compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
A side-by-side editorial comparison of hubEvals and hubExamples — release velocity, themes, recent moves, and the top alternatives to consider.
Forecast-hub scoring that learned to handle joint, sample-based predictions.
hubEvals scores model output from collaborative forecasting hubs, wrapping scoringutils and translating hubverse formats into forecast objects it can evaluate. The package has moved quickly from a thin translation layer to something that handles every output type the hubverse defines — quantile, mean, median, nominal and ordinal pmf, and samples. The most recent releases are almost entirely about the failure modes of relative skill scoring rather than about new metrics.
Example data for the hubverse, moving whenever the standards it demonstrates move.
hubExamples ships the reference datasets that hubverse vignettes and downstream packages use to demonstrate forecast and target data. Its releases track the hubverse specification rather than any independent roadmap: 1.0.0 exists because the target time series standard changed, and 0.1.0 because the oracle output terminology did. The current release, 1.0.1, is a single fix for column deserialisation on systems without arrow.
hubEvals scores model output from collaborative forecasting hubs, wrapping scoringutils and translating hubverse formats into forecast objects it can evaluate. The package has moved quickly from a thin translation layer to something that handles every output type the hubverse defines — quantile, mean, median, nominal and ordinal pmf, and samples. The most recent releases are almost entirely about the failure modes of relative skill scoring rather than about new metrics.
Two threads dominate. The first is coverage of output types, which reached its widest point with sample-based and compound scoring. The second, and the one occupying every recent release, is making relative skill degrade gracefully: single-model input, comparison groups with one model, and groups missing the requested baseline have each been converted from a cryptic upstream abort into a defined result. That pattern — inherited scoringutils errors being caught and given hub-specific meaning — is the clearest signal of where this package adds value.
Expect continued work smoothing scoringutils error surfaces into hub-aware behaviour, and performance attention on relative skill, which was explicitly optimised in the latest release.
hubExamples ships the reference datasets that hubverse vignettes and downstream packages use to demonstrate forecast and target data. Its releases track the hubverse specification rather than any independent roadmap: 1.0.0 exists because the target time series standard changed, and 0.1.0 because the oracle output terminology did. The current release, 1.0.1, is a single fix for column deserialisation on systems without arrow.
This is a downstream member of the hubverse package family, alongside hubUtils, hubData and hubValidations, and it moves when they define something new. The pattern across all four entries is the same: a standard changes upstream, hubExamples updates its data objects and vignettes to match. Sibling package hubAdmin has not shipped since November 2025, so the cohort is not currently in a coordinated wave.
The next release will most likely follow the next hubverse data-standard revision rather than lead it.
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either hubEvals or hubExamples.
An MLE package rebuilt around composable solvers, then renamed to match.
nabla dropped its C++ engine to chase exact derivatives at any order.
Eight months from first release to keyring caching and workload identity.
A research-project workflow package where the interesting work is in the plumbing.
A cyclomatic complexity checker that ships once every couple of years, and lands when it does.
Extreme value sampling in pure upkeep mode, mostly answering to Rcpp and CRAN.
See all hubEvals alternatives → · See all hubExamples alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package, hubverse — within Analytics. hubEvals is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. hubEvals is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top hubEvals alternatives in Analytics are ranked by recent ship velocity. Browse the "hubEvals alternatives" section above for the current picks, or visit /alternatives/hubevals for the full list with editorial commentary on each.
Top hubExamples alternatives in Analytics are ranked by recent ship velocity. Browse the "hubExamples alternatives" section above for the current picks, or visit /alternatives/hubexamples for the full list with editorial commentary on each.