function extracted from http://www.jennessent.com/arcview/idf.htm
-      # TODO: Make it truly Student's T sample.
-      def random(elements: 1, seed: Random.new_seed)
-        warn 'This is an alpha version code. The generated sample is similar to an uniform distribution'
-        srand(seed)
-
-        v = degrees_of_freedom
-        results = []
-
-        # Because the Quantile function of a student-t distribution is between (-Infinity, y)
-        # we setup an small threshold in order to properly compute the integral
-        threshold = 10_000.0e-12
-
-        elements.times do
-          y = rand
-          results << Math.simpson_rule(threshold, y, 10_000) do |t|
-            up = Math.gamma((v+1)/2.0)
-            down = Math.sqrt(Math::PI * v) * Math.gamma(v/2.0)
-            right = (1 + ((y ** 2)/v.to_f)) ** ((v+1)/2.0)
-            left = up/down.to_f
-
-            left * right
-          end
-        end
-
-        if elements == 1
-          results.first
-        else
-          results
-        end
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/distribution/uniform.rb b/ruby-statistics/lib/statistics/distribution/uniform.rb
deleted file mode 100644
index 4550159bc9..0000000000
--- a/ruby-statistics/lib/statistics/distribution/uniform.rb
+++ /dev/null
@@ -1,40 +0,0 @@
-module Statistics
-  module Distribution
-    class Uniform
-      attr_accessor :left, :right
-
-      def initialize(a, b)
-        self.left = a.to_f
-        self.right = b.to_f
-      end
-
-      def density_function(value)
-        if value >= left && value <= right
-          1/(right - left)
-        else
-          0
-        end
-      end
-
-      def cumulative_function(value)
-        if value < left
-          0
-        elsif value >= left && value <= right
-          (value - left)/(right - left)
-        else
-          1
-        end
-      end
-
-      def mean
-        (1/2.0) * ( left + right )
-      end
-      alias_method :median, :mean
-
-
-      def variance
-        (1/12.0) * ( right - left ) ** 2
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/distribution/weibull.rb b/ruby-statistics/lib/statistics/distribution/weibull.rb
deleted file mode 100644
index 90c876855e..0000000000
--- a/ruby-statistics/lib/statistics/distribution/weibull.rb
+++ /dev/null
@@ -1,66 +0,0 @@
-module Statistics
-  module Distribution
-    class Weibull
-      attr_accessor :shape, :scale # k and lambda
-
-      def initialize(k, lamb)
-        self.shape = k.to_f
-        self.scale = lamb.to_f
-      end
-
-      def cumulative_function(random_value)
-        return 0 if random_value < 0
-
-        1 - Math.exp(-((random_value/scale) ** shape))
-      end
-
-      def density_function(value)
-        return if shape <= 0 || scale <= 0
-        return 0 if value < 0
-
-        left = shape/scale
-        center = (value/scale)**(shape - 1)
-        right = Math.exp(-((value/scale)**shape))
-
-        left * center * right
-      end
-
-      def mean
-        scale * Math.gamma(1 + (1/shape))
-      end
-
-      def mode
-        return 0 if shape <= 1
-
-        scale * (((shape - 1)/shape) ** (1/shape))
-      end
-
-      def variance
-        left = Math.gamma(1 + (2/shape))
-        right = Math.gamma(1 + (1/shape)) ** 2
-
-        (scale ** 2) * (left - right)
-      end
-
-      # Using the inverse CDF function, also called quantile, we can calculate
-      # a random sample that follows a weibull distribution.
