# pymc-elicito **Repository Path**: mirrors_pymc-devs/pymc-elicito ## Basic Information - **Project Name**: pymc-elicito - **Description**: Expert prior elicitation with PyMC - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-11-15 - **Last Updated**: 2026-06-06 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # PyMC elicito: A Python package for expert prior elicitation using PyMC **Key info :** [![Docs](https://readthedocs.org/projects/pymc-elicito/badge/?version=latest)](https://pymc-elicito.readthedocs.io) [![Main branch: supported Python versions](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fpymc-devs%2Fpymc-elicito%2Fmain%2Fpyproject.toml)](https://github.com/pymc-devs/pymc-elicito/blob/main/pyproject.toml) [![Licence](https://img.shields.io/badge/license-Apache%20License%202.0-blue)](https://github.com/pymc-devs/pymc-elicito/blob/main/LICENCE) **PyPI :** in progress **Conda :** in progress **Tests :** [![CI](https://github.com/pymc-devs/pymc-elicito/actions/workflows/ci.yaml/badge.svg?branch=main)](https://github.com/pymc-devs/pymc-elicito/actions/workflows/ci.yaml) [![Coverage](https://codecov.io/gh/pymc-devs/pymc-elicito/branch/main/graph/badge.svg)](https://codecov.io/gh/pymc-devs/pymc-elicito) **Other info :** [![Last Commit](https://img.shields.io/github/last-commit/pymc-devs/pymc-elicito.svg)](https://github.com/pymc-devs/pymc-elicito/commits/main) [![Contributors](https://img.shields.io/github/contributors/pymc-devs/pymc-elicito.svg)](https://github.com/pymc-devs/pymc-elicito/graphs/contributors) ## Status **Prototype**: This project is just starting up and the code is all prototype. Pymc-elicito re-implements the Python package [`elicito`](https://github.com/florence-bockting/elicito) using pymc instead of tensorflow(-probability) as dependency. ## Description **Expert prior elicitation** aims to define prior distributions for parameters within a Bayesian model that accurately incorporate the expectations of a domain expert. The `elicito` computational framework supports the modular implementation of diverse expert prior elicitation methods. Full documentation can be found at: [pymc-elicito.readthedocs.io](https://pymc-elicito.readthedocs.io/en/latest/). We recommend reading the docs there because the internal documentation links don't render correctly on GitHub's viewer. ## Installation ### As an application If you want to use PyMC elicito as an application, then we recommend using the 'locked' version of the package. This version pins the version of all dependencies too, which reduces the chance of installation issues because of breaking updates to dependencies. The locked version of PyMC elicito can be installed with === "mamba" ```sh mamba install -c conda-forge pymc-elicito-locked ``` === "conda" ```sh conda install -c conda-forge pymc-elicito-locked ``` === "pip" ```sh pip install 'pymc-elicito[locked]' ``` ### As a library If you want to use PyMC elicito as a library, for example you want to use it as a dependency in another package/application that you're building, then we recommend installing the package with the commands below. This method provides the loosest pins possible of all dependencies. This gives you, the package/application developer, as much freedom as possible to set the versions of different packages. However, the tradeoff with this freedom is that you may install incompatible versions of PyMC elicito's dependencies (we cannot test all combinations of dependencies, particularly ones which haven't been released yet!). Hence, you may run into installation issues. If you believe these are because of a problem in PyMC elicito, please [raise an issue](https://github.com/pymc-devs/pymc-elicito/issues). The (non-locked) version of PyMC elicito can be installed with === "mamba" ```sh mamba install -c conda-forge pymc-elicito ``` === "conda" ```sh conda install -c conda-forge pymc-elicito ``` === "pip" ```sh pip install pymc-elicito ``` Additional dependencies can be installed using === "mamba" If you are installing with mamba, we recommend installing the extras by hand because there is no stable solution yet (see [conda issue #7502](https://github.com/conda/conda/issues/7502)) === "conda" If you are installing with conda, we recommend installing the extras by hand because there is no stable solution yet (see [conda issue #7502](https://github.com/conda/conda/issues/7502)) === "pip" ```sh # To add plotting dependencies pip install 'pymc-elicito[plots]' # To add all optional dependencies pip install 'pymc-elicito[full]' ``` ### For developers For development, we rely on [uv](https://docs.astral.sh/uv/) for all our dependency management. To get started, you will need to make sure that uv is installed ([instructions here](https://docs.astral.sh/uv/getting-started/installation/) (we found that the self-managed install was best, particularly for upgrading uv later). For all of our work, we use our `Makefile`. You can read the instructions out and run the commands by hand if you wish, but we generally discourage this because it can be error prone. In order to create your environment, run `make virtual-environment`. If there are any issues, the messages from the `Makefile` should guide you through. If not, please raise an issue in the [issue tracker](https://github.com/pymc-devs/pymc-elicito/issues). For the rest of our developer docs, please see [development][development]. ## Original template This project was generated from this template: [copier core python repository](https://gitlab.com/openscm/copier-core-python-repository). [copier](https://copier.readthedocs.io/en/stable/) is used to manage and distribute this template.