compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
Comparison · Analytics
ggguides vs hubEvals
A side-by-side editorial comparison of ggguides and hubEvals — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
ggguides vs hubEvals: at a glance
What is ggguides?
Three releases in one day to make legend positioning finally do what the docs said.
ggguides is a helper layer over ggplot2's guide system, exposing legend placement and styling through small named functions instead of raw theme() calls. On 23 April 2026 it shipped 1.1.7, 1.1.8 and 1.1.9 within thirteen hours, each fixing a different path by which the justification argument silently did nothing. The common root cause is that ggplot2 3.5 split legend.justification into side-specific theme elements, and ggguides was still writing to the generic fallback.
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.
ggguides vs hubEvals: editorial side-by-side
ggguides
0.0ANALYTICS
Three releases in one day to make legend positioning finally do what the docs said.
◆ Current state
ggguides is a helper layer over ggplot2's guide system, exposing legend placement and styling through small named functions instead of raw theme() calls. On 23 April 2026 it shipped 1.1.7, 1.1.8 and 1.1.9 within thirteen hours, each fixing a different path by which the justification argument silently did nothing. The common root cause is that ggplot2 3.5 split legend.justification into side-specific theme elements, and ggguides was still writing to the generic fallback.
◆ Where it's heading
The package is in the phase where a wrapper meets the reality of the API it wraps. All three same-day releases are the same bug found in successive entry points: legend_inside(), then the four side functions, then legend_style(by = ). Along the way the fix work produced a real feature, a justification argument on the side legend functions. The pattern of a single reporter driving three consecutive releases suggests the surface is being audited rather than randomly patched.
◆ Prediction
Expect a consolidation release that audits the remaining theme elements ggguides writes to against ggplot2 3.5 semantics, rather than another single-path fix.
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.
Alternatives to ggguides and hubEvals
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 ggguides or hubEvals.
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 ggguides alternatives → · See all hubEvals alternatives →
Recent activity from ggguides and hubEvals
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
- 3mo agoggguideslegend_style(by=) justification now reaches the whole-plot theme
- 3mo agoggguidesSide legend functions gain justification and target the right theme element
- 3mo agoggguideslegend_inside() justification now moves the legend as documented
- 5mo agohubEvalsSample output types and multivariate compound scoring ⚡
- 6mo agohubEvalsScoring on transformed scales via transform arguments
- 8mo agoggguidesLegend reordering, key overrides and colorbar styling added
- 11mo agohubEvalsFirst release: score_model_out() and the scoringutils bridge
Frequently asked questions
What is the difference between ggguides and hubEvals?
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 ggguides better than hubEvals?
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 ggguides?
Top ggguides alternatives in Analytics are ranked by recent ship velocity. Browse the "ggguides alternatives" section above for the current picks, or visit /alternatives/ggguides for the full list with editorial commentary on each.
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.