-      #
-      # Formula extracted from https://www.taygeta.com/random/weibull.html
-      def random(elements: 1, seed: Random.new_seed)
-        results = []
-
-        srand(seed)
-
-        elements.times do
-          results << ((-1/scale) * Math.log(1 - rand)) ** (1/shape)
-        end
-
-        if elements == 1
-          results.first
-        else
-          results
-        end
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/spearman_rank_coefficient.rb b/ruby-statistics/lib/statistics/spearman_rank_coefficient.rb
deleted file mode 100644
index cbdd7a03ae..0000000000
--- a/ruby-statistics/lib/statistics/spearman_rank_coefficient.rb
+++ /dev/null
@@ -1,71 +0,0 @@
-module Statistics
-  class SpearmanRankCoefficient
-    def self.rank(data:, return_ranks_only: true)
-      descending_order_data = data.sort { |a, b| b <=> a }
-      rankings = {}
-
-      data.each do |value|
-        # If we have ties, the find_index method will only retrieve the index of the
-        # first element in the list (i.e, the most close to the left of the array),
-        # so when a tie is detected, we increase the temporal ranking by the number of
-        # counted elements at that particular time and then we increase the counter.
-        temporal_ranking = descending_order_data.find_index(value) + 1 # 0-index
-
-        if rankings.fetch(value, false)
-          rankings[value][:rank] += (temporal_ranking + rankings[value][:counter])
-          rankings[value][:counter] += 1
-          rankings[value][:tie_rank] = rankings[value][:rank] / rankings[value][:counter].to_f
-        else
-          rankings[value] = { counter: 1, rank: temporal_ranking, tie_rank: temporal_ranking }
-        end
-      end
-
-      if return_ranks_only
-        data.map do |value|
-          rankings[value][:tie_rank]
-        end
-      else
-        rankings
-      end
-    end
-
-    # Formulas extracted from: https://statistics.laerd.com/statistical-guides/spearmans-rank-order-correlation-statistical-guide.php
-    def self.coefficient(set_one, set_two)
-      raise 'Both group sets must have the same number of cases.' if set_one.size != set_two.size
-      return if set_one.size == 0 && set_two.size == 0
-
-      set_one_mean, set_two_mean = set_one.mean, set_two.mean
-      have_tie_ranks = (set_one + set_two).any? { |rank| rank.is_a?(Float) }
-
-      if have_tie_ranks
-        numerator = 0
-        squared_differences_set_one = 0
-        squared_differences_set_two = 0
-
-        set_one.size.times do |idx|
-          local_diff_one = (set_one[idx] - set_one_mean)
-          local_diff_two = (set_two[idx] - set_two_mean)
-
-          squared_differences_set_one += local_diff_one ** 2
-          squared_differences_set_two += local_diff_two ** 2
-
-          numerator += local_diff_one * local_diff_two
-        end
-
-        denominator = Math.sqrt(squared_differences_set_one * squared_differences_set_two)
-
-        numerator / denominator.to_f # This is rho or spearman's coefficient.
-      else
-        sum_squared_differences = set_one.each_with_index.reduce(0) do |memo, (rank_one, index)|
-          memo += ((rank_one - set_two[index]) ** 2)
-          memo
-        end
-
-        numerator = 6 * sum_squared_differences
-        denominator = ((set_one.size ** 3) - set_one.size)
-
-        1.0 - (numerator / denominator.to_f) # This is rho or spearman's coefficient.
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/statistical_test.rb b/ruby-statistics/lib/statistics/statistical_test.rb
deleted file mode 100644
index 5ce3c234db..0000000000
--- a/ruby-statistics/lib/statistics/statistical_test.rb
+++ /dev/null
@@ -1,11 +0,0 @@
-Dir[File.dirname(__FILE__) + '/statistical_test/**/*.rb'].each {|file| require file }
-
-module Statistics
-  module StatisticalTest
-  end
-end
-
-# If StatisticalTest is not defined, setup alias.
