Publications using IDTxl

If you want to cite IDTxl in your publication, please use

P. Wollstadt, J. T. Lizier, R. Vicente, C. Finn, M. Martinez-Zarzuela, P. Mediano, L. Novelli, M. Wibral (2019). IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks. Journal of Open Source Software, 4(34), 1081. https://doi.org/10.21105/joss.01081

For an automatically compiled list of all citations, head over to Google Scholar or JOSS.

List of publications using IDTxl for data anlaysis:

• Gonzalez et al. (2021). Leading indicators for US house prices: New evidence and implications for EU financial risk managers. European Financial Management, 1-22. https://doi.org/10.1111/eufm.12325
• Wollstadt et al. (2021). Quantifying the predictability of visual scanpaths using active information storage. Entropy, 23(2), 167. https://doi.org/10.3390/e23020167
• Wiebel-Herboth et al. (2021). Measuring inter-and intra-individual differences in visual scan patterns in a driving simulator experiment using active information storage. PLoS one, 16(3), e0248166. https://doi.org/10.1371/journal.pone.0248166
• Frontera et al. (2020). Bidirectional control of fear memories by cerebellar neurons projecting to the ventrolateral periaqueductal grey. Nature Communications, 11, 5207. https://doi.org/10.1038/s41467-020-18953-0
• Sangati & Hofmann (2020). The Role of Co-Representations in Joint Tracking. ALIFE 2020: The 2020 Conference on Artificial Life, 526-534. https://doi.org/10.1162/isal_a_00259
• García-Medina & Hernández C. (2020). Network Analysis of Multivariate Transfer Entropy of Cryptocurrencies in Times of Turbulence. Entropy, 22(7), 760. https://doi.org/10.3390/e22070760
• Sych et al. (2020). Mesoscale brain dynamics reorganizes and stabilizes during learning. bioRxiv, https://doi.org/10.1101/2020.07.08.193334
• Novelli & Lizier (2020). Inferring network properties from time series via transfer entropy and mutual information: validation of bivariate versus multivariate approaches. arXiv:2007.07500 [q-bio.NC]. https://arxiv.org/abs/2007.07500
• Novelli et al. (2020). Deriving pairwise transfer entropy from network structure and motifs. Proc. Royal Soc. A, 476(2236), 20190779. https://doi.org/10.1098/rspa.2019.0779
• Pinzuti et al. (2020). Measuring spectrally-resolved information transfer for sender- and receiver-specific frequencies. PLoS Comput Biol 16(12), e1008526_. https://doi.org/10.1371/journal.pcbi.1008526
• Herzog et al. (2020). Evolving artificial neural networks with feedback. Neural Networks, 123, 153-162. https://doi.org/10.1016/j.neunet.2019.12.004
• Harmah et a;. (2020). Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy. Front Comp Neurosci, 13, 85. https://doi.org/10.3389/fncom.2019.00085
• Warrick & Hamilton (2019). Information theoretic measures of perinatal cardiotocography synchronization. Math Biosci Eng, 17(3), 2179-2192. https://doi.org/10.3934/mbe.2020116
• Novelli et al. (2019). Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing. Network Neuroscience, 3(3), 827-847. https://doi.org/10.1162/netn_a_00092

List of publications referencing IDTxl:

• Harre (2021). Information Theory for Agents in Artificial Intelligence, Psychology, and Economics. Entropy, 23(3): 310; https://doi.org/10.3390/e23030310
• Keshmiri. Conditional Entropy: A Potential Digital Marker for Stress. Entropy, 23(3), 286; https://doi.org/10.3390/e23030286
• Keshmiri (2020). Entropy and the Brain: An Overview. Entropy, 22(9), 917. https://doi.org/10.3390/e22090917
• Timme et al. (2020). A Method to Present and Analyze Ensembles of Information Sources. Entropy, 22(5), 580. https://doi.org/10.3390/e22050580
• Candadai & Izquierdo (2020). infotheory: A C++/Python package for multivariate information theoretic analysis. JOSS, 5(47), 1609. https://doi.org/10.21105/joss.01609
• Finn & Lizier (2020). Generalised Measures of Multivariate Information Content. Entropy, 22(2), 216. https://doi.org/10.3390/e22020216
• Behrendt et al. (2019). RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy. SoftwareX, 10, 100265. https://doi.org/10.1016/j.softx.2019.100265