rnaturalearth
rnaturalearth finished its sp exit and is now optimising how the data actually arrives.
Comparison · Analytics
ggeffects vs mlr3tuning
A side-by-side editorial comparison of ggeffects and mlr3tuning — release velocity, themes, recent moves, and the top alternatives to consider.
ggeffects vs mlr3tuning: at a glance
What is ggeffects?
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.
What is mlr3tuning?
mlr3tuning is rebuilding its async machinery under a stable public surface
mlr3tuning provides hyperparameter optimization for the mlr3 ecosystem, and its recent history is dominated by the asynchronous tuning path: archive freezing, callback stages around queue evaluation, and version-locked compatibility with the rush backend. Releases pair a small feature with several fixes and an explicit compatibility line naming the mlr3 or rush version they track. The most recent release drops all workarounds for older rush versions, which suggests that dependency has stabilized enough to require rather than accommodate.
ggeffects vs mlr3tuning: editorial side-by-side
ggeffects
0.0ANALYTICS
ggeffects hands its contrast engine to modelbased and keeps the interface
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
Expect the features lost in the modelbased handover to return as that package's contrast and slope estimation matures, rather than being reimplemented locally.
mlr3tuning
2.5ANALYTICS
mlr3tuning is rebuilding its async machinery under a stable public surface
◆ Current state
mlr3tuning provides hyperparameter optimization for the mlr3 ecosystem, and its recent history is dominated by the asynchronous tuning path: archive freezing, callback stages around queue evaluation, and version-locked compatibility with the rush backend. Releases pair a small feature with several fixes and an explicit compatibility line naming the mlr3 or rush version they track. The most recent release drops all workarounds for older rush versions, which suggests that dependency has stabilized enough to require rather than accommodate.
◆ Where it's heading
Two things are being tidied at once. The async archive is converging on a consistent data.table representation across batch and async variants, so results are shaped the same regardless of how tuning ran. Separately, the package is becoming a better ecosystem citizen — unioning tuner properties on load instead of overwriting them, removing its callbacks on unload, and raising informative errors from AutoTuner accessors on an untrained model. Both are the marks of a package used as a dependency more than as a destination.
◆ Prediction
With rush pinned to 1.2.0 and the compatibility shims gone, the next release is likely to expose more of the async path through callbacks rather than change the tuning interface.
Alternatives to ggeffects and mlr3tuning
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 ggeffects or mlr3tuning.
writexl
writexl spent nine years refusing to do formatting, then shipped all of it in 2.0.0.
rgbif
rgbif is steadily pushing users off paged searching and onto real downloads.
rstanarm
rstanarm is community-maintained now, tracking Stan and lme4 rather than adding models.
taxa
taxa started a ground-up rewrite in 2021 and has published almost nothing since.
rotl
rotl's whole release history is keeping name matching honest against a moving taxonomy.
See all ggeffects alternatives → · See all mlr3tuning alternatives →
Recent activity from ggeffects and mlr3tuning
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 18d agomlr3tuningmlr3tuning 1.6.1 stops clobbering other packages' tuner properties
- 4mo agomlr3tuningmlr3tuning 1.6.0 aligns archive column order across tuning classes
- 8mo agomlr3tuningmlr3tuning 1.5.1 tracks xgboost 3.1.2.1
- 8mo agomlr3tuningmlr3tuning 1.5.0 adds queue evaluation stages to async callbacks
- 1y agomlr3tuningmlr3tuning 1.4.0 unifies logging under a base mlr3 logger
- 1y agoggeffectsggeffects delegates contrasts and slopes to modelbased ⚡
- 1y agoggeffectsFive focal terms and formula-based contrast tests
- 1y agomlr3tuningmlr3tuning 1.3.0 adds a frozen async archive and leaner worker storage
- 1y agoggeffectsMixed-model predictions split type from interval
- 1y agoggeffectsBias correction for back-transformed mixed-model predictions
- 1y agoggeffectsSupport for WeightIt model classes
- 2y agoggeffectsglmgee support and vcov controls for ggemmeans()
Frequently asked questions
What is the difference between ggeffects and mlr3tuning?
They serve adjacent needs but don't currently overlap on shipped themes. mlr3tuning 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 ggeffects better than mlr3tuning?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mlr3tuning 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 ggeffects?
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.
What are the best alternatives to mlr3tuning?
Top mlr3tuning alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3tuning alternatives" section above for the current picks, or visit /alternatives/mlr3tuning for the full list with editorial commentary on each.