compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
A side-by-side editorial comparison of hubEvals and logr — 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 SAS-style logging package for R, shipping small and slowly by design.
logr produces SAS-style log files for R scripts, and is one component of the r-sassy suite aimed at analysts migrating clinical and pharmaceutical workflows off SAS. Release notes are terse — often a single line — and the cadence has thinned considerably, with one release in 2026 following a long gap. The functionality visible across this window is essentially complete: handlers, suspend and resume, explicit log_info/log_error/log_warning entries, and console output.
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.
logr produces SAS-style log files for R scripts, and is one component of the r-sassy suite aimed at analysts migrating clinical and pharmaceutical workflows off SAS. Release notes are terse — often a single line — and the cadence has thinned considerably, with one release in 2026 following a long gap. The functionality visible across this window is essentially complete: handlers, suspend and resume, explicit log_info/log_error/log_warning entries, and console output.
The arc runs from correctness work on warning and error capture toward giving users control over where log output goes and how it is formatted. Recent releases are refinements of message content rather than new logging concepts, which is what a package settling into maintenance looks like. Nothing in these entries suggests an expansion of scope beyond the SAS-log-emulation brief.
Expect continued low-volume maintenance releases refining message detail and integration with the rest of the r-sassy suite, rather than new logging capability.
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 logr.
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 logr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. hubEvals and logr 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. hubEvals and logr 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.
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 logr alternatives in Analytics are ranked by recent ship velocity. Browse the "logr alternatives" section above for the current picks, or visit /alternatives/logr for the full list with editorial commentary on each.