index.Rmd

---
title: "Liesel Tutorials"
author: "Hannes Riebl, Paul Wiemann"
date: "`r Sys.Date()`"
site: bookdown::bookdown_site
documentclass: book
bibliography: book.bib
url: https://liesel-devs.github.io/liesel-tutorials/
cover-image: https://avatars.githubusercontent.com/u/53620082
description: |
  Tutorial book for the Liesel probabilistic programming framework
link-citations: yes
github-repo: liesel-devs/liesel-tutorials
---

```{r bookdown, include=FALSE}
bookdown::clean_book(clean = TRUE)
dir.create(bookdown:::output_path(), recursive = TRUE)
file.create(bookdown:::output_path(".nojekyll"))
```

```{r venv, include=FALSE}
rliesel::use_liesel_venv()
```

```{python matplotlib, include=FALSE}
import matplotlib as plt

plt.rcParams.update({"figure.max_open_warning": 0})
```

```{python pandas, include=FALSE}
import re

import pandas as pd


def format_float(x):
    return re.sub(r"\.?0*$", "", f"{x:.5f}")


pd.set_option("display.float_format", format_float)

pd.set_option("display.max_rows", 30)
pd.set_option("display.max_columns", 160)
pd.set_option("display.width", 160)

pd.set_option("display.html.border", 0)


def print_df(self):
    return self.to_markdown()


pd.DataFrame.__repr__ = print_df
```

# About

Welcome to the Liesel tutorial book, which aims to illustrate the features of the Liesel probabilistic programming framework by example.

Liesel's focus is 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).

## Installation

For installation instructions, see the [README](https://github.com/liesel-devs/liesel#installation) in the main repository.

## Further reading

For a scientific discussion of the software, see our paper on arXiv (in preparation). If you are programming with Liesel, the [API documentation](https://liesel-devs.github.io/liesel) might come in handy.