-if defined?(Statistics) && !(defined?(StatisticalTest))
-  StatisticalTest = Statistics::StatisticalTest
-end
diff --git a/ruby-statistics/lib/statistics/statistical_test/chi_squared_test.rb b/ruby-statistics/lib/statistics/statistical_test/chi_squared_test.rb
deleted file mode 100644
index ce96b34f77..0000000000
--- a/ruby-statistics/lib/statistics/statistical_test/chi_squared_test.rb
+++ /dev/null
@@ -1,42 +0,0 @@
-module Statistics
-  module StatisticalTest
-    class ChiSquaredTest
-      def self.chi_statistic(expected, observed)
-        # If the expected is a number, we asumme that all expected observations
-        # has the same probability to occur, hence we expect to see the same number
-        # of expected observations per each observed value
-        statistic = if expected.is_a? Numeric
-                      observed.reduce(0) do |memo, observed_value|
-                        up = (observed_value - expected) ** 2
-                        memo += (up/expected.to_f)
-                      end
-                    else
-                      expected.each_with_index.reduce(0) do |memo, (expected_value, index)|
-                        up = (observed[index] - expected_value) ** 2
-                        memo += (up/expected_value.to_f)
-                      end
-                    end
-
-          [statistic, observed.size - 1]
-      end
-
-      def self.goodness_of_fit(alpha, expected, observed)
-        chi_score, df = *self.chi_statistic(expected, observed) # Splat array result
-
-        return if chi_score.nil? || df.nil?
-
-        probability = Distribution::ChiSquared.new(df).cumulative_function(chi_score)
-        p_value = 1 - probability
-
-        # According to https://stats.stackexchange.com/questions/29158/do-you-reject-the-null-hypothesis-when-p-alpha-or-p-leq-alpha
-        # We can assume that if p_value <= alpha, we can safely reject the null hypothesis, ie. accept the alternative hypothesis.
-        { probability: probability,
-          p_value: p_value,
-          alpha: alpha,
-          null: alpha < p_value,
-          alternative: p_value <= alpha,
-          confidence_level: 1 - alpha }
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/statistical_test/f_test.rb b/ruby-statistics/lib/statistics/statistical_test/f_test.rb
deleted file mode 100644
index 94be720c96..0000000000
--- a/ruby-statistics/lib/statistics/statistical_test/f_test.rb
+++ /dev/null
@@ -1,83 +0,0 @@
-module Statistics
-  module StatisticalTest
-    class FTest
-      # This method calculates the one-way ANOVA F-test statistic.
-      # We assume that all specified arguments are arrays.
-      # It returns an array with three elements:
-      #   [F-statistic or F-score, degrees of freedom numerator, degrees of freedom denominator].
-      #
-      # Formulas extracted from:
-      # https://courses.lumenlearning.com/boundless-statistics/chapter/one-way-anova/
-      # http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_HypothesisTesting-ANOVA/BS704_HypothesisTesting-Anova_print.html
-      def self.anova_f_score(*args)
-        # If only two groups have been specified as arguments, we follow the classic F-Test for
-        # equality of variances, which is the ratio between the variances.
-        f_score = nil
-        df1 = nil
-        df2 = nil
-
-        if args.size == 2
-          variances = [args[0].variance, args[1].variance]
-
-          f_score = variances.max/variances.min.to_f
-          df1 = 1 # k-1 (k = 2)
-          df2 = args.flatten.size - 2 # N-k (k = 2)
-        elsif args.size > 2
-          total_groups = args.size
-          total_elements = args.flatten.size
-          overall_mean = args.flatten.mean
-
-          sample_sizes = args.map(&:size)
-          sample_means = args.map(&:mean)
-          sample_stds = args.map(&:standard_deviation)
-
-          # Variance between groups
-          iterator = sample_sizes.each_with_index
-
-          variance_between_groups = iterator.reduce(0) do |summation, (size, index)|
-            inner_calculation = size * ((sample_means[index] - overall_mean) ** 2)
-
-            summation += (inner_calculation / (total_groups - 1).to_f)
-          end
-
-          # Variance within groups
-          variance_within_groups = (0...total_groups).reduce(0) do |outer_summation, group_index|
-            outer_summation += args[group_index].reduce(0) do |inner_sumation, observation|
-              inner_calculation = ((observation - sample_means[group_index]) ** 2)
-              inner_sumation += (inner_calculation / (total_elements - total_groups).to_f)
-            end
-          end
-
-          f_score = variance_between_groups/variance_within_groups.to_f
-          df1 = total_groups - 1
-          df2 = total_elements - total_groups
-        end
-
-        [f_score, df1, df2]
-      end
-
-      # This method expects the alpha value and the groups to calculate the one-way ANOVA test.
