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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. Mathematics provides the abstract framework to describe patterns we observe in 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 as well as traditional information theory measures. 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.


# install philentropy version 0.1.0 from CRAN



# retrieve available distance metrics
 [1] "euclidean"         "manhattan"         "minkowski"        
 [4] "chebyshev"         "sorensen"          "gower"            
 [7] "soergel"           "kulczynski_d"      "canberra"         
[10] "lorentzian"        "intersection"      "non-intersection" 
[13] "wavehedges"        "czekanowski"       "motyka"           
[16] "kulczynski_s"      "tanimoto"          "ruzicka"          
[19] "inner_product"     "harmonic_mean"     "cosine"           
[22] "hassebrook"        "jaccard"           "dice"             
[25] "fidelity"          "bhattacharyya"     "hellinger"        
[28] "matusita"          "squared_chord"     "squared_euclidean"
[31] "pearson"           "neyman"            "squared_chi"      
[34] "prob_symm"         "divergence"        "clark"            
[37] "additive_symm"     "kullback-leibler"  "jeffreys"         
[40] "k_divergence"      "topsoe"            "jensen-shannon"   
[43] "jensen_difference" "taneja"            "kumar-johnson"    
[46] "avg"
# define a probability density function P
P <- 1:10/sum(1:10)
# define a probability density function Q
Q <- 20:29/sum(20:29)

# combine P and Q as matrix object
x <- rbind(P,Q)

# compute the jensen-shannon distance between
# probability density functions P and Q
distance(x, method = "jensen-shannon")
jensen-shannon using unit 'log'.

Install Developer Version

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


The current status of the package as well as a detailed history of the functionality of each version of philentropy can be found in the NEWS section.

Important Functions

Distance Measures

Information Theory

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:

or find me on twitter: HajkDrost