compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
A side-by-side editorial comparison of hubEvals and lazyeval — release velocity, themes, recent moves, and the top alternatives to consider.
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.
A package retired in 2017 just got rewritten against R's public C API.
lazyeval was the tidyverse's pre-rlang non-standard evaluation layer, formally set aside in 2017 when tidy evaluation replaced it. After eight and a half years without a release, 0.2.3 arrives as a compliance rewrite: the implementation now uses R's public C API, and the release note states it may differ from the historical one in subtle ways. Nothing about the package's role has changed.
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.
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.
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.
lazyeval was the tidyverse's pre-rlang non-standard evaluation layer, formally set aside in 2017 when tidy evaluation replaced it. After eight and a half years without a release, 0.2.3 arrives as a compliance rewrite: the implementation now uses R's public C API, and the release note states it may differ from the historical one in subtle ways. Nothing about the package's role has changed.
This is a dormancy revival driven entirely from outside, R core tightening what counts as the public C API forces packages using older internals to be rewritten or be archived. lazyeval is still a dependency deep in older package trees, so keeping it installable matters more than developing it. The caveat about subtle behavioural differences is the notable part: a package nobody is developing has changed behaviour in ways its release note declines to enumerate.
Expect no further development, only additional compliance releases if R core tightens the C API again.
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 lazyeval.
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 hubEvals alternatives → · See all lazyeval alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
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.
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.
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.
Top lazyeval alternatives in Analytics are ranked by recent ship velocity. Browse the "lazyeval alternatives" section above for the current picks, or visit /alternatives/lazyeval for the full list with editorial commentary on each.