compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
A side-by-side editorial comparison of hubPredEvalsData and revdbayes — release velocity, themes, recent moves, and the top alternatives to consider.
The pipeline turning hub forecasts into dashboard-ready evaluation data.
hubPredEvalsData generates the scored evaluation data that hubverse prediction dashboards read, driven by a predevals-config.yml and scoring through hubEvals underneath. It is the youngest package in this part of the stack and the fastest-moving in configuration terms, having already passed a breaking 1.0.0 and a schema-versioned feature addition. Its output contract is a scores.csv file consumed downstream, which shapes what its releases care about.
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.
hubPredEvalsData generates the scored evaluation data that hubverse prediction dashboards read, driven by a predevals-config.yml and scoring through hubEvals underneath. It is the youngest package in this part of the stack and the fastest-moving in configuration terms, having already passed a breaking 1.0.0 and a schema-versioned feature addition. Its output contract is a scores.csv file consumed downstream, which shapes what its releases care about.
Each release widens what the config file can express — round selection, then scale transformations with per-target overrides, then target labelling pulled from the hub's own task metadata. The pattern is consistent: capability that already exists in hubEvals gets a declarative surface here so hub maintainers configure it rather than write code. Recent attention to byte-stable output ordering shows the file is being treated as a reproducible artifact, not just a report.
Expect the config schema to keep absorbing hubEvals capabilities as declarative options, with continued attention to making scores.csv reproducible and diffable between runs.
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.
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.
Expect further small releases driven by Rcpp or CRAN check changes rather than by the sampling methods.
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 hubPredEvalsData or revdbayes.
An MLE package rebuilt around composable solvers, then renamed to match.
nabla dropped its C++ engine to chase exact derivatives at any order.
Eight months from first release to keyring caching and workload identity.
A research-project workflow package where the interesting work is in the plumbing.
A cyclomatic complexity checker that ships once every couple of years, and lands when it does.
A package retired in 2017 just got rewritten against R's public C API.
See all hubPredEvalsData alternatives → · See all revdbayes alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. hubPredEvalsData and revdbayes are shipping at a similar cadence (velocity 0.0 vs 0.0, 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. hubPredEvalsData and revdbayes are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top hubPredEvalsData alternatives in Analytics are ranked by recent ship velocity. Browse the "hubPredEvalsData alternatives" section above for the current picks, or visit /alternatives/hubpredevalsdata for the full list with editorial commentary on each.
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.