Code for partially conditioned Granger causality using the covariance matrix (see below for other estimators though).
This function implements the method described in
Marinazzo et al, Causal information approach to partial conditioning in multivariate data sets, Comput Math Methods Med. 2012;2012:303601. doi: 10.1155/2012/303601
https://www.hindawi.com/journals/cmmm/2012/303601/
Instructions are in the pdf file.
For example the classical Mutual Information, or the Kernel Granger Causality (ht, tps://journals.aps.org/pre/abstract/10.1103/PhysRevE.77.056215, ht)tps://journals.aps.org/prl/abstract/10.1103/PhysRevLett.100.144103, https://github.com/danielemarinazzo/KernelGrangerCausality).
These files (still in beta version, are those containing MIexact or kernel in their names.
Please do not hesitate to contact us for suggestions and remarks http://ost.io/@danielemarinazzo
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