BORG vs TimescaleDB

BORG detects spatial, temporal and clustered dependence in a modelling dataset and generates a cross-validation scheme that respects it — spatial blocks, temporal blocks, group folds — rather than letting random splits leak information between train and test. Its distinguishing choice is enforcement: when it finds dependence, random CV is blocked outright and needs an explicit allow_random=TRUE to proceed. The package also wraps the standard rsample and caret entry points so the guard applies inside existing workflows.

The entire visible history is a single day, and the sequence within it is coherent rather than churn: enforcement first, then the evidence layer, then framework integration, then idiomatic R polish. The evidence work matters to the pitch — borg_compare_cv() runs random against blocked CV so users see the inflation on their own data instead of taking the warning on faith, and the methods-text and certificate exports are aimed squarely at getting this into published papers. By the final release the interface has been rebuilt on standard S3 plot and summary methods.

The wrappers so far cover rsample and caret; tidymodels and mlr3 are the obvious remaining entry points if the guard is to reach the workflows it hasn't yet intercepted.