compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
Comparison · Analytics
hubEvals vs tulpa
A side-by-side editorial comparison of hubEvals and tulpa — release velocity, themes, recent moves, and the top alternatives to consider.
hubEvals vs tulpa: 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 tulpa?
A Bayesian spatial engine reshaping itself so downstream packages own their own diagnostics.
tulpa is the C++/R inference engine sitting under a family of ecological occupancy packages, tagging releases several times a week in the 0.0.x range. The current window splits cleanly in two: an API move that turns its calibration and goodness-of-fit entry points into S3 generics, and a run of numerical-correctness work in the nested-Laplace grid. A notable share of releases exist to record a measurement that produced no code change at all.
hubEvals vs tulpa: 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.
tulpa
6.3ANALYTICS
A Bayesian spatial engine reshaping itself so downstream packages own their own diagnostics.
◆ Current state
tulpa is the C++/R inference engine sitting under a family of ecological occupancy packages, tagging releases several times a week in the 0.0.x range. The current window splits cleanly in two: an API move that turns its calibration and goodness-of-fit entry points into S3 generics, and a run of numerical-correctness work in the nested-Laplace grid. A notable share of releases exist to record a measurement that produced no code change at all.
◆ Where it's heading
The generics conversion and the new cross-Hessian return value point the same way: the engine is being reshaped into something downstream packages extend rather than wrap, with the extension points made explicit. The correctness fixes cluster tightly on the joint nested-Laplace driver — indefinite Hessians hitting negative pivots, chunk counts read from live machine load, grid cells silently dropped from a fit — which is where the remaining risk visibly sits. Reported numbers have moved more than once in this window, so the project is still finding cases where earlier answers were wrong rather than merely imprecise.
◆ Prediction
Expect the rest of the diagnostics layer to finish migrating onto generics, and continued hardening of the batched joint driver's dense path, which is the one route that recently diverged from its own single-species equivalent.
Alternatives to hubEvals and tulpa
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 tulpa.
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 tulpa alternatives →
Recent activity from hubEvals and tulpa
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1d agotulpatulpa_re_aghq() exposes the mode/theta cross-Hessian
- 4d agotulpaDense batched joint path could silently drop a grid cell
- 5d agotulpaCalibration and goodness-of-fit entry points become S3 generics ⚡
- 6d agotulpaCUDA backend had two definitions; link order decided if it ran
- 6d agotulpaHyperparameter bounds now flag when they leave the node range
- 7d agotulpaNeither candidate outer-cell rule promoted, decided on coverage
- 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
- 5mo agohubEvalsSample output types and multivariate compound scoring ⚡
- 6mo agohubEvalsScoring on transformed scales via transform arguments
- 11mo agohubEvalsFirst release: score_model_out() and the scoringutils bridge
Frequently asked questions
What is the difference between hubEvals and tulpa?
They serve adjacent needs but don't currently overlap on shipped themes. tulpa is currently shipping more aggressively (velocity 6.3 vs 2.5), with 1 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 tulpa?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. tulpa is currently shipping more aggressively (velocity 6.3 vs 2.5), with 1 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 tulpa?
Top tulpa alternatives in Analytics are ranked by recent ship velocity. Browse the "tulpa alternatives" section above for the current picks, or visit /alternatives/tulpa for the full list with editorial commentary on each.