TRACX is a new model of sequence learning. It is a connectionist autoassociator model which fits a wide range of phenomena from the infant statistical learning and adult implicit learning literature. TRACX outperforms PARSER (Perruchet & Vintner, 1998) and the simple recurrent network (SRN, Cleeremans & McClelland, 1991) in matching human sequence segmentation on existing data. More details of the model can be found in:

French, R. M., Addyman, C., & Mareschal, D. (2011). TRACX: A recognition-based connectionist framework for sequence segmentation and chunk extraction Psychological Review, 118(4), 614–636. doi:10.1037/a0025255

It also implements an improved version "TRACX 2.0" described in:

French, R. and Cottrell, G.W. (2014) TRACX 2.0: A memory-based, biologically-plausible model of sequence segmentation and chunk extraction. In Proceedings of the 36th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society pdf

This is a python implementation.