YODA is a statistical toolkit for binned data, mainly used by the Rivet analysis package. Rivet takes simulated events from Monte Carlo models of physics processes, and statistically compares them to pre-recorded data via YODA: this combination is used by hundreds of physicists across experiment and theory to develop, improve, test, and rule out new models. In turn this helps the LHC to use the best possible modelling, and to target searches for new physics.
The YODA system is an attempt to do basic computational statistics objects in a new and more coherent way, clearly separating content and semantics from presentation, and emphasising the conceptual links between different types of histogram. In this way we not only improve functionality for data reinterpretation purposes, but also explore alternative ways of handling data that fit better to the requirements of future physics analyses.
A new C++ inheritance structure was added to YODA in GSoC 2020, enabling efficient and flexible binned storage and manipulation of arbitrary data types, and we now want to use this generalised design to handle multiple parallel streams of event weights in a novel way. This will enable more powerful uses of event variations, including a) estimating statistical correlations via Poisson bootstrapping, b) and transparent handling of modelling variations without need for histogram wrappers. These features will be useful for many areas of MC phenomenology, e.g. in designing new physics analyses.