compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
Comparison · Analytics
hubEvals vs SeuratObject
A side-by-side editorial comparison of hubEvals and SeuratObject — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
hubEvals vs SeuratObject: 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 SeuratObject?
The data structure under Seurat, quietly absorbing spatial transcriptomics.
SeuratObject holds the classes and accessors that Seurat is built on, so its releases are felt by every package in that ecosystem rather than by end users directly. Recent work splits between spatial data support, where the Segmentation class gained an sf.data slot and Visium V2 image cropping arrived, and steady correction of subsetting behaviour. Version 5.4.0 adds a settable default dimensional reduction and finer control over factor levels when subsetting.
hubEvals vs SeuratObject: 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.
SeuratObject
0.0ANALYTICS
The data structure under Seurat, quietly absorbing spatial transcriptomics.
◆ Current state
SeuratObject holds the classes and accessors that Seurat is built on, so its releases are felt by every package in that ecosystem rather than by end users directly. Recent work splits between spatial data support, where the Segmentation class gained an sf.data slot and Visium V2 image cropping arrived, and steady correction of subsetting behaviour. Version 5.4.0 adds a settable default dimensional reduction and finer control over factor levels when subsetting.
◆ Where it's heading
Two threads run through the window. The first is spatial: sf-backed segmentation boundaries, a compact slot to mark objects that skip the sp-inherited representation, and Visium V2 cropping, all pointing at spatial transcriptomics becoming a first-class citizen of the object model rather than a bolt-on. The second is a visible argument with itself over droplevels in subsetting, added in 5.1.0, reverted in 5.3.0, and returned in 5.4.0 as an opt-in parameter, which is how a foundational class settles a behaviour it cannot change lightly.
◆ Prediction
Expect the spatial classes to keep absorbing new assay formats, with breaking behaviour continuing to arrive as opt-in parameters rather than changed defaults.
Alternatives to hubEvals and SeuratObject
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 SeuratObject.
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 SeuratObject alternatives →
Recent activity from hubEvals and SeuratObject
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
- 4mo agoSeuratObjectDefault dimensional reduction becomes settable on Seurat objects
- 5mo agohubEvalsSample output types and multivariate compound scoring ⚡
- 6mo agohubEvalsScoring on transformed scales via transform arguments
- 8mo agoSeuratObjectSegmentation gains a compact slot; subsetting stops dropping factor levels
- 11mo agohubEvalsFirst release: score_model_out() and the scoringutils bridge
- 11mo agoSeuratObjectSegmentation boundaries move to sf objects
- 1y agoSeuratObjectSubsetting starts dropping unused factor levels, plus spatial feature accessors
- 2y agoSeuratObjectSeuratObject 5.0.2
- 2y agoSeuratObjectSeuratObject 5.0.1
Frequently asked questions
What is the difference between hubEvals and SeuratObject?
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 SeuratObject?
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 SeuratObject?
Top SeuratObject alternatives in Analytics are ranked by recent ship velocity. Browse the "SeuratObject alternatives" section above for the current picks, or visit /alternatives/seuratobject for the full list with editorial commentary on each.