<!-- This README is for PyPI. The README for GitHub is in .github/README.md -->
# Liesel: A Probabilistic Programming Framework
<img src="https://raw.githubusercontent.com/liesel-devs/liesel/main/misc/logo/logo-light.png" alt="logo" align="right" width="185">
Liesel is a probabilistic programming framework with a focus on semi-parametric regression. It includes:
- [**Liesel**](https://github.com/liesel-devs/liesel), a library to express statistical models as Probabilistic Graphical Models (PGMs). Through the PGM representation, the user can build and update models in a natural way.
- **Goose**, a library to build custom MCMC algorithms with several parameter blocks and MCMC kernels such as the No U-Turn Sampler (NUTS), the Iteratively Weighted Least Squares (IWLS) sampler, or different Gibbs samplers. Goose also takes care of the MCMC bookkeeping and the chain post-processing.
- [**RLiesel**](https://github.com/liesel-devs/rliesel), an R interface for Liesel which assists the user with the configuration of semi-parametric regression models such as Generalized Additive Models for Location, Scale and Shape (GAMLSS) with different response distributions, spline-based smooth terms and shrinkage priors.
The name "Liesel" is an homage to the [Gänseliesel fountain](https://en.wikipedia.org/wiki/G%C3%A4nseliesel), landmark of Liesel's birth city [Göttingen](https://en.wikipedia.org/wiki/G%C3%B6ttingen).
## Resources
- [Paper on arXiv](https://arxiv.org/abs/2209.10975)
- [Liesel & Goose repo](https://github.com/liesel-devs/liesel)
- [Liesel & Goose API docs](https://docs.liesel-project.org)
- [RLiesel repo](https://github.com/liesel-devs/rliesel)
- [Tutorials](https://docs.liesel-project.org/en/latest/tutorials_overview.html)
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"description": "<!-- This README is for PyPI. The README for GitHub is in .github/README.md -->\n\n# Liesel: A Probabilistic Programming Framework\n\n<img src=\"https://raw.githubusercontent.com/liesel-devs/liesel/main/misc/logo/logo-light.png\" alt=\"logo\" align=\"right\" width=\"185\">\n\nLiesel is a probabilistic programming framework with a focus on semi-parametric regression. It includes:\n\n- [**Liesel**](https://github.com/liesel-devs/liesel), a library to express statistical models as Probabilistic Graphical Models (PGMs). Through the PGM representation, the user can build and update models in a natural way.\n- **Goose**, a library to build custom MCMC algorithms with several parameter blocks and MCMC kernels such as the No U-Turn Sampler (NUTS), the Iteratively Weighted Least Squares (IWLS) sampler, or different Gibbs samplers. Goose also takes care of the MCMC bookkeeping and the chain post-processing.\n- [**RLiesel**](https://github.com/liesel-devs/rliesel), an R interface for Liesel which assists the user with the configuration of semi-parametric regression models such as Generalized Additive Models for Location, Scale and Shape (GAMLSS) with different response distributions, spline-based smooth terms and shrinkage priors.\n\nThe name \"Liesel\" is an homage to the [G\u00e4nseliesel fountain](https://en.wikipedia.org/wiki/G%C3%A4nseliesel), landmark of Liesel's birth city [G\u00f6ttingen](https://en.wikipedia.org/wiki/G%C3%B6ttingen).\n\n## Resources\n\n- [Paper on arXiv](https://arxiv.org/abs/2209.10975)\n- [Liesel & Goose repo](https://github.com/liesel-devs/liesel)\n- [Liesel & Goose API docs](https://docs.liesel-project.org)\n- [RLiesel repo](https://github.com/liesel-devs/rliesel)\n- [Tutorials](https://docs.liesel-project.org/en/latest/tutorials_overview.html)\n",
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