compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
Comparison · Analytics
hubEvals vs revdbayes
A side-by-side editorial comparison of hubEvals and revdbayes — release velocity, themes, recent moves, and the top alternatives to consider.
hubEvals vs revdbayes: 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 revdbayes?
Extreme value sampling in pure upkeep mode, mostly answering to Rcpp and CRAN.
revdbayes performs Bayesian extreme value analysis using ratio-of-uniforms sampling, giving random samples rather than MCMC chains. Every entry in the visible window is filed under bug fixes and minor improvements. The most recent, 1.5.7, strips missing values before fitting the generalised Pareto MLE; the two before it are an Rcpp compatibility patch and a response to CRAN check failures that turned out to be false positives.
hubEvals vs revdbayes: 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.
revdbayes
0.0ANALYTICS
Extreme value sampling in pure upkeep mode, mostly answering to Rcpp and CRAN.
◆ Current state
revdbayes performs Bayesian extreme value analysis using ratio-of-uniforms sampling, giving random samples rather than MCMC chains. Every entry in the visible window is filed under bug fixes and minor improvements. The most recent, 1.5.7, strips missing values before fitting the generalised Pareto MLE; the two before it are an Rcpp compatibility patch and a response to CRAN check failures that turned out to be false positives.
◆ Where it's heading
The methods are settled and the release traffic is external: Rcpp issues, CRAN platform checks, documentation anchor requirements. Two of the six releases exist only because CRAN's check farm flagged something, and one of those flags resolved itself. Sibling package profileCI from the same maintainer has been more active, which suggests attention has moved to newer work rather than away from R entirely.
◆ Prediction
Expect further small releases driven by Rcpp or CRAN check changes rather than by the sampling methods.
Alternatives to hubEvals and revdbayes
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 revdbayes.
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.
lazyeval
A package retired in 2017 just got rewritten against R's public C API.
See all hubEvals alternatives → · See all revdbayes alternatives →
Recent activity from hubEvals and revdbayes
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 agorevdbayesMissing values now removed before generalised Pareto MLE fitting
- 5mo agohubEvalsSample output types and multivariate compound scoring ⚡
- 6mo agohubEvalsScoring on transformed scales via transform arguments
- 7mo agorevdbayesRcpp patch applied to avoid masking Rf_error()
- 7mo agorevdbayesPatch for macOS CRAN check errors that proved to be false positives
- 11mo agohubEvalsFirst release: score_model_out() and the scoringutils bridge
- 2y agorevdbayesArgument documentation corrected; Rd link anchors fixed
- 2y agorevdbayesRcpp warning fix plus Rd itemize corrections
- 2y agorevdbayesC++11 specification dropped to clear a CRAN note
Frequently asked questions
What is the difference between hubEvals and revdbayes?
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 revdbayes?
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 revdbayes?
Top revdbayes alternatives in Analytics are ranked by recent ship velocity. Browse the "revdbayes alternatives" section above for the current picks, or visit /alternatives/revdbayes for the full list with editorial commentary on each.