compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
Comparison · Analytics
hubEvals vs stdmod
A side-by-side editorial comparison of hubEvals and stdmod — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
hubEvals vs stdmod: 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 stdmod?
A moderation-analysis package now pointing users at its own siblings for the harder work.
stdmod computes standardized moderation effects in regression, part of a cluster of R packages from the same author covering moderation, mediation and model comparison. Its release feed is a CRAN-announcement format — several entries carry nothing but a version and a link — and its development has slowed markedly, with the substantive feature work sitting back in 2024. The most recent release is documentation rather than code.
hubEvals vs stdmod: 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.
stdmod
2.5ANALYTICS
A moderation-analysis package now pointing users at its own siblings for the harder work.
◆ Current state
stdmod computes standardized moderation effects in regression, part of a cluster of R packages from the same author covering moderation, mediation and model comparison. Its release feed is a CRAN-announcement format — several entries carry nothing but a version and a link — and its development has slowed markedly, with the substantive feature work sitting back in 2024. The most recent release is documentation rather than code.
◆ Where it's heading
The clearest signal is the latest release redirecting users toward betaselectr and manymome for tasks stdmod also covers, on the grounds that those packages handle them more comprehensively. That is a package consciously narrowing its scope within a family rather than competing with its siblings. The earlier feature work — conditional effects at chosen moderator values, R-squared increase reporting, print formatting — reads as a stable core that has since been left alone.
◆ Prediction
Expect stdmod to stay in maintenance while the author's newer packages absorb the overlapping functionality, with future releases likely limited to CRAN compliance and documentation.
Alternatives to hubEvals and stdmod
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 stdmod.
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 stdmod alternatives →
Recent activity from hubEvals and stdmod
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 22d agostdmodDocs now steer users to betaselectr and manymome
- 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
- 7mo agostdmodv0.2.12 at CRAN
- 11mo agohubEvalsFirst release: score_model_out() and the scoringutils bridge
- 2y agostdmodSummary printout gains rounding and p-value formatting control
- 2y agostdmodR-squared increase reporting and chosen moderator values
- 3y agostdmodv0.2.0.0 at CRAN
- 4y agostdmodv0.1.7.4 at CRAN
Frequently asked questions
What is the difference between hubEvals and stdmod?
Both compete on the same themes — r-package — within Analytics. hubEvals and stdmod are shipping at a similar cadence (velocity 2.5 vs 2.5, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is hubEvals better than stdmod?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. hubEvals and stdmod are shipping at a similar cadence (velocity 2.5 vs 2.5, both within Sparkpulse's "active" band). 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 stdmod?
Top stdmod alternatives in Analytics are ranked by recent ship velocity. Browse the "stdmod alternatives" section above for the current picks, or visit /alternatives/stdmod for the full list with editorial commentary on each.