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fabletools vs poissonreg

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

fabletools vs poissonreg: at a glance

Featurefabletoolspoissonreg
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesforecasting, tidyverts, model-combination, reconciliationtidymodels, count-regression, glmnet, r-language
Last editorial update1h ago43m 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 poissonreg?

poissonreg gave its models away to parsnip and kept the glue — now it just keeps glmnet honest.

poissonreg is a tidymodels extension that wires Poisson and zero-inflated count regression into the parsnip interface. Its defining event was giving up ownership: the model definition functions moved into parsnip itself, leaving this package as engine bindings and prediction plumbing. The current dev release is entirely correctness and hygiene work — glmnet predictions now default to mean counts rather than the linear predictor, and single-observation prediction works at last.

Read the full poissonreg trajectory →

fabletools vs poissonreg: 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.

P
poissonreg
ANALYTICS
0.0

poissonreg gave its models away to parsnip and kept the glue — now it just keeps glmnet honest.

◆ Current state

poissonreg is a tidymodels extension that wires Poisson and zero-inflated count regression into the parsnip interface. Its defining event was giving up ownership: the model definition functions moved into parsnip itself, leaving this package as engine bindings and prediction plumbing. The current dev release is entirely correctness and hygiene work — glmnet predictions now default to mean counts rather than the linear predictor, and single-observation prediction works at last.

◆ Where it's heading

Release cadence has collapsed from yearly to a four-year gap between 1.0.1 and the current development version, and the content has shifted from features to deduplication against parsnip — copied helper functions replaced by the upstream originals, obsolete generic registrations removed, tests migrated to the shared extension-package pattern. This is what a stabilized tidymodels satellite looks like: the interface lives upstream, and the package's job is to not drift from it.

◆ Prediction

The dev version's accumulated fixes point to a CRAN release of 1.0.2 as the next move, with content limited to the glmnet prediction corrections rather than any new engine or model type.

Alternatives to fabletools and poissonreg

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 poissonreg.

See all fabletools alternatives → · See all poissonreg alternatives →

Recent activity from fabletools and poissonreg

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

  1. 1mo agofabletoolsModel combination rebuilt on joint N-way convolution
  2. 3mo agofabletoolsCoherency matrices exposed, mdl_lst gains tidier methods
  3. 3mo agopoissonregglmnet predictions now default to mean counts
  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. 3y agopoissonregDocumentation regenerated for valid HTML5
  9. 4y agopoissonregCase weight support tracks parsnip 1.0.0
  10. 4y agopoissonregModel definitions move out of poissonreg into parsnip
  11. 4y agopoissonregglm becomes the default engine; tidy() for hurdle models
  12. 5y agopoissonregFirst release, with a glmnet column-order safeguard

Frequently asked questions

What is the difference between fabletools and poissonreg?

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

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

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