diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt
index de5c5351..f42bfb43 100644
--- a/docs/_sources/index.rst.txt
+++ b/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.
diff --git a/docs/index.html b/docs/index.html
index 5f49f5d6..74c1f362 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -69,9 +69,13 @@
Quick search
TIGRAMITE
Github repo
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:
-
-J. Runge et al. (2018): Detecting Causal Associations in Large Nonlinear Time Series Datasets.
-https://arxiv.org/abs/1702.07007v2
+
+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
+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
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
diff --git a/docs/searchindex.js b/docs/searchindex.js
index 53af83e6..1fe65b25 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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