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fabletools vs mlr3spatial

A side-by-side editorial comparison of fabletools and mlr3spatial — release velocity, themes, recent moves, and the top alternatives to consider.

fabletools vs mlr3spatial: at a glance

Featurefabletoolsmlr3spatial
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesforecasting, tidyverts, model-combination, reconciliationmlr3, spatial, raster, prediction
Last editorial update54m ago4h ago
WebsiteVisit →Visit →

What is fabletools?

The tidyverts forecasting core rebuilt model combination on full residual covariance.

fabletools is the framework layer under fable and fpp3 — mables, fables, accuracy measures, reconciliation, and the model arithmetic that lets forecasters express ensembles as expressions. Version 0.8.0 reworked that arithmetic: combination now uses a joint N-way convolution accounting for the full residual covariance across components rather than composing pairwise, and every arithmetic operator collapses to a single model_combination with correctly implied weights, so nested expressions like ((m1 + m2)/2 + m3)/2 flatten automatically. In parallel, the package has been shedding graphics to {ggtime} on a deliberately slow deprecation clock.

Read the full fabletools trajectory →

What is mlr3spatial?

Raster prediction in mlr3 finally returns class probabilities, not just hard labels.

mlr3spatial connects mlr3 learners to raster and vector spatial data, handling chunked prediction over large rasters through DataBackendRaster. Development is slow and fix-heavy: most releases in the last two years were compatibility work against mlr3 and paradox rather than new capability. 0.7.0 is the exception.

Read the full mlr3spatial trajectory →

fabletools vs mlr3spatial: editorial side-by-side

F
fabletools
ANALYTICS
0.0

The tidyverts forecasting core rebuilt model combination on full residual covariance.

◆ Current state

fabletools is the framework layer under fable and fpp3 — mables, fables, accuracy measures, reconciliation, and the model arithmetic that lets forecasters express ensembles as expressions. Version 0.8.0 reworked that arithmetic: combination now uses a joint N-way convolution accounting for the full residual covariance across components rather than composing pairwise, and every arithmetic operator collapses to a single model_combination with correctly implied weights, so nested expressions like ((m1 + m2)/2 + m3)/2 flatten automatically. In parallel, the package has been shedding graphics to {ggtime} on a deliberately slow deprecation clock.

◆ Where it's heading

The framework is being narrowed and deepened at the same time. Narrowed, because plotting is moving out to a dedicated package over an announced two-year deprecation, leaving fabletools to modeling infrastructure. Deepened, because the recent statistical work targets correctness in places users could not easily inspect — combination weights, inverse-variance weighting computed on response rather than innovation residuals, reconciliation coherency matrices exposed via coherent_smat() and coherent_cmat(). Class hygiene follows the same instinct, with mdl_lst replacing lst_mdl and gaining augment(), glance(), and tidy() so global and reconciliation models report statistics like any other.

◆ Prediction

With combination and reconciliation infrastructure freshly reworked, the remaining announced work is the ggtime separation, so expect the graphics re-exports to keep degrading toward removal while modeling changes stay incremental.

M
mlr3spatial
ANALYTICS
0.0

Raster prediction in mlr3 finally returns class probabilities, not just hard labels.

◆ Current state

mlr3spatial connects mlr3 learners to raster and vector spatial data, handling chunked prediction over large rasters through DataBackendRaster. Development is slow and fix-heavy: most releases in the last two years were compatibility work against mlr3 and paradox rather than new capability. 0.7.0 is the exception.

◆ Where it's heading

The package tracks the mlr3 core rather than leading it — 0.5.0 and 0.6.1 exist to absorb upstream changes in paradox and mlr3. Against that background, 0.7.0 adding probability predictions to predict_spatial() is the first genuine capability increase in a while, arriving alongside two DataBackendRaster fixes for multi-band sources and similarly-named layers. Cadence is roughly one release per year.

◆ Prediction

Given the pattern, the next release is more likely to be compatibility work against a new mlr3 or terra version than another feature; further raster-backend edge cases around layer naming are the visible loose end.

Alternatives to fabletools and mlr3spatial

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 fabletools or mlr3spatial.

See all fabletools alternatives → · See all mlr3spatial alternatives →

Recent activity from fabletools and mlr3spatial

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

  1. 1mo agomlr3spatialpredict_spatial() gains probability predictions
  2. 1mo agofabletoolsModel combination rebuilt on joint N-way convolution
  3. 3mo agofabletoolsCoherency matrices exposed, mdl_lst gains tidier methods
  4. 5mo agofabletoolsGraphics methods now require fabletools to be attached
  5. 6mo agofabletoolsTime series graphics migrating out to ggtime
  6. 8mo agofabletoolsggplot2 4.0.0 compatibility patch
  7. 8mo agofabletoolsIRF() generic and multivariate bootstrap sample paths
  8. 10mo agomlr3spatialCompatibility with mlr3 1.2.0
  9. 1y agomlr3spatialError on conflicting X/Y columns in sf objects
  10. 2y agomlr3spatialCompatibility with paradox 1.0.0
  11. 3y agomlr3spatialUse terra::inMemory() instead of the @ptr slot
  12. 3y agomlr3spatialspatial_predict() accepts stars, sf and Raster* inputs

Frequently asked questions

What is the difference between fabletools and mlr3spatial?

They serve adjacent needs but don't currently overlap on shipped themes. fabletools and mlr3spatial 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 fabletools better than mlr3spatial?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. fabletools and mlr3spatial 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 fabletools?

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

What are the best alternatives to mlr3spatial?

Top mlr3spatial alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3spatial alternatives" section above for the current picks, or visit /alternatives/mlr3spatial for the full list with editorial commentary on each.