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 the form of probability distributions that either follow fixed patterns (parametric distributions) or more dynamic ones (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 it will be useful to those who share it with me.
# install philentropy version 0.10.0 from CRAN
install.packages("philentropy")Or get the latest developer version:
# install.packages("devtools")
library(devtools)
install_github("HajkD/philentropy", build_vignettes = TRUE, dependencies = TRUE)HG Drost (2018).
Philentropy: Information Theory and Distance Quantification with R.
Journal of Open Source Software, 3(26), 765.
https://doi.org/10.21105/joss.00765
🪶 I am developing
philentropyin my spare time and would be very grateful if you would consider citing the paper above if it was useful for your research. These citations help me continue maintaining and extending the package.
library(philentropy)
P <- c(0.1, 0.2, 0.7)
Q <- c(0.2, 0.2, 0.6)
distance(rbind(P, Q), method = "jensen-shannon")jensen-shannon using unit 'log'.
jensen-shannon
0.02628933💡 Tip: Got a large matrix (rows = samples, cols = features)?
Usedistance(X, method="cosine", mute.message=TRUE)to compute the full pairwise matrix quickly and quietly.
| Goal | Recommended Methods |
|---|---|
| 🔁 Clustering / similarity | cosine, correlation,
euclidean |
| 📊 Probability or compositional data | jensen-shannon, hellinger,
kullback-leibler |
| 🧬 Sparse counts / binary | canberra, jaccard,
sorensen |
| ⚖️ Scale-invariant | manhattan, chebyshev |
Run
getDistMethods()to explore all 50+ implemented measures.
library(philentropy)
philentropy::getDistMethods()# define probability density functions P and Q
P <- 1:10/sum(1:10)
Q <- 20:29/sum(20:29)
x <- rbind(P, Q)
philentropy::distance(x, method = "jensen-shannon")jensen-shannon using unit 'log'.
jensen-shannon
0.02628933Alternatively, compute all available distances:
philentropy::dist.diversity(x, p = 2, unit = "log2")Flagship examples with top venues. Click to expand full lists.
🎓 philentropy has been used in dozens of peer-reviewed publications to quantify distances, divergences, and similarities in complex biological and computational datasets.
distance() – Implements 46 probability
distance/similarity measuresgetDistMethods() – Get all method names for
distancedist.diversity() – Computes distance diversity between
PDFsestimate.probability() – Estimate probability vectors
from countsH() – Shannon’s Entropy H(X)JE() – Joint Entropy H(X,Y)CE() – Conditional Entropy H(X|Y)MI() – Mutual Information I(X,Y)KL() – Kullback–Leibler DivergenceJSD() – Jensen–Shannon DivergencegJSD() – Generalized Jensen–Shannon DivergenceFind the current status and version history in the
👉 NEWS
section.
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