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We introduce PyHHMM, an object-oriented open-source Python implementation ofHeterogeneous-Hidden Markov Models (HHMMs). In addition to HMM's basic corefunctionalities, such as different initialization algorithms and classicalobservations models, i.e., continuous and multinoulli, PyHHMM distinctivelyemphasizes features not supported in similar available frameworks: aheterogeneous observation model, missing data inference, different model orderselection criterias, and semi-supervised training. These characteristics resultin a feature-rich implementation for researchers working with sequential data.PyHHMM relies on the numpy, scipy, scikit-learn, and seaborn Python packages,and is distributed under the Apache-2.0 License. PyHHMM's source code ispublicly available on Github (https://github.com/fmorenopino/HeterogeneousHMM)to facilitate adoptions and future contributions. A detailed documentation(https://pyhhmm.readthedocs.io/en/latest), which covers examples of use andmodels' theoretical explanation, is available. The package can be installedthrough the Python Package Index (PyPI), via 'pip install pyhhmm'.

PyHHMM: A Python Library for Heterogeneous Hidden Markov Models