compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
Comparison · Analytics
hubEvals vs nabla
A side-by-side editorial comparison of hubEvals and nabla — release velocity, themes, recent moves, and the top alternatives to consider.
hubEvals vs nabla: 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 nabla?
nabla dropped its C++ engine to chase exact derivatives at any order.
nabla does forward-mode automatic differentiation in R using dual numbers, returning derivatives exact to machine precision rather than approximated by finite differences. It shipped as dualr in January 2026, then a day later released 0.5.0 under a changed identity: derivatives generalise from a hardcoded second order to arbitrary order through recursive nesting, and the Rcpp and C++ fast paths are deleted so the package is pure R. The current release, 0.7.1, is CRAN resubmission cleanup.
hubEvals vs nabla: 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.
nabla
0.0ANALYTICS
nabla dropped its C++ engine to chase exact derivatives at any order.
◆ Current state
nabla does forward-mode automatic differentiation in R using dual numbers, returning derivatives exact to machine precision rather than approximated by finite differences. It shipped as dualr in January 2026, then a day later released 0.5.0 under a changed identity: derivatives generalise from a hardcoded second order to arbitrary order through recursive nesting, and the Rcpp and C++ fast paths are deleted so the package is pure R. The current release, 0.7.1, is CRAN resubmission cleanup.
◆ Where it's heading
The 0.5.0 release note states the positioning explicitly: exact machine-precision derivatives at any order, not speed. Removing compiled code to make that claim coherent is an unusual direction, most numerical R packages move the other way, and it commits the package to a niche where correctness beats throughput. The old second-order API survives as deprecated thin wrappers, so the pivot was made without stranding early users. The rapid rename and version jump suggest identity was settled late.
◆ Prediction
Expect CRAN acceptance to be followed by work on the optimiser and MLE integration paths, where arbitrary-order derivatives have the clearest use.
Alternatives to hubEvals and nabla
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 nabla.
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.
lazyeval
A package retired in 2017 just got rewritten against R's public C API.
See all hubEvals alternatives → · See all nabla alternatives →
Recent activity from hubEvals and nabla
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 agohubEvalsSingle-model scoring returns relative skill of 1 instead of erroring
- 5mo agohubEvalsSample output types and multivariate compound scoring ⚡
- 5mo agonablaCRAN resubmission cleanup after reviewer feedback
- 6mo agonablaArbitrary-order exact derivatives arrive; compiled code removed ⚡
- 6mo agonablaFirst release as dualr: dual-number autodiff with MLE helpers
- 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 nabla?
They serve adjacent needs but don't currently overlap on shipped themes. 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 hubEvals better than nabla?
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 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 nabla?
Top nabla alternatives in Analytics are ranked by recent ship velocity. Browse the "nabla alternatives" section above for the current picks, or visit /alternatives/nabla for the full list with editorial commentary on each.