{Reference Type}: Journal Article
{Title}: hopsy - a methods marketplace for convex polytope sampling in Python.
{Author}: Paul RD;Jadebeck JF;Stratmann A;Wiechert W;Nöh K;
{Journal}: Bioinformatics
{Volume}: 40
{Issue}: 7
{Year}: 2024 Jul 1
{Factor}: 6.931
{DOI}: 10.1093/bioinformatics/btae430
{Abstract}: <B>CONCLUSIONS: </B>Effective collaboration between developers of Bayesian inference methods and users is key to advance our quantitative understanding of biosystems. We here present hopsy, a versatile open-source platform designed to provide convenient access to powerful Markov chain Monte Carlo sampling algorithms tailored to models defined on convex polytopes (CP). Based on the high-performance C++ sampling library HOPS, hopsy inherits its strengths and extends its functionalities with the accessibility of the Python programming language. A versatile plugin-mechanism enables seamless integration with domain-specific models, providing method developers with a framework for testing, benchmarking, and distributing CP samplers to approach real-world inference tasks. We showcase hopsy by solving common and newly composed domain-specific sampling problems, highlighting important design choices. By likening hopsy to a marketplace, we emphasize its role in bringing together users and developers, where users get access to state-of-the-art methods, and developers contribute their own innovative solutions for challenging domain-specific inference problems.<BR><B>METHODS: </B>Sources, documentation and a continuously updated list of sampling algorithms are available at https://jugit.fz-juelich.de/IBG-1/ModSim/hopsy, with Linux, Windows and MacOS binaries at https://pypi.org/project/hopsy/.