PID-LASSO - Matlab Tool for the computation of
Partial Information Decomposition based on LASSO Multivariate Model Identification
This Matlab toolbox allows to compute analytically the parameters of
a VAR model exploring the combined approach of LASSO regression and
State space (SS) models. Then, all the information measures composing
the Partial Information Decomposition (PID) and conditional Transfer
Entropy (cTE) are computed for multivariate stochastic process
elaborating the results provided in [1]-[2]-[3].
[1]-Antonacci, Y.; Astolfi, L.; Nollo, G.; Faes L.; Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological Networks. Entropy 2020, Sub.
[2]-Faes, L.; Marinazzo, D.; Stramaglia, S. Multiscale information decomposition: Exact computation for multivariate Gaussian processes. Entropy 2017, 19, 408.
[3]-Barnett, L.; Seth, A.K. Granger causality for state-space models. Phys. Rev. E 2015, 91, 040101.
DOWNLOAD:
Zip file with all scripts and functions: PIDlasso.zip
The code is provided free of charge. It is neither exhaustively tested nor particularly well documented. The authors accept no liability for its use. Use, modification and redistribution of the code is allowed in any way users see fit. Authors ask only that authorship is acknowledged and ref. [1] is cited upon utilization of the code in integral or partial form.
DISCLAIMER OF WARRANTIES AND LIMITATION OF LIABILITY The code is supplied as is and all use is at your own risk. The authors disclaim all warranties of any kind, either express or implied, as to the softwares, including, but not limited to, implied warranties of fitness for a particular purpose, merchantability or non - infringement of proprietary rights. Neither this agreement nor any documentation furnished under it is intended to express or imply any warranty that the operation of the software will be error - free. Under no circumstances shall the authors of the softwares provided here be liable to any user for direct, indirect, incidental, consequential, special, or exemplary damages, arising from the software, or user' s use or misuse of the softwares. Such limitation of liability shall apply whether the damages arise from the use or misuse of the data provided or errors of the software.
Contacts
Yuri Antonacci -
Department of Computer, Control and Management Engineering, Sapienza
University of Rome, Rome, Italy - yuriantonacci.89@gmail.com -
yuri.antonacci@uniroma1.it. https://github.com/YuriAntonacci
Laura Astolfi - Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
Giandomenico Nollo - Department of Industrial Engineering, University of Trento,
Italy
Luca Faes - Department of Engineering, University of Palermo, Italy - luca.faes@unipa.it
- http://www.lucafaes.net/