tsIMAGES is a version of tsGFCI which averages BIC scores across multiple data sets. Thus, it is used to search for a PAG (partial ancestral graph) from time series data from multiple units (subjects, countries, etc). tsIMAGES allows both for unmeasured (hidden, latent) variables and the possibility that different subjects have different causal parameters, though they share the same qualitative causal structure. As with IMAGES, the user can specify a ?penalty score? to produce more sparse models. For the traditional definition of the BIC score, set the penalty to 1.0. See the documentation for IMAGES and tsGFCI.

References:

1. Entner, D., & Hoyer, P. O. (2010). On causal discovery from time series data using FCI.
2. Proceedings of the Fifth European Workshop on Probabilistic Graphical Models, 121-128.