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Comparison · Analytics

dfms vs fellingdater

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

Shared themes:ropensci

dfms vs fellingdater: at a glance

Featuredfmsfellingdater
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesnowcasting, state-space-models, econometrics, ropenscidendrochronology, crossdating, archaeology, ropensci
Last editorial update1h ago1h 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 fellingdater?

Went from estimating felling dates to doing the crossdating that produces them.

fellingdater estimates when a tree was felled from sapwood measurements, the core inference in dendrochronological dating of timber. Version 1.0.0 passed rOpenSci review with that scope, and the 2024 releases were mostly about the accompanying JOSS paper and user-supplied sapwood datasets. Version 1.2.0 changed the package's remit substantially, adding an entire trs_* family for tree-ring series handling: crossdating with multiple statistical measures, the Hollstein and Baillie-Pilcher t-value transformations, parallel variation percentages, synthetic series generation, and dated-series plotting.

Read the full fellingdater trajectory →

dfms vs fellingdater: 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
fellingdater
ANALYTICS
0.0

Went from estimating felling dates to doing the crossdating that produces them.

◆ Current state

fellingdater estimates when a tree was felled from sapwood measurements, the core inference in dendrochronological dating of timber. Version 1.0.0 passed rOpenSci review with that scope, and the 2024 releases were mostly about the accompanying JOSS paper and user-supplied sapwood datasets. Version 1.2.0 changed the package's remit substantially, adding an entire trs_* family for tree-ring series handling: crossdating with multiple statistical measures, the Hollstein and Baillie-Pilcher t-value transformations, parallel variation percentages, synthetic series generation, and dated-series plotting.

◆ Where it's heading

The package has expanded backwards along the workflow. It began at the last step — given dated series, estimate the felling date — and 1.2.0 added the step before it, establishing those dates by crossdating in the first place. Version 1.2.1 is early polish on that new surface: axis control, non-syntactic column names, encoding safety in read_fh(). The direction is a single package covering the chain from raw ring widths to a felling-date estimate.

◆ Prediction

Expect the trs_* family to keep accumulating polish and additional crossdating statistics, since it is barely a year old and 1.2.1 was already fixing its plotting and top_n behaviour. Whether the two halves of the package get unified into one workflow interface is the open question the entries do not answer.

Alternatives to dfms and fellingdater

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 fellingdater.

See all dfms alternatives → · See all fellingdater alternatives →

Recent activity from dfms and fellingdater

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

  1. 1mo agodfmsconvert() exports models to dlm and KFAS state-space objects
  2. 4mo agofellingdaterPolish for the crossdating plots and file reader
  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 agofellingdaterAdds a full crossdating and tree-ring analysis toolkit
  8. 1y agofellingdaterUser-supplied sapwood data works across all functions
  9. 1y agofellingdaterFixes fd_report() with user-defined sapwood files
  10. 1y agodfmsAdds mixed-frequency estimation via quarterly.vars
  11. 2y agofellingdaterJOSS paper accepted; citation updated
  12. 2y agofellingdaterAdds a workflow vignette ahead of JOSS submission

Frequently asked questions

What is the difference between dfms and fellingdater?

Both compete on the same themes — ropensci — within Analytics. dfms and fellingdater 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 fellingdater?

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

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