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Liesel is a new probabilistic programming framework developed with the aim ofsupporting research on Bayesian inference based on Markov chain Monte Carlo(MCMC) simulations in general and semi-parametric regression specifications inparticular. Its three main components are (i) an R interface (RLiesel) for theconfiguration of an initial semi-parametric regression model, (ii) agraph-based model building library, where the initial model graph can bemanipulated to incorporate new research ideas, and (iii) an MCMC library fordesigning modular inference algorithms combining multiple types of well-testedand possibly customized MCMC kernels. The graph builder as well as the MCMClibrary are implemented in Python, relying on JAX as a numerical computinglibrary, and can therefore benefit from the latest machine learning technologysuch as automatic differentiation, just-in-time (JIT) compilation, and the useof high-performance computing devices such as tensor processing units (TPUs).Liesel provides all required tools for efficient and reliable statisticalresearch on complex models and estimation algorithms. Its modular design allowsusers to expand the model library and inference algorithms, offering theflexibility and customization options to tailor the software to any specificresearch needs.

Liesel: A Probabilistic Programming Framework for Developing Semi-Parametric Regression Models and Custom Bayesian Inference Algorithms