Merge pull request #175 from jakobrunge/developer

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jakobrunge · Feb 23, 2022 · 0c6a023 · 0c6a023
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diff --git a/.travis.yml b/.travis.yml
@@ -2,8 +2,8 @@ language: python
 python:
   # We don't actually use the Travis Python since we are using Conda, but this
   # keeps it organized.
-  - "3.6"
   - "3.7"
+  - "3.9"
 install:
   - sudo apt-get update
   # We do this conditionally because it saves us some downloading if the

diff --git a/FUNDING.yml b/FUNDING.yml
@@ -0,0 +1,4 @@
+# repo:     https://github.com/jakobrunge/tigramite
+# filename: FUNDING.yml
+
+github: jakobrunge
diff --git a/MANIFEST.in b/MANIFEST.in
diff --git a/README.md b/README.md
@@ -1,17 +1,19 @@
-# TIGRAMITE – Causal discovery for time series datasets
-Version 4.2
+# Tigramite – Causal discovery for time series datasets
+Version 5.0
 
 (Python Package)
 
 [Github](https://github.com/jakobrunge/tigramite.git)
 
 [Documentation](https://jakobrunge.github.io/tigramite/)
 
---> [Take a survey to improve tigramite](https://forms.gle/bXdj5vWkSDB7yN9u7) <--
+[Simple GUI](https://github.com/stbachinger/TigramiteGui)
 
 ## Overview
 
-Tigramite provides several causal discovery methods that can be used under different sets of assumptions. An application always consists of a method and a chosen conditional independence test, e.g. PCMCI together with ParCorr. The following two tables give an overview of the assumptions involved:
+__Update:__ Tigramite now has a new CausalEffects class that allows to estimate (conditional) causal effects and mediation based on a causal graph. Have a look at the tutorial.
+
+Further, Tigramite provides several causal discovery methods that can be used under different sets of assumptions. An application always consists of a method and a chosen conditional independence test, e.g. PCMCI together with ParCorr. The following two tables give an overview of the assumptions involved:
 
 | Method | Assumptions         | Output |
 |--------|---------------------------------------------------------------------------|----|
@@ -23,25 +25,24 @@ Tigramite provides several causal discovery methods that can be used under diffe
 
 | Conditional independence test | Assumptions                                  |
 |--------|---------------------------------------------------------------------------|
-| ParCorr  | univariate, continuous, linear Gaussian dependencies |
-| GPDC / GPDCtorch | univariate, continuous, additive dependencies        |
-| CMIknn | multivariate, continuous, general dependencies           |
-| CMIsymb | univariate, discrete/categorical dependencies           |
+| ParCorr  | univariate variables with continuous, linear Gaussian dependencies |
+| GPDC / GPDCtorch | univariate variables with continuous, additive dependencies        |
+| CMIknn | multivariate variables with continuous, general dependencies           |
+| CMIsymb | univariables with discrete/categorical dependencies           |
 
 ## General Notes
 
-Tigramite is a causal time series analysis python package. It allows to
- efficiently reconstruct causal graphs from high-dimensional time series datasets and model the obtained causal dependencies for causal mediation and prediction analyses. 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:
+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. You can find a simple GUI version on https://github.com/stbachinger/TigramiteGui. Please cite the following papers depending on which method you use:
 
 - 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
-- 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
+- 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
 - 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
 - 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
-
+- 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. https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html
 
 ## Features
 
@@ -53,32 +54,33 @@ Tigramite is a causal time series analysis python package. It allows to
 - parallel computing script based on mpi4py
 - handling of missing values and masks
 - p-value correction and confidence interval estimation
-- causal mediation class to analyze causal pathways
+- causal effect class to  non-parametrically estimate (conditional) causal effects and also linear mediated causal effects
 - prediction class based on sklearn models including causal feature selection
+- GUI covering basic functionality on https://github.com/stbachinger/TigramiteGui
 
 
 ## Required python packages
 
-- numpy>=1.17.0
-- scipy>=1.3.0
-- scikit-learn>=0.21   (optional, necessary for GPDC test)
-- matplotlib>=3.4.0 (optional, only for plotting)
-- networkx>=2.4 (optional, only for plotting and mediation)
-- cython>=0.29.12   (optional, necessary for CMIknn)
-- mpi4py>=3.0.3   (optional, necessary for using the parallelized script)
-- dcor>=0.5.3     (optional, necessary for GPDC)
-- gpytorch>=1.4 (optional, necessary for GPDCtorch implementation)
+- python=3.7/3.8/3.9
+- numpy
+- scipy
+- numba
 
+## Optional packages depending on used functions
+- scikit-learn>=0.21  # Gaussian Process (GP) Regression
+- matplotlib>=3.4.0   # Plotting
+- networkx>=2.4       # Plotting
+- torch>=1.7          # GPDC torch version
+- gpytorch>=1.4       # GPDC torch version
+- dcor>=0.5.3         # GPDC distance correlation version
 
 ## Installation
 
 python setup.py install
 
 This will install tigramite in your path.
 
-To use just the ParCorr and CMIsymb independence tests, only numpy and scipy are required. For other independence tests more packages are required:
-
-- CMIknn: cython can optionally be used for compilation, otherwise the provided ``*.c'' file is used 
+To use just the ParCorr, CMIknn, and CMIsymb independence tests, only numpy/numba and scipy are required. For other independence tests more packages are required:
 
 - GPDC: scikit-learn is required for Gaussian Process regression and dcor for distance correlation
 
@@ -94,7 +96,7 @@ You commit to cite above papers in your reports or publications.
 
 ## License
 
-Copyright (C) 2014-2020 Jakob Runge
+Copyright (C) 2014-2022 Jakob Runge
 
 See license.txt for full text.
 

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