tidytext
Finished, widely taught, and shipping roxygen fixes.
A side-by-side editorial comparison of bbotk and ggeffects — release velocity, themes, recent moves, and the top alternatives to consider.
bbotk is generalizing from an optimizer toolkit into an evaluation framework.
bbotk is the black-box optimization backend behind mlr3 tuning: search spaces, terminators, archives, and an async layer built on rush. Recent releases pair steady async-API buildout with removal of the deprecated arguments that preceded it. The 1.9.0 release introduced EvalInstance as a base class for OptimInstance, separating evaluating an objective from optimizing one.
ggeffects hands its contrast engine to modelbased and keeps the interface
ggeffects computes and plots marginal effects for a long tail of R model classes. Its recent line has two threads: steadily broadening model support and argument surface, and repeatedly absorbing breaking changes from the packages it computes on top of. In 2.2.0 it stopped absorbing them and delegated test_predictions() and johnson_neyman() to modelbased instead.
bbotk is the black-box optimization backend behind mlr3 tuning: search spaces, terminators, archives, and an async layer built on rush. Recent releases pair steady async-API buildout with removal of the deprecated arguments that preceded it. The 1.9.0 release introduced EvalInstance as a base class for OptimInstance, separating evaluating an objective from optimizing one.
Two threads run through the visible history. The first is async optimization maturing: ArchiveAsync gained a full push/finish/fail vocabulary over rush tasks in 1.11.0, and 1.12.0 deleted the deprecated extra arguments it replaced. The second is dependency consolidation, with custom C hypervolume code handed to moocore and rush pinned to 1.0.0, trimming maintenance surface as the async path becomes the default.
The deprecation removals in 1.12.0 suggest the async archive API is now treated as settled; the next releases most likely build on EvalInstance rather than continuing to churn ArchiveAsync.
ggeffects computes and plots marginal effects for a long tail of R model classes. Its recent line has two threads: steadily broadening model support and argument surface, and repeatedly absorbing breaking changes from the packages it computes on top of. In 2.2.0 it stopped absorbing them and delegated test_predictions() and johnson_neyman() to modelbased instead.
The package is settling into a front-end role — a consistent predict_response() interface over other people's estimation engines — rather than owning the computation itself. The 2.x releases also show a pattern of removing deprecated arguments and clarifying mixed-model semantics, so the interface is being tightened as the backend is outsourced.
Expect the features lost in the modelbased handover to return as that package's contrast and slope estimation matures, rather than being reimplemented locally.
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 bbotk or ggeffects.
Finished, widely taught, and shipping roxygen fixes.
Text features finally stay sparse all the way to the model.
The package that made calibration a step instead of an afterthought.
workflowsets keeps widening what counts as a model worth comparing.
The tidymodels pipeline grew a third stage, and it happens after the model runs.
Posit's MLOps package went quiet for two years, then came back to keep up with recipes.
See all bbotk alternatives → · See all ggeffects alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. bbotk 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. bbotk 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.
Top bbotk alternatives in Analytics are ranked by recent ship velocity. Browse the "bbotk alternatives" section above for the current picks, or visit /alternatives/bbotk for the full list with editorial commentary on each.
Top ggeffects alternatives in Analytics are ranked by recent ship velocity. Browse the "ggeffects alternatives" section above for the current picks, or visit /alternatives/ggeffects for the full list with editorial commentary on each.