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dfms vs poissonreg

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

dfms vs poissonreg: at a glance

Featuredfmspoissonreg
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesnowcasting, state-space-models, econometrics, ropenscitidymodels, count-regression, glmnet, r-language
Last editorial update2h ago44m 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 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 →

dfms vs poissonreg: 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.

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 dfms 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 dfms or poissonreg.

See all dfms alternatives → · See all poissonreg alternatives →

Recent activity from dfms and poissonreg

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

  1. 1mo agodfmsconvert() exports models to dlm and KFAS state-space objects
  2. 3mo agopoissonregglmnet predictions now default to mean counts
  3. 6mo agodfms1.0: rOpenSci review passed, news decomposition added
  4. 6mo agodfmsMixed-frequency estimation gains AR(1) idiosyncratic errors
  5. 9mo agodfmsC++ compatibility with RcppArmadillo 15.0.2
  6. 1y agodfmsFixes estimation with a single quarterly variable
  7. 1y agodfmsAdds mixed-frequency estimation via quarterly.vars
  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 dfms and poissonreg?

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

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