-      # It returns a hash with multiple information and the test result (if reject the null hypotesis or not).
-      # Keep in mind that the values for the alternative key (true/false) does not imply that the alternative hypothesis
-      # is TRUE or FALSE. It's a minor notation advantage to decide if reject the null hypothesis or not.
-
-      def self.one_way_anova(alpha, *args)
-        f_score, df1, df2 = *self.anova_f_score(*args) # Splat array result
-
-        return if f_score.nil? || df1.nil? || df2.nil?
-
-        probability = Distribution::F.new(df1, df2).cumulative_function(f_score)
-        p_value = 1 - probability
-
-        # According to https://stats.stackexchange.com/questions/29158/do-you-reject-the-null-hypothesis-when-p-alpha-or-p-leq-alpha
-        # We can assume that if p_value <= alpha, we can safely reject the null hypothesis, ie. accept the alternative hypothesis.
-        { probability: probability,
-          p_value: p_value,
-          alpha: alpha,
-          null: alpha < p_value,
-          alternative: p_value <= alpha,
-          confidence_level: 1 - alpha }
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/statistical_test/kolmogorov_smirnov_test.rb b/ruby-statistics/lib/statistics/statistical_test/kolmogorov_smirnov_test.rb
deleted file mode 100644
index 19c5b12fc1..0000000000
--- a/ruby-statistics/lib/statistics/statistical_test/kolmogorov_smirnov_test.rb
+++ /dev/null
@@ -1,70 +0,0 @@
-module Statistics
-  module StatisticalTest
-    class KolmogorovSmirnovTest
-      # Common alpha, and critical D are calculated following formulas from: https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test#Two-sample_Kolmogorov%E2%80%93Smirnov_test
-      def self.two_samples(group_one:, group_two:, alpha: 0.05)
-        samples = group_one + group_two # We can use unbalaced group samples
-
-        ecdf_one = Distribution::Empirical.new(samples: group_one)
-        ecdf_two = Distribution::Empirical.new(samples: group_two)
-
-        d_max = samples.sort.map do |sample|
-          d1 = ecdf_one.cumulative_function(x: sample)
-          d2 = ecdf_two.cumulative_function(x: sample)
-
-          (d1 - d2).abs
-        end.max
-
-        # TODO: Validate calculation of Common alpha.
-        common_alpha = Math.sqrt((-0.5 * Math.log(alpha)))
-        radicand = (group_one.size + group_two.size) / (group_one.size * group_two.size).to_f
-
-        critical_d = common_alpha * Math.sqrt(radicand)
-        # critical_d = self.critical_d(alpha: alpha, n: samples.size)
-
-        # We are unable to calculate the p_value, because we don't have the Kolmogorov distribution
-        # defined. We reject the null hypotesis if Dmax is > than Dcritical.
-        { d_max: d_max,
-          d_critical: critical_d,
-          total_samples: samples.size,
-          alpha: alpha,
-          null: d_max <= critical_d,
-          alternative: d_max > critical_d,
-          confidence_level: 1.0 - alpha }
-      end
-
-      # This is an implementation of the formula presented by Paul Molin and Hervé Abdi in a paper,
-      # called "New Table and numerical approximations for Kolmogorov-Smirnov / Lilliefors / Van Soest
-      # normality test".
-      # In this paper, the authors defines a couple of 6th-degree polynomial functions that allow us
-      # to find an aproximation of the real critical value. This is based in the conclusions made by
-      # Dagnelie (1968), where indicates that critical values given by Lilliefors can be approximated
-      # numerically.
