.. Tigramite documentation master file, created by sphinx-quickstart on Wed Oct 5 18:51:08 2023. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. Welcome to Tigramite's documentation! ===================================== .. toctree:: :maxdepth: 2 :caption: Contents: Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` .. Tigramite documentation master file, created by sphinx-quickstart on Thu May 11 18:32:05 2017. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. TIGRAMITE ========= `Github repo `_ Tigramite is a causal time series analysis python package. It allows to efficiently estimate causal graphs from high-dimensional time series datasets (causal discovery) and to use these graphs for robust forecasting and the estimation and prediction of direct, total, and mediated effects. Causal discovery is based on linear as well as non-parametric conditional independence tests applicable to discrete or continuously-valued time series. Also includes functions for high-quality plots of the results. Please cite the following papers depending on which method you use: - Overview: Runge, J., Gerhardus, A., Varando, G. et al. Causal inference for time series. Nat Rev Earth Environ (2023). https://doi.org/10.1038/s43017-023-00431-y - PCMCI: J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019). https://advances.sciencemag.org/content/5/11/eaau4996 - PCMCI+: J. Runge (2020): Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020,Toronto, Canada, 2019, AUAI Press, 2020. http://auai.org/uai2020/proceedings/579_main_paper.pdf - LPCMCI: Gerhardus, A. & Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders Advances in Neural Information Processing Systems, 2020, 33. https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html - RPCMCI: Elena Saggioro, Jana de Wiljes, Marlene Kretschmer, Jakob Runge; Reconstructing regime-dependent causal relationships from observational time series. Chaos 1 November 2020; 30 (11): 113115. https://doi.org/10.1063/5.0020538 - JPCMCIplus: W. Günther, U. Ninad, J. Runge, Causal discovery for time series from multiple datasets with latent contexts. UAI 2023 - Generally: J. Runge (2018): Causal Network Reconstruction from Time Series: From Theoretical Assumptions to Practical Estimation. Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (7): 075310. https://aip.scitation.org/doi/10.1063/1.5025050 - Nature Communications Perspective paper: https://www.nature.com/articles/s41467-019-10105-3 - Causal effects: J. Runge, Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables, Advances in Neural Information Processing Systems, 2021, 34 - Mediation class: J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502. http://doi.org/10.1038/ncomms9502 - Mediation class: J. Runge (2015): Quantifying information transfer and mediation along causal pathways in complex systems. Phys. Rev. E, 92(6), 62829. http://doi.org/10.1103/PhysRevE.92.062829 - CMIknn: J. Runge (2018): Conditional Independence Testing Based on a Nearest-Neighbor Estimator of Conditional Mutual Information. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. http://proceedings.mlr.press/v84/runge18a.html .. toctree:: :maxdepth: 2 :caption: Contents: .. autosummary:: tigramite.pcmci.PCMCI tigramite.lpcmci.LPCMCI tigramite.rpcmci.RPCMCI tigramite.jpcmciplus.JPCMCIplus tigramite.independence_tests.independence_tests_base.CondIndTest tigramite.independence_tests.parcorr.ParCorr tigramite.independence_tests.robust_parcorr.RobustParCorr tigramite.independence_tests.gpdc.GPDC tigramite.independence_tests.gpdc_torch.GPDCtorch tigramite.independence_tests.cmiknn.CMIknn tigramite.independence_tests.cmisymb.CMIsymb tigramite.independence_tests.oracle_conditional_independence.OracleCI tigramite.independence_tests.parcorr_mult.ParCorrMult tigramite.independence_tests.gsquared.Gsquared tigramite.independence_tests.parcorr_wls.ParCorrWLS tigramite.independence_tests.regressionCI.RegressionCI tigramite.causal_effects.CausalEffects tigramite.models.Models tigramite.models.LinearMediation tigramite.models.Prediction tigramite.data_processing tigramite.toymodels.structural_causal_processes tigramite.plotting :mod:`tigramite.pcmci`: PCMCI =========================================== .. autoclass:: tigramite.pcmci.PCMCI :members: :mod:`tigramite.lpcmci`: LPCMCI =========================================== .. autoclass:: tigramite.lpcmci.LPCMCI :members: :mod:`tigramite.rpcmci`: RPCMCI =========================================== .. autoclass:: tigramite.rpcmci.RPCMCI :members: :mod:`tigramite.jpcmciplus`: JPCMCIplus =========================================== .. autoclass:: tigramite.jpcmciplus.JPCMCIplus :members: :mod:`tigramite.independence_tests`: Conditional independence tests ================================================================================= Base class: .. autoclass:: tigramite.independence_tests.independence_tests_base.CondIndTest :members: Test statistics: .. autoclass:: tigramite.independence_tests.parcorr.ParCorr :members: .. autoclass:: tigramite.independence_tests.robust_parcorr.RobustParCorr :members: .. autoclass:: tigramite.independence_tests.gpdc.GPDC :members: .. autoclass:: tigramite.independence_tests.gpdc_torch.GPDCtorch :members: .. autoclass:: tigramite.independence_tests.cmiknn.CMIknn :members: .. autoclass:: tigramite.independence_tests.cmisymb.CMIsymb :members: .. autoclass:: tigramite.independence_tests.oracle_conditional_independence.OracleCI :members: .. autoclass:: tigramite.independence_tests.parcorr_mult.ParCorrMult :members: .. autoclass:: tigramite.independence_tests.gsquared.Gsquared :members: .. autoclass:: tigramite.independence_tests.parcorr_wls.ParCorrWLS :members: .. autoclass:: tigramite.independence_tests.regressionCI.RegressionCI :members: :mod:`tigramite.causal_effects`: Causal Effect analysis =========================================================== .. autoclass:: tigramite.causal_effects.CausalEffects :members: :mod:`tigramite.models`: Time series modeling, mediation, and prediction ======================================================================== Base class: .. autoclass:: tigramite.models.Models :members: Derived classes: .. autoclass:: tigramite.models.LinearMediation :members: .. autoclass:: tigramite.models.Prediction :members: :mod:`tigramite.data_processing`: Data processing functions =========================================================== .. automodule:: tigramite.data_processing :members: :mod:`tigramite.toymodels`: Toy model generators =========================================================== .. automodule:: tigramite.toymodels.structural_causal_processes :members: :mod:`tigramite.plotting`: Plotting functions ============================================= .. automodule:: tigramite.plotting :members: Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search`