MVGC
The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference. The MVGC Matlab© Toolbox approach to G-causal inference is based on multiple equivalent representations of a VAR model by (i) regression parameters, (ii) the autocovariance sequence and (iii) the cross-power spectral density of the underlying process. It features a variety of algorithms for moving between these representations, enabling selection of the most suitable algorithms with regard to computational efficiency and numerical accuracy.
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References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
Sorted by year (- Nunes, Ronaldo V.; Reyes, Marcelo B.; de Camargo, Raphael Y.: Evaluation of connectivity estimates using spiking neuronal network models (2019)
- Peró-Cebollero, Maribel; Guàrdia-Olmos, Joan; Mancho-Fora, Núria: A systematic review of simulation procedures for fMRI connectivity studies (2018)
- Lavanga, M.; De Wel, O.; Caicedo, A.; Jansen, K.; Dereymaeker, A.; Naulaers, G.; Van Huffel, S.: Monitoring effective connectivity in the preterm brain: a graph approach to study maturation (2017)
- Amblard, Pierre-Olivier: A nonparametric efficient evaluation of partial directed coherence (2015)
- Fasoli, Diego; Faugeras, Olivier; Panzeri, Stefano: A formalism for evaluating analytically the cross-correlation structure of a firing-rate network model (2015)
- Malekpour, Sheida; Sethares, William A.: Conditional Granger causality and partitioned Granger causality: differences and similarities (2015)
- Shao, Pei-Chiang; Huang, Jian-Jia; Shann, Wei-Chang; Yen, Chen-Tung; Tsai, Meng-Li; Yen, Chien-Chang: Granger causality-based synaptic weights estimation for analyzing neuronal networks (2015)
- Joseph T. Lizier: JIDT: An information-theoretic toolkit for studying the dynamics of complex systems (2014) arXiv
- Seth, Anil K.; Barrett, Adam B.; Barnett, Lionel: Causal density and integrated information as measures of conscious level (2011)