-      #
-      # In general, the formula found is:
-      #  C(N, alpha) ^ -2  = A(alpha) * N + B(alpha).
-      #
-      # Where A(alpha), B(alpha) are two 6th degree polynomial functions computed using the principle
-      # of Monte Carlo simulations.
-      #
-      # paper can be found here: https://utdallas.edu/~herve/MolinAbdi1998-LillieforsTechReport.pdf
-      # def self.critical_d(alpha:, n:)
-      #   confidence = 1.0 - alpha
-
-      #   a_alpha = 6.32207539843126 -17.1398870006148 * confidence +
-      #     38.42812675101057 * (confidence ** 2) - 45.93241384693391 * (confidence ** 3) +
-      #     7.88697700041829 * (confidence ** 4) + 29.79317711037858 * (confidence ** 5) -
-      #     18.48090137098585 * (confidence ** 6)
-
-      #   b_alpha = 12.940399038404 - 53.458334259532 * confidence +
-      #     186.923866119699 * (confidence ** 2) - 410.582178349305 * (confidence ** 3) +
-      #     517.377862566267 * (confidence ** 4) - 343.581476222384 * (confidence ** 5) +
-      #     92.123451358715 * (confidence ** 6)
-
-      #   Math.sqrt(1.0 / (a_alpha * n + b_alpha))
-      # end
-    end
-
-    KSTest = KolmogorovSmirnovTest # Alias
-  end
-end
diff --git a/ruby-statistics/lib/statistics/statistical_test/t_test.rb b/ruby-statistics/lib/statistics/statistical_test/t_test.rb
deleted file mode 100644
index 9fdd55ac23..0000000000
--- a/ruby-statistics/lib/statistics/statistical_test/t_test.rb
+++ /dev/null
@@ -1,92 +0,0 @@
-module Statistics
-  module StatisticalTest
-    class TTest
-      # Errors for Zero std
-      class ZeroStdError < StandardError
-        STD_ERROR_MSG = 'Standard deviation for the difference or group is zero. Please, reconsider sample contents'.freeze
-      end
-
-      # Perform a T-Test for one or two samples.
-      # For the tails param, we need a symbol: :one_tail or :two_tail
-      def self.perform(alpha, tails, *args)
-        return if args.size < 2
-
-        degrees_of_freedom = 0
-
-        # If the comparison mean has been specified
-        t_score = if args[0].is_a? Numeric
-                    data_mean = args[1].mean
-                    data_std = args[1].standard_deviation
-
-                    raise ZeroStdError, ZeroStdError::STD_ERROR_MSG if data_std == 0
-
-                    comparison_mean = args[0]
-                    degrees_of_freedom = args[1].size
-
-                    (data_mean - comparison_mean)/(data_std / Math.sqrt(args[1].size).to_f).to_f
-                  else
-                    sample_left_mean = args[0].mean
-                    sample_left_variance = args[0].variance
-                    sample_right_variance = args[1].variance
-                    sample_right_mean = args[1].mean
-                    degrees_of_freedom = args.flatten.size - 2
-
-                    left_root = sample_left_variance/args[0].size.to_f
-                    right_root = sample_right_variance/args[1].size.to_f
-
-                    standard_error = Math.sqrt(left_root + right_root)
-
-                    (sample_left_mean - sample_right_mean).abs/standard_error.to_f
-                  end
-
-        t_distribution = Distribution::TStudent.new(degrees_of_freedom)
-        probability = t_distribution.cumulative_function(t_score)
-
-        # Steps grabbed from https://support.minitab.com/en-us/minitab/18/help-and-how-to/statistics/basic-statistics/supporting-topics/basics/manually-calculate-a-p-value/
-        # See https://github.com/estebanz01/ruby-statistics/issues/23
-        p_value = if tails == :two_tail
-                  2 * (1 - t_distribution.cumulative_function(t_score.abs))
-                  else
