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Comparison · Analytics

dfms vs maplegend

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

dfms vs maplegend: at a glance

Featuredfmsmaplegend
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesnowcasting, state-space-models, econometrics, ropenscicartography, legends, base-graphics, spatial
Last editorial update1h ago50m 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 maplegend?

The legend engine mapsf spun out, now covering legend types the parent map package can draw.

maplegend draws the legends for base-R thematic maps, extracted from mapsf so both packages could evolve the legend vocabulary independently. It has been catching up to the map types it has to serve: 0.6.0 added choro_point, choro_line, and choro_symb for choropleth legends rendered on circles, lines, and symbols, following the histogram legend type in 0.4.0. Considerable effort has gone into behaving correctly when the plot aspect ratio is not 1, which required refactoring most of the package in 0.4.0 and still produced a proportional-symbol segment sizing fix in 0.6.3.

Read the full maplegend trajectory →

dfms vs maplegend: 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.

M
maplegend
ANALYTICS
0.0

The legend engine mapsf spun out, now covering legend types the parent map package can draw.

◆ Current state

maplegend draws the legends for base-R thematic maps, extracted from mapsf so both packages could evolve the legend vocabulary independently. It has been catching up to the map types it has to serve: 0.6.0 added choro_point, choro_line, and choro_symb for choropleth legends rendered on circles, lines, and symbols, following the histogram legend type in 0.4.0. Considerable effort has gone into behaving correctly when the plot aspect ratio is not 1, which required refactoring most of the package in 0.4.0 and still produced a proportional-symbol segment sizing fix in 0.6.3.

◆ Where it's heading

This is a support library whose backlog is defined by its caller. Every legend type mapsf can produce needs a matching legend renderer, and the release notes are dominated by spacing, offset, and border details — box_cex for symbol spacing, NA box placement in horizontal choropleth legends, text overflow when no_data is set. The shared vocabulary with mapsf is being maintained deliberately, with val_rnd, val_big, and val_dec propagating through legend types release by release. Version numbering is not monotonic in this feed, with 0.4.0 published seconds after 0.5.0.

◆ Prediction

Expect the remaining combined map types to acquire matching legends and the val_* formatting arguments to reach the types that still lack them.

Alternatives to dfms and maplegend

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 maplegend.

See all dfms alternatives → · See all maplegend alternatives →

Recent activity from dfms and maplegend

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

  1. 1mo agodfmsconvert() exports models to dlm and KFAS state-space objects
  2. 2mo agomaplegendContinuous legend formatting and aspect-ratio segment fix
  3. 4mo agomaplegendChoropleth legends on points, lines and symbols
  4. 6mo agodfms1.0: rOpenSci review passed, news decomposition added
  5. 6mo agodfmsMixed-frequency estimation gains AR(1) idiosyncratic errors
  6. 7mo agomaplegendPackage-wide refactor for non-unity aspect ratios, histogram legends
  7. 7mo agomaplegendSingle-modality legends allowed for typo, symb and prop_line
  8. 9mo agodfmsC++ compatibility with RcppArmadillo 15.0.2
  9. 1y agodfmsFixes estimation with a single quarterly variable
  10. 1y agomaplegendAlpha transparency and redraw on device resize
  11. 1y agomaplegendOffset and symbol sizing aligned with mapsf
  12. 1y agodfmsAdds mixed-frequency estimation via quarterly.vars

Frequently asked questions

What is the difference between dfms and maplegend?

They serve adjacent needs but don't currently overlap on shipped themes. dfms and maplegend 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 maplegend?

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

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