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Comparison · Analytics
fable vs mlr3filters
A side-by-side editorial comparison of fable and mlr3filters — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-stats
fable vs mlr3filters: at a glance
What is fable?
fable keeps widening its model shelf, one econometric class at a time
fable is the tidyverts forecasting engine, and its releases are almost entirely about which model families it can express. The 0.4.x line added the vector-error-correction and VARIMA classes plus impulse-response methods; 0.5.0 adds fractional differencing via ARFIMA. Between those, the releases are CRAN-check patches and documentation passes.
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.
fable vs mlr3filters: editorial side-by-side
fable
0.0ANALYTICS
fable keeps widening its model shelf, one econometric class at a time
◆ Current state
fable is the tidyverts forecasting engine, and its releases are almost entirely about which model families it can express. The 0.4.x line added the vector-error-correction and VARIMA classes plus impulse-response methods; 0.5.0 adds fractional differencing via ARFIMA. Between those, the releases are CRAN-check patches and documentation passes.
◆ Where it's heading
The package is closing the gap with the older forecast package's model coverage while keeping the tidy model-specification grammar. Each substantive release is a new model class plus the generate()/IRF() plumbing to make it behave like the existing ones. Maintenance releases cluster around CRAN policy and ggplot2/tsibble compatibility rather than internal rewrites.
◆ Prediction
Expect the next substantive release to add another model class or extend generate()/IRF() coverage to the classes that still lack them, rather than change the modelling interface.
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 fable 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 fable 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 fable alternatives → · See all mlr3filters alternatives →
Recent activity from fable and mlr3filters
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 3mo agomlr3filtersFilter dictionary listing now uses prototypes
- 6mo agofablefable adds ARFIMA and fractional differencing ⚡
- 11mo agomlr3filtersBoruta handles logical, factor and ordered features
- 1y agofableIndexing and generate() fixes for VECM models
- 1y agofableVECM and VARIMA models land, plus IRF for VAR and ARIMA ⚡
- 2y agomlr3filtersBoruta and univariate Cox filters added
- 2y agofablePatch for C++ R header changes
- 2y agofableCRAN check patch with generate() fixes
- 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
- 3y agofableTSLM forecasts gain Student's t intervals
Frequently asked questions
What is the difference between fable and mlr3filters?
Both compete on the same themes — r-stats — within Analytics. fable 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 fable better than mlr3filters?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. fable 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 fable?
Top fable alternatives in Analytics are ranked by recent ship velocity. Browse the "fable alternatives" section above for the current picks, or visit /alternatives/fable 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.