EntropyHub

Versión 2.0.0.1 (6,39 MB) por Matt Flood
An open-source toolkit for entropic data analysis
5.0
(7)
2K Descargas
Actualizado 24 abr 2024

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V2.0 Note:
For a list of all feature updates see the updates on www.EntropyHub.xyz.
The vast majority of EntropyHub functionality is compatible with R2016a and later. Only for some specific function calls will users require R2022a and later.
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About
EntropyHub provides a comprehensive set of functions to estimate nonlinear dynamic and information theoretic entropy statistics from time series and image data.
EntropyHub has a simple and consistent syntax that allows the user to augment several parameters at the command line, enabling a range from basic to advanced entropy statistics to be implemented with ease.
EntropyHub functions fall into 8 categories:
  • Base functions for estimating the entropy of a single univariate time series.
  • Cross functions for estimating the entropy between two univariate time series.
  • Multivariate functions for estimating the entropy of a multivariate dataset.
  • Bidimensional functions for estimating the entropy of a two-dimensional univariate matrix.
  • Multiscale functions for estimating the multiscale entropy of a single univariate time series using any of the Base entropy functions.
  • Multiscale Cross functions for estimating the multiscale entropy between two univariate time series using any of the Cross-entropy functions.
  • Multivariate Multiscale functions for estimating the multivariate multiscale entropy of multivariate dataset using any of the Multivariate-entropy functions.
  • Other Supplementary functions for various tasks related to EntropyHub and signal processing.
Terms of Use
EntropyHub is free to use by all on condition that the following reference be included on any outputs realized using the software:
Matthew W. Flood (2021),
EntropyHub: An Open-Source Toolkit for Entropic Time Series Analysis,
PLoS ONE 16(11):e0259448,
DOI: 10.1371/journal.pone.0259448
www.EntropyHub.xyz

Citar como

Flood, Matthew W., and Bernd Grimm. “EntropyHub: An Open-Source Toolkit for Entropic Time Series Analysis.” PLOS ONE, edited by Mashallah Rezakazemi, vol. 16, no. 11, Public Library of Science (PLoS), Nov. 2021, p. e0259448, doi:10.1371/journal.pone.0259448.

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Compatibilidad con la versión de MATLAB
Se creó con R2024a
Compatible con cualquier versión desde R2022a
Compatibilidad con las plataformas
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Versión Publicado Notas de la versión
2.0.0.1

+ Multivariate Sample Entropy
+ Multivariate Fuzzy Entropy
+ Multivariate Dispersion Entropy
+ Multivariate Permutation Entropy
+ Multivariate Cosine Similarity Entropy
+ Multivariate Multiscale Entropy
+ Composite Multivariate Multiscale Entropy
2.0

+ Multivariate Sample Entropy
+ Multivariate Fuzzy Entropy
+ Multivariate Dispersion Entropy
+ Multivariate Permutation Entropy
+ Multivariate Cosine Similarity Entropy
+ Multivariate Multiscale Entropy
+ Composite Multivariate Multiscale Entropy

1.0

New Functions (RangEn, DivEn)
New fuzzy membership functions
Cross entropies with different length sequences
Phase permutation entropy added
Generalized multiscale entropy added
Refined-composite multiscale fuzzy entropy added
Several bug fixes

0.2.1

License and Terms of Use updated to comply with MatLab File Exchange policies.
0.2

The update to v0.2 includes two new bidimensional entropy functions:

Bidimensional Permutation Entropy (PermEn2D)
Bidimensional Espinosa Entropy (EspEn2D)

0.1.1

The update to v0.1.1 includes a correction to the EnofEn function, allowing the user to specify the signal range (xmin, xmax) as outlined in the source literature. Other updates relate to documentation.
0.1