Metadata-Version: 2.1
Name: pymc3-hmm
Version: 0.2.2
Summary: Hidden Markov Models in PyMC3
Home-page: http://github.com/AmpersandTV/pymc3-hmm
Maintainer: Brandon T. Willard
Maintainer-email: brandonwillard+pymc3_hmm@gmail.com
License: UNKNOWN
Description: ![Build Status](https://github.com/AmpersandTV/pymc3-hmm/workflows/PyMC3-HMM/badge.svg)
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/AmpersandTV/pymc3-hmm/main?filepath=examples)
        
        # PyMC3 HMM
        
        Hidden Markov models in [PyMC3](https://github.com/pymc-devs/pymc3).
        
        ### Features
        - Fully implemented PyMC3 `Distribution` classes for HMM state sequences (`DiscreteMarkovChain`) and mixtures that are driven by them (`SwitchingProcess`)
        - A forward-filtering backward-sampling (FFBS) implementation (`FFBSStep`) that works with NUTS&mdash;or any other PyMC3 sampler
        - A conjugate Dirichlet transition matrix sampler (`TransMatConjugateStep`)
        - Support for time-varying transition matrices in the FFBS sampler and all the relevant `Distribution` classes
        
        To use these distributions and step methods in your PyMC3 models, simply import them from the `pymc3_hmm` package.
        
        See the [examples directory](https://nbviewer.jupyter.org/github/AmpersandTV/pymc3-hmm/tree/main/examples/) for demonstrations of the aforementioned features.  You can also use [Binder](https://mybinder.org/v2/gh/AmpersandTV/pymc3-hmm/main?filepath=examples) to run the examples yourself.
        
        ## Installation
        
        Currently, the package can be installed via `pip` directly from GitHub
        ```shell
        $ pip install git+https://github.com/AmpersandTV/pymc3-hmm
        ```
        
        ## Development
        
        First, pull in the source from GitHub:
        
        ```python
        $ git clone git@github.com:AmpersandTV/pymc3-hmm.git
        ```
        
        Next, you can run `make conda` or `make venv` to set up a virtual environment.
        
        Once your virtual environment is set up, install the project, its dependencies, and the `pre-commit` hooks:
        
        ```bash
        $ pip install -r requirements.txt
        $ pre-commit install --install-hooks
        ```
        
        
        
        After making changes, be sure to run `make black` in order to automatically format the code and then `make check` to run the linters and tests.
        
        ## License
        
        [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0)
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.6
Description-Content-Type: text/markdown
