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dfms vs webmockr

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

Shared themes:ropensci

dfms vs webmockr: at a glance

Featuredfmswebmockr
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesnowcasting, state-space-models, econometrics, ropenscihttp-mocking, testing, httr2, ropensci
Last editorial update4h 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 webmockr?

The stubbing library added httr2 support, then spent a year cutting itself free of everything else

webmockr intercepts HTTP requests in R tests and returns stubbed responses, and since 1.0.0 it covers all three clients that matter — httr, httr2 and crul. The releases through 2025 have been about shedding dependencies: internal R6 classes unexported in 2.1.0, crul demoted from Imports to Suggests in the same release, and the mutual dependency with vcr severed in 2.2.0. The package now installs and runs without pulling in the rest of the rOpenSci HTTP stack.

Read the full webmockr trajectory →

dfms vs webmockr: 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.

W
webmockr
ANALYTICS
0.0

The stubbing library added httr2 support, then spent a year cutting itself free of everything else

◆ Current state

webmockr intercepts HTTP requests in R tests and returns stubbed responses, and since 1.0.0 it covers all three clients that matter — httr, httr2 and crul. The releases through 2025 have been about shedding dependencies: internal R6 classes unexported in 2.1.0, crul demoted from Imports to Suggests in the same release, and the mutual dependency with vcr severed in 2.2.0. The package now installs and runs without pulling in the rest of the rOpenSci HTTP stack.

◆ Where it's heading

Two arcs run in sequence. The first is coverage — multiple queued responses in 0.7.0, basic auth mocking, async through crul, then httr2 — building out what can be stubbed. The second, starting with 2.0.0, is correctness and independence: stubs are now deleted if an error occurs mid-construction rather than lingering half-built, partial matching arrives for bodies and queries, and the dependency graph is pruned release by release. The 2.2.0 split from vcr landed within a minute of crul's release taking mocking control into its own clients.

◆ Prediction

RequestPattern is documented as still exported in 2.1.0 but slated for removal, so the next major release is where that lands. Expect continued dependency pruning rather than new client support — the three clients that exist are already covered.

Alternatives to dfms and webmockr

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 webmockr.

See all dfms alternatives → · See all webmockr alternatives →

Recent activity from dfms and webmockr

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. 9mo agodfmsC++ compatibility with RcppArmadillo 15.0.2
  5. 1y agodfmsFixes estimation with a single quarterly variable
  6. 1y agowebmockrvcr dependency severed
  7. 1y agowebmockrInternal classes unexported; crul becomes an optional dependency
  8. 1y agodfmsAdds mixed-frequency estimation via quarterly.vars
  9. 1y agowebmockrFailed stub construction cleans up; partial matching extends to bodies and queries
  10. 2y agowebmockrhttr2 joins httr and crul as a supported client
  11. 3y agowebmockrQueued responses, body matching and integer status codes fixed
  12. 3y agowebmockrRegex URI matching fixed

Frequently asked questions

What is the difference between dfms and webmockr?

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

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

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