{"id":28583617,"url":"https://github.com/majianthu/transferentropy","last_synced_at":"2025-06-11T05:09:36.213Z","repository":{"id":41522380,"uuid":"316087893","full_name":"majianthu/transferentropy","owner":"majianthu","description":"Code for the paper \"Estimating Transfer Entropy via Copula Entropy\"","archived":false,"fork":false,"pushed_at":"2022-07-02T02:36:50.000Z","size":48,"stargazers_count":30,"open_issues_count":0,"forks_count":10,"subscribers_count":0,"default_branch":"main","last_synced_at":"2023-03-08T07:24:03.654Z","etag":null,"topics":["conditional-independence","copula-entropy","granger-causality","time-series-analysis","transfer-entropy"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/majianthu.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-11-26T00:54:58.000Z","updated_at":"2023-02-19T18:02:35.000Z","dependencies_parsed_at":"2022-09-12T10:12:18.853Z","dependency_job_id":null,"html_url":"https://github.com/majianthu/transferentropy","commit_stats":null,"previous_names":[],"tags_count":null,"template":null,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/majianthu%2Ftransferentropy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/majianthu%2Ftransferentropy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/majianthu%2Ftransferentropy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/majianthu%2Ftransferentropy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/majianthu","download_url":"https://codeload.github.com/majianthu/transferentropy/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/majianthu%2Ftransferentropy/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259204818,"owners_count":22821162,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["conditional-independence","copula-entropy","granger-causality","time-series-analysis","transfer-entropy"],"created_at":"2025-06-11T05:09:32.417Z","updated_at":"2025-06-11T05:09:36.198Z","avatar_url":"https://github.com/majianthu.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Estimating Transfer Entropy via Copula Entropy\nThis is the code for the preprint paper \"Estimating Transfer Entropy via Copula Entropy\" which available at [here](https://arxiv.org/abs/1910.04375). A non-parametric method for estimating Transfer Entropy via estimating three Copula Entropy terms is proposed in this paper.\n\nThe proposed method is implemented in the R and Python package '**copent**', available at \n* R : [here](https://github.com/majianthu/copent) and  [CRAN](https://cran.r-project.org/package=copent), \n* Python : [here](https://github.com/majianthu/pycopent) and [PyPI](https://pypi.org/project/copent). \n \nThe method is demonstrated in the experiment with the [UCI Beijing PM2.5 data](http://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data). The following conditional independence measures are compared in the experiment:\n* Transfer Entropy via Copula Entropy (TE) [1];\n* Conditional Distance Correlation (CDC) [2];\n* Kernel-based Conditional Independence (KCI) [3];\n* COnditional DEpendence Coefficient (CODEC) [4];\n* Generalised Covariance Measure (GCM) [5];\n* weighted Generalised Covariance Measure (wGCM) [6];\n* Kernel Partial Correlation (KPC) [7];\n* Partial Correlation (pcor);\n* Randomized conditional Correlation Test (RCoT) [8];\n* kNN based Conditional Mutual Information Estimators [9,10];\n* Fast Conditional Independence Test (fcit) [11];\n* Model-Powered Conditional Independence Test (CCIT) [12];\n* Predictive Conditional Independence Testing (PCIT) [13];\n* Conditional Kendall's Tau (CKT) [14];\n* Conditional Mean Dependence (CMD) [15];\n* Partial Copula based CI test [16].\n\nFor more comparison experiments on conditional independence measures, please refer to our paper \"Evaluating Independence and Conditional Independence Measures\" at [here](https://arxiv.org/abs/2205.07253) and the assoicated code at [here](https://github.com/majianthu/eval).\n\n## References\n1. Ma, J. Estimating Transfer Entropy via Copula Entropy. arXiv preprint arXiv:1910.04375, 2019.\n2. Wang, X.; Pan, W.; Hu, W.; Tian, Y. \u0026 Zhang, H. Conditional distance correlation. Journal of the American Statistical Association, 2015, 110, 1726-1734.\n3. Zhang, K.; Peters, J.; Janzing, D. \u0026 Schölkopf, B. Kernel-based conditional independence test and application in causal discovery. Uncertainty in Artificial Intelligence, 2011, 804-813.\n4. Azadkia, M. \u0026 Chatterjee, S. A simple measure of conditional dependence. arXiv preprint arXiv:1910.12327, 2019.\n5. Shah, R. D. \u0026 Peters, J. The hardness of conditional independence testing and the generalised covariance measure. Annals of Statistics, 2020, 48, 1514-1538.\n6. Cyrill Scheidegger, Julia Hörrmann, Peter Bühlmann. The Weighted Generalised Covariance Measure. arXiv preprint arXiv:2111.04361, 2021.\n7. Huang, Z.; Deb, N. \u0026 Sen, B. Kernel Partial Correlation Coefficient -- a Measure of Conditional Dependence. arXiv preprint arXiv:2012.14804, 2020.\n8. Strobl, E. V.; Zhang, K. \u0026 Visweswaran, S. Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery. arXiv preprint arXiv:1702.03877, 2017.\n9. Runge, J. (2018). Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information. In AISTATS'18.\n10. Octavio César Mesner, Cosma Rohilla Shalizi. Conditional Mutual Information Estimation for Mixed Discrete and Continuous Variables with Nearest Neighbors. arXiv preprint arXiv:1912.03387, 2019.\n11. Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt. Fast Conditional Independence Test for Vector Variables with Large Sample Sizes. arXiv preprint arXiv:1804.02747, 2018.\n12. Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai. Model-Powered Conditional Independence Test. NIPS 2017: 2951-2961.\n13. Samuel Burkart, Franz J Király. Predictive Independence Testing, Predictive Conditional Independence Testing, and Predictive Graphical Modelling. arXiv preprint arXiv:1711.05869, 2017.\n14. Alexis Derumigny, Jean-David Fermanian. A classification point-of-view about conditional Kendall’s tau. Computational Statistics \u0026 Data Analysis, 135, 70-94, 2019.\n15. Xiaofeng Shao, Jingsi Zhang. Martingale Difference Correlation and Its Use in High-Dimensional Variable Screening. Journal of the American Statistical Association, 109(507), 1302-1318, 2014.\n16. Petersen, L., \u0026 Hansen, N. R. Testing Conditional Independence via Quantile Regression Based Partial Copulas. Journal of Machine Learning Research, 22, 1-47, 2021.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmajianthu%2Ftransferentropy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmajianthu%2Ftransferentropy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmajianthu%2Ftransferentropy/lists"}