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Comparison · Analytics
ggeffects vs mlr3cluster
A side-by-side editorial comparison of ggeffects and mlr3cluster — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-stats
ggeffects vs mlr3cluster: 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 mlr3cluster?
mlr3cluster went from a handful of clusterers to covering the field
mlr3cluster supplies clustering learners to the mlr3 framework. Over three releases it added roughly a dozen learners — CLARA, k-prototypes, spectral, then a batch of nine covering finite mixtures, spherical and directional families, self-organising maps, spatio-temporal DBSCAN and robust trimmed clustering. The newest release fixes predict-time behaviour across the hierarchical learners.
ggeffects vs mlr3cluster: 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.
mlr3cluster
0.0ANALYTICS
mlr3cluster went from a handful of clusterers to covering the field
◆ Current state
mlr3cluster supplies clustering learners to the mlr3 framework. Over three releases it added roughly a dozen learners — CLARA, k-prototypes, spectral, then a batch of nine covering finite mixtures, spherical and directional families, self-organising maps, spatio-temporal DBSCAN and robust trimmed clustering. The newest release fixes predict-time behaviour across the hierarchical learners.
◆ Where it's heading
The package is at the tail end of a coverage push, and the emphasis has shifted from adding algorithms to making the ones it has behave correctly at prediction time — cutting trees at the current k, reclustering coresets, failing informatively on unsupported metric combinations. That is the normal sequence after a rapid expansion.
◆ Prediction
Expect further predict-path corrections and parameter-set alignment across the newly added learners before any more algorithms arrive.
Alternatives to ggeffects and mlr3cluster
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 mlr3cluster.
textrecipes
Text features finally stay sparse all the way to the model.
probably
The package that made calibration a step instead of an afterthought.
workflowsets
workflowsets keeps widening what counts as a model worth comparing.
workflows
The tidymodels pipeline grew a third stage, and it happens after the model runs.
vetiver
Posit's MLOps package went quiet for two years, then came back to keep up with recipes.
See all ggeffects alternatives → · See all mlr3cluster alternatives →
Recent activity from ggeffects and mlr3cluster
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agomlr3clusterHierarchical learners now honour k at prediction time
- 2mo agomlr3clusterNine new clustering learners in one release ⚡
- 5mo agomlr3clusterCLARA, k-prototypes and spectral clustering learners added
- 6mo agomlr3clusterTyped error classes and probabilistic EM assignments
- 8mo agomlr3clusterHDBSCAN gains cluster_selection_epsilon
- 1y agomlr3clusterMclust learner brought in line with paradox conventions
- 1y agoggeffectsggeffects delegates contrasts and slopes to modelbased ⚡
- 1y agoggeffectsFive focal terms and formula-based contrast tests
- 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 mlr3cluster?
Both compete on the same themes — r-stats — within Analytics. ggeffects and mlr3cluster are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is ggeffects better than mlr3cluster?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggeffects and mlr3cluster are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). 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 mlr3cluster?
Top mlr3cluster alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3cluster alternatives" section above for the current picks, or visit /alternatives/mlr3cluster for the full list with editorial commentary on each.