-                    1 - probability
-                  end
-
-        { t_score: t_score,
-          probability: probability,
-          p_value: p_value,
-          alpha: alpha,
-          null: alpha < p_value,
-          alternative: p_value <= alpha,
-          confidence_level: 1 - alpha }
-      end
-
-      def self.paired_test(alpha, tails, left_group, right_group)
-        raise StandardError, 'both samples are the same' if left_group == right_group
-
-        # Handy snippet grabbed from https://stackoverflow.com/questions/2682411/ruby-sum-corresponding-members-of-two-or-more-arrays
-        differences = [left_group, right_group].transpose.map { |value| value.reduce(:-) }
-
-        degrees_of_freedom = differences.size - 1
-        difference_std = differences.standard_deviation
-
-        raise ZeroStdError, ZeroStdError::STD_ERROR_MSG if difference_std == 0
-
-        down = difference_std/Math.sqrt(differences.size)
-
-        t_score = (differences.mean - 0)/down.to_f
-
-        probability = Distribution::TStudent.new(degrees_of_freedom).cumulative_function(t_score)
-
-        p_value = 1 - probability
-        p_value *= 2 if tails == :two_tail
-
-        { t_score: t_score,
-          probability: probability,
-          p_value: p_value,
-          alpha: alpha,
-          null: alpha < p_value,
-          alternative: p_value <= alpha,
-          confidence_level: 1 - alpha }
-      end
-    end
-  end
-end
diff --git a/ruby-statistics/lib/statistics/statistical_test/wilcoxon_rank_sum_test.rb b/ruby-statistics/lib/statistics/statistical_test/wilcoxon_rank_sum_test.rb
deleted file mode 100644
index dcfff81247..0000000000
--- a/ruby-statistics/lib/statistics/statistical_test/wilcoxon_rank_sum_test.rb
+++ /dev/null
@@ -1,95 +0,0 @@
-module Statistics
-  module StatisticalTest
-    class WilcoxonRankSumTest
-      def rank(elements)
-        ranked_elements = {}
-
-        elements.sort.each_with_index do |element, index|
-          if ranked_elements.fetch(element, false)
-            # This allow us to solve the ties easily when performing the rank summation per group
-            ranked_elements[element][:counter] += 1
-            ranked_elements[element][:rank] += (index + 1)
-          else
-            ranked_elements[element] = { counter: 1, rank: (index + 1) }
-          end
-        end
-
-        # ranked_elements = [{ x => { counter: 1, rank: y } ]
-        ranked_elements
-      end
-
-      # Steps to perform the calculation are based on http://www.mit.edu/~6.s085/notes/lecture5.pdf
-      def perform(alpha, tails, group_one, group_two)
-        # Size for each group
-        n1, n2 = group_one.size, group_two.size
-
-        # Rank all data
-        total_ranks = rank(group_one + group_two)
-
-        # sum rankings per group
-        r1 = ranked_sum_for(total_ranks, group_one)
-        r2 = ranked_sum_for(total_ranks, group_two)
-
-        # calculate U statistic
-        u1 = (n1 * (n1 + 1)/2.0) - r1
-        u2 = (n2 * (n2 + 1)/2.0 ) - r2
-
-        u_statistic = [u1.abs, u2.abs].min
-
-        median_u = (n1 * n2)/2.0
-
-        ties = total_ranks.values.select { |element| element[:counter] > 1 }
-
-        std_u = if ties.size > 0
-                  corrected_sigma(ties, n1, n2)
-                else
-                  Math.sqrt((n1 * n2 * (n1 + n2 + 1))/12.0)
-                end
-
-        z = (u_statistic - median_u)/std_u
-
-        # Most literature are not very specific about the normal distribution to be used.
-        # We ran multiple tests with a Normal(median_u, std_u) and Normal(0, 1) and we found
-        # the latter to be more aligned with the results.
