Source code for the publications on "a non-linear Granger-causality framework to investigate climate–vegetation dynamics", by Papagiannopoulou et al., GMD & ERL 201
We describe a unification of old and recent ideas for formulating graphical models to explain time s...
TimeSeriesPaper_main_processing.html Html version of Jupyter notebook exemplifying main data analys...
This code was used for the following publications: Blaker, A. T., J. J.-M. Hirschi, M. J. Bell and ...
Satellite Earth observation has led to the creation of global climate data records of many important...
Tianjaou Chu, David Danks, and Clark Glymour. Data Driven Methods for Nonlinear Granger Causality: C...
We use the framework of Granger-causality testing in high-dimensional vector autoregressive models (...
Attribution studies in climate science aim for scientifically ascertaining the influence of climatic...
Inferring species interactions from time series using Granger causality and CCM analyses, comparing ...
This repository holds a collection of python notebooks that can be used to replicate the findings of...
In this paper we test for Granger causality in high-dimensional vector autoregressive models (VARs) ...
Data and code for the project "Global patterns of plant functional traits and their relationships to...
Software code to reproduce the work in the manuscript 'Causal deep learning models for studying the ...
Code and data files for : Climatic and evolutionary contexts are required to infer plant life histo...
No adequate community-wide mechanism or set of standards currently exists to ensure the long-term re...
This source code can be compiled to build the Larix vegetation simulator LAVESI. This program simula...
We describe a unification of old and recent ideas for formulating graphical models to explain time s...
TimeSeriesPaper_main_processing.html Html version of Jupyter notebook exemplifying main data analys...
This code was used for the following publications: Blaker, A. T., J. J.-M. Hirschi, M. J. Bell and ...
Satellite Earth observation has led to the creation of global climate data records of many important...
Tianjaou Chu, David Danks, and Clark Glymour. Data Driven Methods for Nonlinear Granger Causality: C...
We use the framework of Granger-causality testing in high-dimensional vector autoregressive models (...
Attribution studies in climate science aim for scientifically ascertaining the influence of climatic...
Inferring species interactions from time series using Granger causality and CCM analyses, comparing ...
This repository holds a collection of python notebooks that can be used to replicate the findings of...
In this paper we test for Granger causality in high-dimensional vector autoregressive models (VARs) ...
Data and code for the project "Global patterns of plant functional traits and their relationships to...
Software code to reproduce the work in the manuscript 'Causal deep learning models for studying the ...
Code and data files for : Climatic and evolutionary contexts are required to infer plant life histo...
No adequate community-wide mechanism or set of standards currently exists to ensure the long-term re...
This source code can be compiled to build the Larix vegetation simulator LAVESI. This program simula...
We describe a unification of old and recent ideas for formulating graphical models to explain time s...
TimeSeriesPaper_main_processing.html Html version of Jupyter notebook exemplifying main data analys...
This code was used for the following publications: Blaker, A. T., J. J.-M. Hirschi, M. J. Bell and ...
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