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STeMP is a standardized protocol for reporting and guiding spatio-temporal predictive machine-learning modelling in environmental research, aimed at improving transparency and reproducibility. It is accompanied by an R package and web application, and hosted on GitHub.
This paper presents cayleyR, an R package that solves the TopSpin permutation puzzle using an iterative cycle intersection algorithm on Cayley graphs, implemented with a C++ backend and optional Vulkan GPU acceleration.
RSNet is an open-source R package that provides a resampling-based framework for robust and interpretable network inference in high-dimensional data, supporting partial correlation networks and conditional Gaussian Bayesian networks with graphlet-based topology analysis.