updated docs

docs/_sources/index.rst.txt

@@ -10,8 +10,14 @@ TIGRAMITE
 
 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:
 
-1. J. Runge et al. (2018): Detecting Causal Associations in Large Nonlinear Time Series Datasets.
-   https://arxiv.org/abs/1702.07007v2
+
+0. J. Runge et al. (2019): Inferring causation from time series in Earth system sciences.
+   Nature Communications, 10(1):2553.
+   https://www.nature.com/articles/s41467-019-10105-3
+
+1. J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic (2019): Detecting and quantifying causal associations in large nonlinear time series datasets.
+   Sci. Adv. 5, eaau4996.
+   https://advances.sciencemag.org/content/5/11/eaau4996
 
 2. J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems. 
    Nature Communications, 6, 8502. 

docs/index.html

@@ -69,9 +69,13 @@ <h3 id="searchlabel">Quick search</h3>
 <h1>TIGRAMITE<a class="headerlink" href="#tigramite" title="Permalink to this headline">¶</a></h1>
 <p><a class="reference external" href="https://github.com/jakobrunge/tigramite/">Github repo</a></p>
 <p>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:</p>
-<ol class="arabic simple">
-<li><p>J. Runge et al. (2018): Detecting Causal Associations in Large Nonlinear Time Series Datasets.
-<a class="reference external" href="https://arxiv.org/abs/1702.07007v2">https://arxiv.org/abs/1702.07007v2</a></p></li>
+<ol class="arabic simple" start="0">
+<li><p>J. Runge et al. (2019): Inferring causation from time series in Earth system sciences.
+Nature Communications, 10(1):2553.
+<a class="reference external" href="https://www.nature.com/articles/s41467-019-10105-3">https://www.nature.com/articles/s41467-019-10105-3</a></p></li>
+<li><p>J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic (2019): Detecting and quantifying causal associations in large nonlinear time series datasets.
+Sci. Adv. 5, eaau4996.
+<a class="reference external" href="https://advances.sciencemag.org/content/5/11/eaau4996">https://advances.sciencemag.org/content/5/11/eaau4996</a></p></li>
 <li><p>J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems.
 Nature Communications, 6, 8502.
 <a class="reference external" href="http://doi.org/10.1038/ncomms9502">http://doi.org/10.1038/ncomms9502</a></p></li>