| FiSh-package {FiSh} | R Documentation |
FiSh: Fisher-Shannon Method
Description
Proposes non-parametric estimates of the Fisher information measure and the Shannon entropy power. More theoretical and implementation details can be found in Guignard et al. <doi:10.3389/feart.2020.00255>. A 'python' version of this work is available on 'github' and 'PyPi' ('FiShPy').
Details
If this R code is used for academic research, please cite the following paper where it was developed:
F. Guignard, M. Laib, F. Amato, M. Kanevski, Advanced analysis of temporal data using Fisher-Shannon information: theoretical development and application in geosciences, 2020, doi: 10.3389/feart.2020.00255Frontiers in Earth Science, 8:255.
Author(s)
Fabian Guignard fabian.guignard@protonmail.ch and
Mohamed Laib laib.med@gmail.com
Maintainer: Mohamed Laib laib.med@gmail.com
References
S. J. Sheather and M. C. Jones (1991). A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society, Series B, 53, 683 - 690.
M. P. Wand and M. C. Jones (1995). Kernel Smoothing. Chapman and Hall, London.
C. Vignat, J.F Bercher (2003). Analysis of signals in the Fisher–Shannon information plane, Physics Letters A, 312, 190, 27 – 33.
F. Guignard, M. Laib, F. Amato, M. Kanevski, Advanced analysis of temporal data using Fisher-Shannon information: theoretical development and application in geosciences, 2020, doi: 10.3389/feart.2020.00255Frontiers in Earth Science, 8:255.