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Comparison · Analytics
dfms vs markdown
A side-by-side editorial comparison of dfms and markdown — release velocity, themes, recent moves, and the top alternatives to consider.
dfms vs markdown: 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 markdown?
A package that finished, declared itself done, and handed its core function to a successor.
The R markdown package spent 2023 adding real capability — fenced code block attributes, HTML widget rendering, compatibility shims for rmarkdown's document functions. Then 1.13 declared the package feature-complete and maintenance-only, naming litedown as where development continues. Version 2.0 completes that handover: mark(), the package's core function, is now a thin wrapper around litedown::mark(), and users are told to call litedown directly.
dfms vs markdown: 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.
markdown
0.0ANALYTICS
A package that finished, declared itself done, and handed its core function to a successor.
◆ Current state
The R markdown package spent 2023 adding real capability — fenced code block attributes, HTML widget rendering, compatibility shims for rmarkdown's document functions. Then 1.13 declared the package feature-complete and maintenance-only, naming litedown as where development continues. Version 2.0 completes that handover: mark(), the package's core function, is now a thin wrapper around litedown::mark(), and users are told to call litedown directly.
◆ Where it's heading
This is a controlled retirement rather than abandonment. The maintainer closed out the outstanding feature work first, announced the succession explicitly, and only then reduced the package to a compatibility surface. What remains is a stable shim for the installed base while new work happens in a package with a different name and scope.
◆ Prediction
Expect only CRAN-driven fixes here from now on, with any genuinely new rendering capability appearing in litedown instead. The entries state this policy directly, so the main open question is how long the wrapper is kept before deprecation warnings appear.
Alternatives to dfms and markdown
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 markdown.
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.
See all dfms alternatives → · See all markdown alternatives →
Recent activity from dfms and markdown
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agodfmsconvert() exports models to dlm and KFAS state-space objects
- 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
- 1y agodfmsFixes estimation with a single quarterly variable
- 1y agodfmsAdds mixed-frequency estimation via quarterly.vars
- 1y agomarkdownmark() becomes a thin wrapper around litedown::mark()
- 2y agomarkdownPackage declared feature-complete; work moves to litedown ⚡
- 2y agomarkdownAdds compatibility shims for rmarkdown's document functions
- 2y agomarkdownFixes verbatim code blocks with language attributes
- 2y agomarkdownFixes raw blocks broken by code-block attribute support
- 2y agomarkdownFenced code blocks gain attributes; HTML widgets render
Frequently asked questions
What is the difference between dfms and markdown?
They serve adjacent needs but don't currently overlap on shipped themes. dfms and markdown 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 markdown?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. dfms and markdown 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 markdown?
Top markdown alternatives in Analytics are ranked by recent ship velocity. Browse the "markdown alternatives" section above for the current picks, or visit /alternatives/markdown for the full list with editorial commentary on each.