compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
Comparison · Analytics
hubEvals vs tulpaObs
A side-by-side editorial comparison of hubEvals and tulpaObs — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
hubEvals vs tulpaObs: at a glance
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.
What is tulpaObs?
An occupancy-modeling package that just deleted its own duplicate vocabulary for diagnostics.
tulpaObs is the ecological occupancy and abundance modeling layer built on the tulpa engine, releasing at high frequency and with version numbers that do not advance monotonically in publication order. The current window covers three strands: a breaking consolidation of its diagnostic surface onto generics the engine now owns, the completion of simulation-based-calibration registration across all 27 model families, and a correctness fix that materially moves previously reported information criteria. Several releases exist only to pin a new engine version and record what that change does when measured from this side.
hubEvals vs tulpaObs: editorial side-by-side
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.
tulpaObs
6.3ANALYTICS
An occupancy-modeling package that just deleted its own duplicate vocabulary for diagnostics.
◆ Current state
tulpaObs is the ecological occupancy and abundance modeling layer built on the tulpa engine, releasing at high frequency and with version numbers that do not advance monotonically in publication order. The current window covers three strands: a breaking consolidation of its diagnostic surface onto generics the engine now owns, the completion of simulation-based-calibration registration across all 27 model families, and a correctness fix that materially moves previously reported information criteria. Several releases exist only to pin a new engine version and record what that change does when measured from this side.
◆ Where it's heading
The package is systematically removing the parallel names it had accumulated for concepts owned elsewhere, and the registration work is closing rather than expanding — the SBC scope reached its final family in this window. Its cadence is tightly coupled to the engine's, to the point where the interesting content of some releases is a dependency floor plus a measurement. With the breaking rename and the registration scope both behind it, the surface work looks close to finished.
◆ Prediction
Expect the follow-on releases to be consolidation rather than expansion — registry branches, regenerated documentation, engine pins — with the next substantive move most likely a new model family beyond the original registration scope.
Alternatives to hubEvals and tulpaObs
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 tulpaObs.
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 hubEvals alternatives → · See all tulpaObs alternatives →
Recent activity from hubEvals and tulpaObs
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1d agotulpaObsAGENTS.md added as the Codex-facing counterpart to CLAUDE.md
- 1d agotulpaObsSBC helper now handles any response rank, fixing 4D families
- 1d agotulpaObsms_abun() registered for SBC, closing the 27-family scope
- 1d agotulpaObsSBC registry gains the ms_abun() ranked-quantity branch
- 5d agotulpaObsEvery diagnostic becomes one verb dispatched on the fit (breaking) ⚡
- 5d agotulpaObsInformation criteria now score random effects the fit carried
- 24d agohubEvalsScored-forecast counts and faster relative skill
- 1mo agohubEvalsDisaggregated relative skill no longer aborts the whole call
- 1mo agohubEvalsSingle-model scoring returns relative skill of 1 instead of erroring
- 5mo agohubEvalsSample output types and multivariate compound scoring ⚡
- 6mo agohubEvalsScoring on transformed scales via transform arguments
- 11mo agohubEvalsFirst release: score_model_out() and the scoringutils bridge
Frequently asked questions
What is the difference between hubEvals and tulpaObs?
Both compete on the same themes — r-package — within Analytics. tulpaObs is currently shipping more aggressively (velocity 6.3 vs 2.5), with 1 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 hubEvals better than tulpaObs?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. tulpaObs is currently shipping more aggressively (velocity 6.3 vs 2.5), with 1 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 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.
What are the best alternatives to tulpaObs?
Top tulpaObs alternatives in Analytics are ranked by recent ship velocity. Browse the "tulpaObs alternatives" section above for the current picks, or visit /alternatives/tulpaobs for the full list with editorial commentary on each.