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TSEntropies (version 0.9)

Time Series Entropies

Description

Computes various entropies of given time series. This is the initial version that includes ApEn() and SampEn() functions for calculating approximate entropy and sample entropy. Approximate entropy was proposed by S.M. Pincus in "Approximate entropy as a measure of system complexity", Proceedings of the National Academy of Sciences of the United States of America, 88, 2297-2301 (March 1991). Sample entropy was proposed by J. S. Richman and J. R. Moorman in "Physiological time-series analysis using approximate entropy and sample entropy", American Journal of Physiology, Heart and Circulatory Physiology, 278, 2039-2049 (June 2000). This package also contains FastApEn() and FastSampEn() functions for calculating fast approximate entropy and fast sample entropy. These are newly designed very fast algorithms, resulting from the modification of the original algorithms. The calculated values of these entropies are not the same as the original ones, but the entropy trend of the analyzed time series determines equally reliably. Their main advantage is their speed, which is up to a thousand times higher. A scientific article describing their properties has been submitted to The Journal of Supercomputing and in present time it is waiting for the acceptance.

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Version

Install

install.packages('TSEntropies')

Monthly Downloads

237

Version

0.9

License

GPL-3

Maintainer

Jiri Tomcala

Last Published

October 8th, 2018

Functions in TSEntropies (0.9)

ApEn_C

ApEn_C
SampEn_C

SampEn_C
SampEn_R

SampEn_R
ApEn

ApEn
FastApEn

FastApEn
ApEn_R

ApEn_R
FastSampEn_C

FastSampEn_C
FastApEn_R

FastApEn_R
SampEn

SampEn
FastApEn_C

FastApEn_C
FastSampEn_R

FastSampEn_R
FastSampEn

FastSampEn