LIESEL - A Software Framework for Bayesian Semiparametric Distributional Regression

• Applicant: Professor Dr. Thomas Kneib
• Subject Area: Statistics and Econometrics ; Software Engineering and Programming Languages
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 443179956

LIESEL is a new software framework for research on Bayesian semiparametric distributional regression with a particular focus on modularity, extensibility, and reliability. Distributional regression has recently received considerable attention in the statistical community since it enables researchers to explore complex relations between explanatory and response variables beyond the mean, e.g. effects on distributional features such as scale, skewness, dependence, etc. LIESEL facilitates the development of statistical software in this area by bridging the gap between specialized implementations on the one and general purpose software packages for Bayesian inference on the other hand. It is both flexible enough to serve as a basis for the implementation of complex model extensions and convenient enough for quick prototyping and evaluation of statistical models and statistical inference algorithms. For this, we rely on state-of-the-art tools for all computational aspects as well as for documentation, community building, and quality assurance.LIESEL is implemented as a Python package with an R interface and provides an accessible and consistent API for the specification and estimation of distributional regression models. It is based on directed acyclic graph representations of Bayesian statistical models, which leads to a modular and comprehensible codebase. A large number of model components including non-linear, spatial, and random covariate effects, different Markov chain Monte Carlo algorithms, and other helpful tools, e.g. for model visualization and model checking, are pre-implemented in LIESEL and can be used for the development of model extensions. With LIESEL, we are contributing to the development of trustworthy statistical software, reproducible statistical research, and advances in statistical methodology by providing a well-tested and reliable basis for the implementation of model extensions and new inference algorithms.A prototype of LIESEL has been developed in the research project "Semiparametric Regression Models for Location, Scale, and Shape" and subproject "Spatio-Temporal Distributional Regression Modeling" of RTG 2300 "Enrichment of European Beech Forests with Conifers". In the proposed project, we will realize the full potential of our software framework by (i) systematically reevaluating and improving the core software components with the goal of ensuring long-term sustainability and reuse of the software, (ii) demonstrating the potential of LIESEL in several application cases conducted in collaboration with research partners, and (iii) extensively investing in dissemination and outreach activities to bring LIESEL to the attention of applied researchers. To achieve our goals, we are going to build on our considerable experience with the development of statistical software and our network of scientists that will work with the software.

• DFG Programme: Research Grants