r2rtf
The clinical-report table engine learned Chinese, then learned to leave RTF entirely
A side-by-side editorial comparison of dfms and pkglite — release velocity, themes, recent moves, and the top alternatives to consider.
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.
pkglite's whole job is knowing which files in an R package are text — and it keeps getting better at guessing.
pkglite packs an R package into a single plain-text file and unpacks it again, the mechanism pharmaceutical submissions use to move source through systems that accept text but not archives. The API settled at 0.2.0 with file specification templates, `merge()` and `prune()`. Every release since has improved the same thing: the dictionary that decides whether a file is text or binary, most recently rebuilt from the file extensions found across 21,369 CRAN packages.
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.
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.
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.
pkglite packs an R package into a single plain-text file and unpacks it again, the mechanism pharmaceutical submissions use to move source through systems that accept text but not archives. The API settled at 0.2.0 with file specification templates, `merge()` and `prune()`. Every release since has improved the same thing: the dictionary that decides whether a file is text or binary, most recently rebuilt from the file extensions found across 21,369 CRAN packages.
The failure mode this package cares about is silent — misclassify a binary file as text and the round trip corrupts it, misclassify text as binary and it bloats or drops. So the work is empirical rather than architectural: mine real packages for what extensions actually appear, then widen coverage where specific ecosystems break the pattern. Stan interfaces via rstan brought `src/Makevars` and `src/Makefile` handling; machine learning frameworks brought their own binary formats. Dependencies have gone the other way, with cli removed and replaced by internal equivalents.
Expect the next substantive release to widen file specification coverage again for whatever package family the maintainers find breaking the default discovery, since that has been the content of every non-maintenance release for four years.
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 pkglite.
The clinical-report table engine learned Chinese, then learned to leave RTF entirely
New stewardship at openpharma, then two releases adding the methods MCP-Mod was missing
The stubbing library added httr2 support, then spent a year cutting itself free of everything else
crul took mocking back from webmockr and made it a property of the client itself
Six releases, six identical bodies — the feed carries the package abstract instead of release notes
chattr deleted every LLM integration it had written and outsourced the lot to ellmer
See all dfms alternatives → · See all pkglite alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. dfms and pkglite 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. dfms and pkglite 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.
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.
Top pkglite alternatives in Analytics are ranked by recent ship velocity. Browse the "pkglite alternatives" section above for the current picks, or visit /alternatives/pkglite for the full list with editorial commentary on each.