The General Hidden Markov Model Library (GHMM) is a C library with additional Python bindings implementing a wide range of types of Hidden Markov Models and algorithms: discrete, continous emissions, basic training, HMM clustering, HMM mixtures.
The vision for this initiative is to offer genome analysis resources for cloud computing. This Science as a Service (ScaaS) will allow incorporation, develop and optimization of life science SW for the cloud. See http://bit.ly/JCVICloud for more details.