Package 'kdpee'

Title: Fast Multidimensional Entropy Estimation by k-d Partitioning
Description: Estimate entropy of multidimensional data set. Compatibility updates by Renova.
Authors: Olaf Mersmann [aut, cre] , Dan Stowell [aut, cph], Queen Mary University of London [cph]
Maintainer: Olaf Mersmann <[email protected]>
License: GPL (>= 3)
Version: 1.0.0.9001
Built: 2026-11-13 17:47:30 UTC
Source: https://github.com/renova-r/kdpee

Help Index

Fast Entropy Estimation of Multi-Dimensional Data

Description

Non-parametric estimator for the differential entropy of a multidimensional distribution, given a limited set of data points, by a recursive rectilinear partitioning.

Usage

kdpee(X, ci = 0.95, lower = apply(X, 2, min), upper = apply(X, 2, max))

Arguments

X

[matrix]
Data, one observation per row.
ci

[numeric(1)]
Confidence threshold used to decide if a cell should be divided further. Defaults to 95%.
lower

[numeric(n)]
Lower bound of the support of X.
upper

[numeric(n)]
Upper bound of the support of X.

Value

Differential entropy estimate.

References

D. Stowell and M. D. Plumbley Fast multidimensional entropy estimation by k-d partitioning. IEEE Signal Processing Letters 16 (6), 537–540, June 2009. http://dx.doi.org/10.1109/LSP.2009.2017346

Examples

Xu <- matrix(runif(1000 * 100), ncol=100)
kdpee(Xu)

Xn <- matrix(rnorm(1000 * 100), ncol=100)
kdpee(Xn)