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Wollstadt, Patricia Lizier, Joseph Vicente, Raul Finn, Conor Martínez Zarzuela, Mario Mediano, Pedro Novelli, Leonardo Wibral, Michael 2024-01-10T11:25:06Z 2024-01-10T11:25:06Z 2019 Wollstadt et al., (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 2475-9066 https://uvadoc.uva.es/handle/10324/64344 10.21105/joss.01081 1081 34 Journal of Open Source Software 4 2475-9066 We present IDTxl (the Information Dynamics Toolkit xl), a new open source Python toolbox for effective network inference from multivariate time series using information theory, available from GitHub (https://github.com/pwollstadt/IDTxl). Information theory (Cover & Thomas, 2006; MacKay, 2003; Shannon, 1948) is the math- ematical theory of information and its transmission over communication channels. In- formation theory provides quantitative measures of the information content of a single random variable (entropy) and of the information shared between two variables (mutual information). The defined measures build on probability theory and solely depend on the probability distributions of the variables involved. As a consequence, the dependence between two variables can be quantified as the information shared between them, without the need to explicitly model a specific type of dependence. Hence, mutual information is a model-free measure of dependence, which makes it a popular choice for the analysis of systems other than communication channels. eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2019 The Author(s) Atribución 4.0 Internacional IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks info:eu-repo/semantics/article