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Comparison · Analytics
fable vs mlr3measures
A side-by-side editorial comparison of fable and mlr3measures — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-stats
fable vs mlr3measures: 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 mlr3measures?
mlr3measures is systematically retrofitting sample weights across every metric
mlr3measures is the metric library behind mlr3. Recent releases follow two threads: adding measures — linex, pinball, Mu AUC, gmean, gpr, mcc — and retrofitting sample_weights support across the existing ones, reaching AUC and the confusion-matrix family in 1.3.0. Along the way 1.1.0 deprecated four regression measures and corrected the bias definitions.
fable vs mlr3measures: 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.
mlr3measures
0.0ANALYTICS
mlr3measures is systematically retrofitting sample weights across every metric
◆ Current state
mlr3measures is the metric library behind mlr3. Recent releases follow two threads: adding measures — linex, pinball, Mu AUC, gmean, gpr, mcc — and retrofitting sample_weights support across the existing ones, reaching AUC and the confusion-matrix family in 1.3.0. Along the way 1.1.0 deprecated four regression measures and corrected the bias definitions.
◆ Where it's heading
The library is maturing rather than growing: weighted evaluation and observation-wise loss functions are being brought to metrics that already existed, which is what downstream weighted-resampling and per-observation analysis need. The deprecations suggest the maintainers are willing to remove measures they consider ill-defined rather than keep them for compatibility.
◆ Prediction
Expect sample_weights and observation-wise variants to reach the remaining measures that lack them.
Alternatives to fable and mlr3measures
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 mlr3measures.
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 mlr3measures alternatives →
Recent activity from fable and mlr3measures
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 3mo agomlr3measuresWeighted AUC and weighted confusion-matrix measures
- 6mo agofablefable adds ARFIMA and fractional differencing ⚡
- 8mo agomlr3measuresObservation-wise loss for bbrier and logloss
- 11mo agomlr3measuresrse, rsq, rrse and rae deprecated; bias measures corrected
- 1y agofableIndexing and generate() fixes for VECM models
- 1y agofableVECM and VARIMA models land, plus IRF for VAR and ARIMA ⚡
- 1y agomlr3measureslinex, pinball and Mu AUC measures added
- 2y agomlr3measuresgmean, gpr and multiclass MCC added
- 2y agofablePatch for C++ R header changes
- 2y agofableCRAN check patch with generate() fixes
- 3y agofableTSLM forecasts gain Student's t intervals
- 4y agomlr3measuresObservation-wise loss functions introduced
Frequently asked questions
What is the difference between fable and mlr3measures?
Both compete on the same themes — r-stats — within Analytics. fable and mlr3measures 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 mlr3measures?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. fable and mlr3measures 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 mlr3measures?
Top mlr3measures alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3measures alternatives" section above for the current picks, or visit /alternatives/mlr3measures for the full list with editorial commentary on each.