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ggeffects vs mlr3mbo

A side-by-side editorial comparison of ggeffects and mlr3mbo — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-stats

ggeffects vs mlr3mbo: at a glance

Featureggeffectsmlr3mbo
SectorAnalyticsAnalytics
Velocity score0.02.5
Sparks · 30d00
Top themesmarginal-effects, r-stats, statistics, breaking-changesbayesian-optimization, mlr3, hyperparameter-tuning, r-stats
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

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.

Read the full ggeffects trajectory →

What is mlr3mbo?

mlr3mbo picked its defaults from a benchmark study, not from taste

mlr3mbo does model-based and Bayesian optimisation for mlr3. Its 1.0.0 release added a dictionary of pre-built acquisition-function optimisers and, more consequentially, replaced the default surrogate, acquisition function and optimiser settings with values derived from a large-scale benchmark study. The releases since are corrections to the acquisition-optimiser path exposed by that new default configuration.

Read the full mlr3mbo trajectory →

ggeffects vs mlr3mbo: editorial side-by-side

G
ggeffects
ANALYTICS
0.0

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.

M
mlr3mbo
ANALYTICS
2.5

mlr3mbo picked its defaults from a benchmark study, not from taste

◆ Current state

mlr3mbo does model-based and Bayesian optimisation for mlr3. Its 1.0.0 release added a dictionary of pre-built acquisition-function optimisers and, more consequentially, replaced the default surrogate, acquisition function and optimiser settings with values derived from a large-scale benchmark study. The releases since are corrections to the acquisition-optimiser path exposed by that new default configuration.

◆ Where it's heading

The package has moved from a toolkit that expected users to assemble a Bayesian optimisation loop into one with a defensible default loop, and the recent fixes — warm-start sizing on multi-objective archives, silently discarded terminators, stale x_domain values — are the consequences of more people running the default path.

◆ Prediction

Expect continued hardening of the acquisition-optimiser classes rather than new acquisition functions.

Alternatives to ggeffects and mlr3mbo

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 mlr3mbo.

See all ggeffects alternatives → · See all mlr3mbo alternatives →

Recent activity from ggeffects and mlr3mbo

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 23d agomlr3mboAcquisition optimiser fixes for warm starts and archives
  2. 3mo agomlr3mboDictionary lookup and restart-limit fixes
  3. 4mo agomlr3mborush 1.0.0 compatibility and Surrogate$check()
  4. 5mo agomlr3mbomlr3mbo 1.0.0 ships benchmark-derived default settings
  5. 10mo agomlr3mbomlr3learners 0.13.0 compatibility
  6. 11mo agomlr3mboMaintainer change and mlr3pipelines 0.9.0 upkeep
  7. 1y agoggeffectsggeffects delegates contrasts and slopes to modelbased
  8. 1y agoggeffectsFive focal terms and formula-based contrast tests
  9. 1y agoggeffectsMixed-model predictions split type from interval
  10. 1y agoggeffectsBias correction for back-transformed mixed-model predictions
  11. 1y agoggeffectsSupport for WeightIt model classes
  12. 2y agoggeffectsglmgee support and vcov controls for ggemmeans()

Frequently asked questions

What is the difference between ggeffects and mlr3mbo?

Both compete on the same themes — r-stats — within Analytics. mlr3mbo 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 mlr3mbo?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mlr3mbo 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 mlr3mbo?

Top mlr3mbo alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3mbo alternatives" section above for the current picks, or visit /alternatives/mlr3mbo for the full list with editorial commentary on each.