tigramite 4.0.0-beta-foss-2018b-Python-3.6.6

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. Currently, tigramite cannot identify causal directionality for contemporaneous links which are left undirected. Also includes functions for high-quality plots of the results.

Accessing tigramite 4.0.0-beta-foss-2018b-Python-3.6.6

To load the module for tigramite 4.0.0-beta-foss-2018b-Python-3.6.6 please use this command on the BEAR systems (BlueBEAR, BEARCloud VMs, and CaStLeS VMs):

module load tigramite/4.0.0-beta-foss-2018b-Python-3.6.6

BEAR Apps Version

2020b

More Information

For more information visit the tigramite website.

Dependencies

This version of tigramite has a direct dependency on: foss/2018b matplotlib/3.0.0-foss-2018b-Python-3.6.6 networkx/2.2-foss-2018b-Python-3.6.6 Python/3.6.6-foss-2018b rpy2/3.0.5-foss-2018b-Python-3.6.6 scikit-learn/0.20.0-foss-2018b-Python-3.6.6

Last modified on 24th November 2020