  NoMoPy is a code for fitting, analyzing, and generating noise modeled as a
hidden Markov model (HMM) or, more generally, factorial hidden Markov model
(FHMM). This code, written in Python, implements approximate and exact
expectation maximization (EM) algorithms for performing the parameter
estimation process, model selection procedures via cross-validation, and
parameter confidence region estimation. Here, we describe in detail the
functionality implemented in NoMoPy and provide examples of its use and
performance on example problems.
