philentropy

Similarity and Distance Quantification between Probability Functions

Describe and understand the world through data.

Data collection and data comparison are the foundations of scientific research. This scientific method allows us to infer natural patterns and enables us to understand the world around us. Mathematics provides the abstract framework to encode the patterns we learned from nature and Statistics provides the framework to quantify the uncertainty of these patterns. In statistics natural patterns are described in form of probability distributions which either follow a fixed pattern (parametric distributions) or more dynamic patterns (non-parametric distributions).

The philentropy package implements fundamental distance and similarity measures to quantify distances between probability density functions. In this regard, it aims to provide a framework for comparing natural patterns in a statistical notation.

This project is born out of my passion for statistics and I hope that it will be useful to the people who share it with me.

Tutorials

• Introduction
• Distances and Similarity Measures
• Information Theory

Installation

# install.packages("devtools")

# install the current version of philentropy on your system
library(devtools)
install_github("HajkD/philentropy", build_vignettes = TRUE, dependencies = TRUE)

# On Windows, this won't work - see ?build_github_devtools
install_github("HajkD/philentropy", build_vignettes = TRUE, dependencies = TRUE)

# When working with Windows, first you need to install the
# R package: rtools -> install.packages("rtools")

# Afterwards you can install devtools -> install.packages("devtools")
# and then you can run:

devtools::install_github("HajkD/philentropy", build_vignettes = TRUE, dependencies = TRUE)

# and then call it from the library
library("philentropy", lib.loc = "C:/Program Files/R/R-3.1.1/library")

Important Functions

Distance Measures

• distance() : implements 46 fundamental probability distance (or similarity) measures

Information Theory

• H() : Shannon's Entropy H(X)
• JE() : Joint-Entropy H(X,Y)
• CE() : Conditional-Entropy H(X | Y)
• MI() : Shannon's Mutual Information I(X,Y)
• KL() : Kullback–Leibler Divergence
• JSD() : Jensen-Shannon Divergence
• gJSD() : Generalized Jensen-Shannon Divergence

Correlation Analyses

• lin.cor() : Computes linear correlations

Discussions and Bug Reports

I would be very happy to learn more about potential improvements of the concepts and functions provided in this package.

Furthermore, in case you find some bugs or need additional (more flexible) functionality of parts of this package, please let me know:

https://github.com/HajkD/philentropy/issues

or find me on twitter: HajkDrost