-        probability = Distribution::StandardNormal.new.cumulative_function(z.abs)
-        p_value = 1 - probability
-        p_value *= 2 if tails == :two_tail
-
-        { probability: probability,
-          u: u_statistic,
-          z: z,
-          p_value: p_value,
-          alpha: alpha,
-          null: alpha < p_value,
-          alternative: p_value <= alpha,
-          confidence_level: 1 - alpha }
-      end
-
-      # Formula extracted from http://www.statstutor.ac.uk/resources/uploaded/mannwhitney.pdf
-      private def corrected_sigma(ties, total_group_one, total_group_two)
-        n = total_group_one + total_group_two
-
-        rank_sum = ties.reduce(0) do |memo, t|
-                    memo += ((t[:counter] ** 3) - t[:counter])/12.0
-                  end
-
-        left = (total_group_one * total_group_two)/(n * (n - 1)).to_f
-        right = (((n ** 3) - n)/12.0) - rank_sum
-
-        Math.sqrt(left * right)
-      end
-
-      private def ranked_sum_for(total, group)
-        # sum rankings per group
-        group.reduce(0) do |memo, element|
-          rank_of_element = total[element][:rank] / total[element][:counter].to_f
-          memo += rank_of_element
-        end
-      end
-    end
-
-    # Both test are the same. To keep the selected name, we just alias the class
-    # with the implementation.
-    MannWhitneyU = WilcoxonRankSumTest
-  end
-end
diff --git a/ruby-statistics/lib/statistics/version.rb b/ruby-statistics/lib/statistics/version.rb
deleted file mode 100644
index 7c6cd46de5..0000000000
--- a/ruby-statistics/lib/statistics/version.rb
+++ /dev/null
@@ -1,3 +0,0 @@
-module Statistics
-  VERSION = "2.1.1"
-end
diff --git a/ruby-statistics/ruby-statistics.gemspec b/ruby-statistics/ruby-statistics.gemspec
deleted file mode 100644
index b5ce2cebf1..0000000000
--- a/ruby-statistics/ruby-statistics.gemspec
+++ /dev/null
@@ -1,34 +0,0 @@
-# coding: utf-8
-lib = File.expand_path("../lib", __FILE__)
-$LOAD_PATH.unshift(lib) unless $LOAD_PATH.include?(lib)
-require "statistics/version"
-
-Gem::Specification.new do |spec|
-  spec.name          = "ruby-statistics"
-  spec.version       = Statistics::VERSION
-  spec.authors       = ["esteban zapata"]
-  spec.email         = ["estebanz01@outlook.com"]
-
-  spec.summary       = %q{A ruby gem for som specific statistics. Inspired by the jStat js library.}
-  spec.description   = %q{This gem is intended to accomplish the same purpose as jStat js library:
-                          to provide ruby with statistical capabilities without the need
-                          of a statistical programming language like R or Octave. Some functions
-                          and capabilities are an implementation from other authors and are
-                          referenced properly in the class/method.}
-  spec.homepage      = "https://github.com/estebanz01/ruby-statistics"
-  spec.license       = "MIT"
-
-  # Prevent pushing this gem to RubyGems.org. To allow pushes either set the 'allowed_push_host'
-  # to allow pushing to a single host or delete this section to allow pushing to any host.
-  spec.files         = `git ls-files -z`.split("\x0").reject do |f|
-    f.match(%r{^(test|spec|features)/})
-  end
-  spec.bindir        = "exe"
-  spec.executables   = spec.files.grep(%r{^exe/}) { |f| File.basename(f) }
-  spec.require_paths = ["lib"]
-
-  spec.add_development_dependency "rake", '~> 12.0', '>= 12.0.0'
-  spec.add_development_dependency "rspec", '~> 3.6', '>= 3.6.0'
-  spec.add_development_dependency "grb", '~> 0.4.1', '>= 0.4.1'
-  spec.add_development_dependency 'byebug', '~> 9.1.0', '>= 9.1.0'
-end