TranCIT: Transient Causal Interaction Toolbox
TranCIT: Transient Causal Interaction Toolbox
A Python package for quantifying causal relationships in multivariate time series data, with a focus on transient neural events and neuroscience applications. TranCIT implements advanced causality measures including Dynamic Causal Strength (DCS), relative Dynamic Causal Strength (rDCS), Granger causality, and transfer entropy, specifically designed for analyzing transient neural interactions.
- Max Planck Institute for Biological Cybernetics Germany
- University of Tehran Iran (Islamic Republic of)
- TU Dresden Germany
- Max Planck Society Germany
- Chinese Academy of Sciences China (People's Republic of)
magnetoencephalography, causality, time-series, transfer-entropy, computational-neuroscience, electroencephalogram, trancit, granger-causality, neuroscience, causal-inference, machine learning, statistics, transient-neural-events, multivariate-time-series, local-field-potential, robust-directed-coherence-spectroscopy
magnetoencephalography, causality, time-series, transfer-entropy, computational-neuroscience, electroencephalogram, trancit, granger-causality, neuroscience, causal-inference, machine learning, statistics, transient-neural-events, multivariate-time-series, local-field-potential, robust-directed-coherence-spectroscopy
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