compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
Comparison · Analytics
hubData vs hubEvals
A side-by-side editorial comparison of hubData and hubEvals — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:hubverseepidemiologyr-package
hubData vs hubEvals: at a glance
What is hubData?
The Arrow data layer for forecast hubs, spending its releases on cloud and materialisation bugs.
hubData is the access layer for hubverse forecasting hubs, connecting to local and cloud-stored model output through Arrow and handing back lazy connections or materialised tibbles. Its releases divide sharply between schema and utility additions in the 1.x line and, more recently, a run of defect fixes in the cloud and Arrow integration. Two of those fixes involved data being silently wrong rather than an error being raised.
What is hubEvals?
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.
hubData vs hubEvals: editorial side-by-side
hubData
0.0ANALYTICS
The Arrow data layer for forecast hubs, spending its releases on cloud and materialisation bugs.
◆ Current state
hubData is the access layer for hubverse forecasting hubs, connecting to local and cloud-stored model output through Arrow and handing back lazy connections or materialised tibbles. Its releases divide sharply between schema and utility additions in the 1.x line and, more recently, a run of defect fixes in the cloud and Arrow integration. Two of those fixes involved data being silently wrong rather than an error being raised.
◆ Where it's heading
The package has largely finished adding surface and is now paying down the cost of sitting on top of Arrow and S3: ALTREP-backed columns escaping into user sessions, cloud hubs whose declared format differs from what is actually written, and metadata arrays parsing inconsistently. Each fix narrows the gap between what the storage layer does and what an R user expects. The performance-motivated default flip in 2.0.0 points the same way, prioritising large cloud hubs over conservative local behaviour.
◆ Prediction
Expect continued fixes at the Arrow and cloud boundary, particularly where declared hub configuration and actual stored format disagree, which has now produced defects twice.
hubEvals
2.5ANALYTICS
Forecast-hub scoring that learned to handle joint, sample-based predictions.
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
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.
Alternatives to hubData and hubEvals
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 hubData or hubEvals.
nabla
nabla dropped its C++ engine to chase exact derivatives at any order.
snowflakeauth
Eight months from first release to keyring caching and workload identity.
kwb.geosalz
A research-project workflow package where the interesting work is in the plumbing.
cyclocomp
A cyclomatic complexity checker that ships once every couple of years, and lands when it does.
revdbayes
Extreme value sampling in pure upkeep mode, mostly answering to Rcpp and CRAN.
See all hubData alternatives → · See all hubEvals alternatives →
Recent activity from hubData and hubEvals
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 24d agohubEvalsScored-forecast counts and faster relative skill
- 1mo agohubEvalsDisaggregated relative skill no longer aborts the whole call
- 1mo agohubDataCloud hubs declaring CSV no longer return an empty connection
- 1mo agohubEvalsSingle-model scoring returns relative skill of 1 instead of erroring
- 3mo agohubDatacollect_hub() returns plain vectors instead of ALTREP views
- 3mo agohubDataArray-valued metadata fields now parse as list columns
- 5mo agohubEvalsSample output types and multivariate compound scoring ⚡
- 6mo agohubEvalsScoring on transformed scales via transform arguments
- 7mo agohubDatadate_col parameter for oracle output schemas
- 8mo agohubDataconnect_hub() skips file validation by default (breaking)
- 10mo agohubDataArrow schema conversion and validation utilities
- 11mo agohubEvalsFirst release: score_model_out() and the scoringutils bridge
Frequently asked questions
What is the difference between hubData and hubEvals?
Both compete on the same themes — hubverse, epidemiology, r-package — 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.
Is hubData better than hubEvals?
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.
What are the best alternatives to hubData?
Top hubData alternatives in Analytics are ranked by recent ship velocity. Browse the "hubData alternatives" section above for the current picks, or visit /alternatives/hubdata for the full list with editorial commentary on each.
What are the best alternatives to hubEvals?
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.