compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
Comparison · Analytics
hubEvals vs manymome
A side-by-side editorial comparison of hubEvals and manymome — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
hubEvals vs manymome: 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 manymome?
Steady quarterly releases behind a feed that shows almost none of what changed.
manymome computes indirect and moderated effects for path-analysis and SEM models using bootstrap and Monte Carlo intervals. The four most recent CRAN releases (0.3.2 through 0.3.6) publish as bare pointers to the package's own NEWS page, so the feed carries no changelog text for any of them. Where content is visible, at 0.3.1 and 0.2.9, the work is fitting-engine breadth and speed rather than new methodology.
hubEvals vs manymome: 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.
manymome
0.0ANALYTICS
Steady quarterly releases behind a feed that shows almost none of what changed.
◆ Current state
manymome computes indirect and moderated effects for path-analysis and SEM models using bootstrap and Monte Carlo intervals. The four most recent CRAN releases (0.3.2 through 0.3.6) publish as bare pointers to the package's own NEWS page, so the feed carries no changelog text for any of them. Where content is visible, at 0.3.1 and 0.2.9, the work is fitting-engine breadth and speed rather than new methodology.
◆ Where it's heading
The legible arc runs toward turning the q_* quick-mediation wrappers into a complete workflow: lavaan::sem fitting with full information maximum likelihood for missing data, a plot method, and user-specified mediation models, alongside repeated optimization of do_boot() and do_mc(). Cadence is roughly quarterly and has held for two years. What the last four versions actually contain cannot be read from this feed.
◆ Prediction
Expect continued quarterly CRAN releases extending the q_* family; beyond that the entries shown do not support a confident call on direction.
Alternatives to hubEvals and manymome
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 manymome.
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.
revdbayes
Extreme value sampling in pure upkeep mode, mostly answering to Rcpp and CRAN.
See all hubEvals alternatives → · See all manymome alternatives →
Recent activity from hubEvals and manymome
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
- 2mo agomanymome0.3.6 CRAN release
- 4mo agomanymome0.3.4 CRAN release
- 5mo agohubEvalsSample output types and multivariate compound scoring ⚡
- 6mo agohubEvalsScoring on transformed scales via transform arguments
- 7mo agomanymome0.3.3 CRAN release
- 8mo agomanymome0.3.2 CRAN release
- 11mo agohubEvalsFirst release: score_model_out() and the scoringutils bridge
- 11mo agomanymome0.3.1 CRAN release
- 1y agomanymome0.2.9 CRAN release
Frequently asked questions
What is the difference between hubEvals and manymome?
Both compete on the same themes — r-package — within Analytics. 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 manymome?
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 manymome?
Top manymome alternatives in Analytics are ranked by recent ship velocity. Browse the "manymome alternatives" section above for the current picks, or visit /alternatives/manymome for the full list with editorial commentary on each.