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Comparison · Analytics
dfms vs sftime
A side-by-side editorial comparison of dfms and sftime — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:interoperability
dfms vs sftime: at a glance
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.
What is sftime?
The spatiotemporal companion to sf, moving at the pace of the packages around it.
sftime extends sf with an active time column, giving R a data frame class for data that is both spatial and temporal. Its recent history is almost entirely integration work: 0.3.0 added conversion methods from spatstat point patterns, sftrack and sftraj movement objects and cubble data frames, plus dedicated tidyr::drop_na() and dplyr::dplyr_reconstruct() methods. The two releases since are a namespace version-check correction and a switch from the magrittr pipe to the native pipe in examples.
dfms vs sftime: editorial side-by-side
dfms
0.0ANALYTICS
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.
sftime
0.0ANALYTICS
The spatiotemporal companion to sf, moving at the pace of the packages around it.
◆ Current state
sftime extends sf with an active time column, giving R a data frame class for data that is both spatial and temporal. Its recent history is almost entirely integration work: 0.3.0 added conversion methods from spatstat point patterns, sftrack and sftraj movement objects and cubble data frames, plus dedicated tidyr::drop_na() and dplyr::dplyr_reconstruct() methods. The two releases since are a namespace version-check correction and a switch from the magrittr pipe to the native pipe in examples.
◆ Where it's heading
The package's job is to be interoperable, so its releases follow whatever the surrounding spatial and tidyverse packages do. The dplyr_reconstruct() work is the clearest example of why that matters: inheriting sf's method caused column binding to silently return an sf object where an sftime object was expected, which is the kind of class-preservation bug that only surfaces two steps downstream. Development is sparse, roughly one release a year.
◆ Prediction
Expect further conversion methods as new spatiotemporal classes appear in the R spatial ecosystem, and continued tracking of dplyr and tidyr generics. The entries do not indicate any planned change to the sftime class itself.
Alternatives to dfms and sftime
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 sftime.
maplegend
The legend engine mapsf spun out, now covering legend types the parent map package can draw.
lang
R help pages translated on demand by whichever LLM you point it at.
mapsf
Thematic mapping in base R that finally got a theming system, then spent two years polishing legends.
qualtRics
qualtRics moved its contact functions onto XM Directory days before the old endpoints died.
fabletools
The tidyverts forecasting core rebuilt model combination on full residual covariance.
Recent activity from dfms and sftime
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agodfmsconvert() exports models to dlm and KFAS state-space objects
- 3mo agosftimeExamples switched to the native R pipe
- 6mo agodfms1.0: rOpenSci review passed, news decomposition added ⚡
- 6mo agodfmsMixed-frequency estimation gains AR(1) idiosyncratic errors
- 9mo agodfmsC++ compatibility with RcppArmadillo 15.0.2
- 11mo agosftimeFixes the cubble namespace version check
- 1y agodfmsFixes estimation with a single quarterly variable
- 1y agodfmsAdds mixed-frequency estimation via quarterly.vars
- 1y agosftimeConverts from spatstat, sftrack and cubble objects
Frequently asked questions
What is the difference between dfms and sftime?
Both compete on the same themes — interoperability — within Analytics. dfms and sftime 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 sftime?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. dfms and sftime 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 sftime?
Top sftime alternatives in Analytics are ranked by recent ship velocity. Browse the "sftime alternatives" section above for the current picks, or visit /alternatives/sftime for the full list with editorial commentary on each.