Deprecated: Use of this version of tigramite is deprecated. More information on our Applications Support and Retention Policy.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
The listed architectures consist of two part: OS-CPU.
- BlueBEAR: The OS used on BlueBEAR is represented by EL and there are several different processor (CPU) types available on BlueBEAR. More information about the processor types on BlueBEAR is available on the BlueBEAR Job Submission page.
- BEAR and CaStLeS Cloud VMs: These VMs can have one of two OSes. Those with access to a BEAR Cloud or CaStLeS VM should check that the listed architectures for an application include the OS of VM being used. The VMs, irrespective of OS, will use the haswell CPU type.
For more information visit the tigramite website.
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