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Comparison · Analytics
ggeffects vs mlr3filters
A side-by-side editorial comparison of ggeffects and mlr3filters — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-stats
ggeffects vs mlr3filters: 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 mlr3filters?
mlr3filters grows one feature-selection filter at a time
mlr3filters provides feature-filter methods to mlr3. Its releases follow a consistent shape: one or two new filters, broader feature-type support on existing ones, and error-message work. Boruta and a univariate Cox filter arrived in 0.8.0; 0.9.0 extended Boruta to logical, factor and ordered features and moved param_set to an active binding.
ggeffects vs mlr3filters: 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.
mlr3filters
0.0ANALYTICS
mlr3filters grows one feature-selection filter at a time
◆ Current state
mlr3filters provides feature-filter methods to mlr3. Its releases follow a consistent shape: one or two new filters, broader feature-type support on existing ones, and error-message work. Boruta and a univariate Cox filter arrived in 0.8.0; 0.9.0 extended Boruta to logical, factor and ordered features and moved param_set to an active binding.
◆ Where it's heading
This is incremental infrastructure that tracks mlr3's own conventions — cli printing, prototype-based dictionaries, featureless learners as defaults — while slowly widening which data types each filter accepts. Nothing in the recent history suggests a change of scope.
◆ Prediction
Expect another filter or two plus continued feature-type broadening, keeping pace with mlr3 core conventions.
Alternatives to ggeffects and mlr3filters
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 mlr3filters.
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 mlr3filters alternatives →
Recent activity from ggeffects and mlr3filters
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 3mo agomlr3filtersFilter dictionary listing now uses prototypes
- 11mo agomlr3filtersBoruta handles logical, factor and ordered features
- 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()
- 2y agomlr3filtersBoruta and univariate Cox filters added
- 3y agomlr3filtersMissing-value tagging and wider CarScore feature support
- 3y agomlr3filtersMissing-value checks and featureless learner defaults
- 3y agomlr3filtersSurvival CAR score filter and pipeline documentation
Frequently asked questions
What is the difference between ggeffects and mlr3filters?
Both compete on the same themes — r-stats — within Analytics. ggeffects and mlr3filters 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 mlr3filters?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggeffects and mlr3filters 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 mlr3filters?
Top mlr3filters alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3filters alternatives" section above for the current picks, or visit /alternatives/mlr3filters for the full list with editorial commentary on each.