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dfms vs vcr

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

Shared themes:ropensci

dfms vs vcr: at a glance

Featuredfmsvcr
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesnowcasting, state-space-models, econometrics, ropenscitesting, http-mocking, breaking-change, api-cleanup
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 vcr?

Added the HTTP client everyone moved to, then deleted a decade of its own public surface.

vcr records HTTP interactions to disk so R package tests can replay them without network access. Two releases define its current state. Version 1.6.0 added httr2 support alongside the existing httr and crul backends, following the R ecosystem's migration to httr2. Version 2.0 then removed a large amount of accumulated public surface — the logging functions, vcr_last_error(), the exported R6 classes including RequestHandler, Request, VcrResponse and HTTPInteractionList, and several configuration options that had stopped working or could not be implemented correctly.

Read the full vcr trajectory →

dfms vs vcr: 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.

V
vcr
ANALYTICS
0.0

Added the HTTP client everyone moved to, then deleted a decade of its own public surface.

◆ Current state

vcr records HTTP interactions to disk so R package tests can replay them without network access. Two releases define its current state. Version 1.6.0 added httr2 support alongside the existing httr and crul backends, following the R ecosystem's migration to httr2. Version 2.0 then removed a large amount of accumulated public surface — the logging functions, vcr_last_error(), the exported R6 classes including RequestHandler, Request, VcrResponse and HTTPInteractionList, and several configuration options that had stopped working or could not be implemented correctly.

◆ Where it's heading

The package is consolidating after years of additive growth. The 2.0 removals are almost all things that were exported without needing to be, or options that promised behaviour the implementation could not guarantee — check_cassette_names() was deprecated precisely because it cannot be made correct. Cassette maintenance is being simplified too, with re_record_interval now the single mechanism for expiring recordings.

◆ Prediction

Expect the post-2.0 releases to be about migration support and fallout from the removed API, since the breaking list is long enough that reverse dependencies will surface problems. Async support for httr2 stays blocked until req_perform_parallel gains a mocking hook, which the entries note is upstream work.

Alternatives to dfms and vcr

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 vcr.

See all dfms alternatives → · See all vcr alternatives →

Recent activity from dfms and vcr

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

  1. 1mo agodfmsconvert() exports models to dlm and KFAS state-space objects
  2. 6mo agodfms1.0: rOpenSci review passed, news decomposition added
  3. 6mo agodfmsMixed-frequency estimation gains AR(1) idiosyncratic errors
  4. 8mo agovcr2.0 removes the logging API and the exported R6 classes
  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. 1y agovcrMaintainer email address updated
  9. 2y agovcrAdds httr2 support alongside httr and crul
  10. 3y agovcrDrops compilation; test setup moves back to helper files
  11. 3y agovcrFixes request matching with escaped characters
  12. 5y agovcrvcr_test_path() finds the package root correctly

Frequently asked questions

What is the difference between dfms and vcr?

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

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

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