compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
Comparison · Analytics
hubEvals vs MultiSpline
A side-by-side editorial comparison of hubEvals and MultiSpline — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
hubEvals vs MultiSpline: 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 MultiSpline?
MultiSpline went from five functions to a full multilevel spline framework in seven weeks.
MultiSpline fits spline-based nonlinear models to multilevel and longitudinal data in R. The package reached CRAN in February 2026 with five functions covering fitting, summary, prediction, plotting and intraclass correlations. Version 0.2.0, seven weeks later, adds cross-classified and nested random-effect structures, automatic knot selection, a multilevel R-squared variance partition, derivative-based interpretation with turning points, model comparison against polynomials, and cluster heterogeneity analysis, while keeping every 0.1.0 call valid.
hubEvals vs MultiSpline: 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.
MultiSpline
0.0ANALYTICS
MultiSpline went from five functions to a full multilevel spline framework in seven weeks.
◆ Current state
MultiSpline fits spline-based nonlinear models to multilevel and longitudinal data in R. The package reached CRAN in February 2026 with five functions covering fitting, summary, prediction, plotting and intraclass correlations. Version 0.2.0, seven weeks later, adds cross-classified and nested random-effect structures, automatic knot selection, a multilevel R-squared variance partition, derivative-based interpretation with turning points, model comparison against polynomials, and cluster heterogeneity analysis, while keeping every 0.1.0 call valid.
◆ Where it's heading
The arc is a research package being built out into a workflow at speed: 0.1.0 could fit a curve, 0.2.0 can tell you where the curve turns, how much variance each level explains, and whether a spline beats a polynomial at all. Backward compatibility was preserved across that expansion, which suggests the author is building for outside users rather than a single paper. The JOSS submission referenced in 0.1.1 points at academic distribution as the intended channel.
◆ Prediction
With the interpretation and diagnostics layers now in place, the next release will most likely extend the supported model families beyond the current lmer and glmer backends.
Alternatives to hubEvals and MultiSpline
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 MultiSpline.
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 MultiSpline alternatives →
Recent activity from hubEvals and MultiSpline
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
- 4mo agoMultiSplineCross-classified and nested structures turn MultiSpline into a framework ⚡
- 5mo agohubEvalsSample output types and multivariate compound scoring ⚡
- 5mo agoMultiSplineMultiSpline v0.1.1
- 5mo agoMultiSplineMultiSpline v0.1.0 - Initial Release
- 5mo agoMultiSplinev0.1.0.1
- 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 MultiSpline?
Both compete on the same themes — 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 hubEvals better than MultiSpline?
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 MultiSpline?
Top MultiSpline alternatives in Analytics are ranked by recent ship velocity. Browse the "MultiSpline alternatives" section above for the current picks, or visit /alternatives/multispline for the full list with editorial commentary on each.