This is an implementation of CCM and DMV (e.g. [1,2]), with extensions to run on two input streams.
The library is called "predictabilityParsing" because one of the streams will be words, and the
other will be (a quantization of) word duration. The idea is to learn something about syntactic
structure by exploiting predictability effects (e.g. [3]). That is why this library is called
"predictabilityParsing."

[1] (2002). Klein, D., & Manning, C. A Generative Constituent-Context Model for Improved Grammar Induction, Dan Klein and Chris Manning,
In Proceedings of the Association for Computational Linguistics (ACL).

[2] (2004). Klein, D., & Manning, C. Corpus-Based Induction of Syntactic Structure: Models of
Dependency and Constituency, In Proceedings of the Association for Computational Linguistics (ACL).

[3] (2006). Gahl, S., Garnsey, S., Fisher, C. & Matzen, L. "That sounds unlikely": Syntactic
probabilities affect pronunciation. Proceedings of the 28th Annual Conference of the Cognitive
Science Society.


There are a few elaborations on these models as well.