Computes 46 optimized distance and similarity measures for comparing probability functions. These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.
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).
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 0.0.2 from CRANinstall.packages("philentropy")
# install.packages("devtools")# install the current version of philentropy on your systemlibrary(devtools)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.
distance(): Implements 46 fundamental probability distance (or similarity) measures
getDistMethods(): Get available method names for 'distance'
dist.diversity(): Distance Diversity between Probability Density Functions
estimate.probability(): Estimate Probability Vectors From Count Vectors
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
lin.cor(): Computes linear correlations
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
distance()when check for
colSums(x) > 1.001was peformed (leak was found with
Initial submission version.