Welcome to `pymc-extras`

PyMC Extras extends PyMC with additional distributions, inference methods, and model transformations. It is maintained by the PyMC team and hosts functionality that is too specialized for the core library, but useful enough that you shouldn't have to write it yourself.

Highlights include:

Automatic marginalization: exact for finite discrete and conjugate variables, approximate via the Laplace approximation
Alternative inference methods: Pathfinder, DADVI, INLA, Laplace approximation, and better MAP estimation
Statespace models: SARIMAX, VARMAX, ETS, and structural time series with Kalman filtering
Additional distributions such as DiscreteMarkovChain, GeneralizedPoisson, and GenExtreme

pymc-extras mirrors the namespaces in pymc to make usage and migration as easy as possible. For example, distributions are used exactly like those in pymc:

import pymc as pm
import pymc_extras as pmx

with pm.Model():
    xi = pm.HalfNormal("xi", 0.2)
    pmx.GenExtreme("llik", mu=1, sigma=0.5, xi=xi, observed=data)

See the documentation for the full API reference.

Installation

pip install pymc-extras

or for the development version:

pip install git+https://github.com/pymc-devs/pymc-extras.git

Questions

What belongs in `pymc-extras`?

statistical methods, for example step methods or model construction helpers
distributions that are tricky to sample from or test
specialized fitting methods or distributions
any code that requires additional optimization before it can be used in practice

Functionality that proves widely useful may graduate to the main pymc repository.

What does not belong in `pymc-extras`?

Case studies
Implementations that cannot be applied generically, for example because they are tied to variables from a toy example

Contributing

We welcome contributions! Check out the contributing guidelines to get started.

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