compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
A side-by-side editorial comparison of hubPredEvalsData and stdmod — 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.
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.
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.
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.
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.
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.
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 stdmod.
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.
Extreme value sampling in pure upkeep mode, mostly answering to Rcpp and CRAN.
See all hubPredEvalsData alternatives → · See all stdmod alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. stdmod 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. stdmod 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.
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 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.