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Comparison · Analytics

dfms vs fabletools

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

dfms vs fabletools: at a glance

Featuredfmsfabletools
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesnowcasting, state-space-models, econometrics, ropensciforecasting, tidyverts, model-combination, reconciliation
Last editorial update1h ago54m ago
WebsiteVisit →Visit →

What is dfms?

Peer-reviewed, feature-complete, and now able to hand its models to other forecasting engines.

dfms estimates dynamic factor models in R, the workhorse for nowcasting economic activity from ragged, mixed-frequency data. The package worked through the Banbura and Modugno (2014) specification in stages — quarterly variables in 0.3.0, AR(1) idiosyncratic errors combined with mixed frequency in 0.4.0 — then declared 1.0.0 feature-complete on completing rOpenSci peer review, adding news decomposition to attribute forecast revisions to specific data releases. Version 1.0.1 adds convert(), which exports fitted models to dlm or KFAS state-space objects.

Read the full dfms trajectory →

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 →

dfms vs fabletools: editorial side-by-side

D
dfms
ANALYTICS
0.0

Peer-reviewed, feature-complete, and now able to hand its models to other forecasting engines.

◆ Current state

dfms estimates dynamic factor models in R, the workhorse for nowcasting economic activity from ragged, mixed-frequency data. The package worked through the Banbura and Modugno (2014) specification in stages — quarterly variables in 0.3.0, AR(1) idiosyncratic errors combined with mixed frequency in 0.4.0 — then declared 1.0.0 feature-complete on completing rOpenSci peer review, adding news decomposition to attribute forecast revisions to specific data releases. Version 1.0.1 adds convert(), which exports fitted models to dlm or KFAS state-space objects.

◆ Where it's heading

The package has finished the implementation programme it set out in its 2023 vignette and is now working on the edges: interoperability with other state-space packages rather than more estimation methods of its own. The convert() function is the clearest signal — instead of implementing smoothing and prediction intervals natively, it hands the model to packages that already have them. The rOpenSci move also puts it on a review-backed, documented footing that research users can cite.

◆ Prediction

Expect continued interoperability and diagnostic work rather than new estimators, since the maintainer has explicitly scoped the package as complete. Bug fixes against RcppArmadillo releases will likely remain the other recurring driver.

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.

Alternatives to dfms and fabletools

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 dfms or fabletools.

See all dfms alternatives → · See all fabletools alternatives →

Recent activity from dfms and fabletools

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

  1. 1mo agofabletoolsModel combination rebuilt on joint N-way convolution
  2. 1mo agodfmsconvert() exports models to dlm and KFAS state-space objects
  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. 6mo agodfms1.0: rOpenSci review passed, news decomposition added
  7. 6mo agodfmsMixed-frequency estimation gains AR(1) idiosyncratic errors
  8. 8mo agofabletoolsggplot2 4.0.0 compatibility patch
  9. 8mo agofabletoolsIRF() generic and multivariate bootstrap sample paths
  10. 9mo agodfmsC++ compatibility with RcppArmadillo 15.0.2
  11. 1y agodfmsFixes estimation with a single quarterly variable
  12. 1y agodfmsAdds mixed-frequency estimation via quarterly.vars

Frequently asked questions

What is the difference between dfms and fabletools?

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

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

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

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.