tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
Comparison · Analytics
chattr vs dfms
A side-by-side editorial comparison of chattr and dfms — release velocity, themes, recent moves, and the top alternatives to consider.
chattr vs dfms: at a glance
What is chattr?
chattr deleted every LLM integration it had written and outsourced the lot to ellmer
chattr puts a large language model inside the RStudio IDE, either through a Shiny app or directly at the console. As of 0.3.0 it no longer talks to any model provider itself: all integration goes through ellmer, and the hand-written OpenAI, Databricks and LlamaGPT backends were removed. The package's supported model list is now whatever ellmer supports, and the Shiny app streams responses through ellmer rather than managing a background process.
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.
chattr vs dfms: editorial side-by-side
chattr
0.0ANALYTICS
chattr deleted every LLM integration it had written and outsourced the lot to ellmer
◆ Current state
chattr puts a large language model inside the RStudio IDE, either through a Shiny app or directly at the console. As of 0.3.0 it no longer talks to any model provider itself: all integration goes through ellmer, and the hand-written OpenAI, Databricks and LlamaGPT backends were removed. The package's supported model list is now whatever ellmer supports, and the Shiny app streams responses through ellmer rather than managing a background process.
◆ Where it's heading
The first two releases show why that happened. Each provider brought its own error formats, token discovery and response handling, and 0.2.0 is largely a list of per-provider repairs — OpenAI error parsing, Copilot token discovery and model defaults, a new Databricks foundation model backend. Maintaining that surface scales linearly with the number of providers, and the pivot to ellmer trades it for a single dependency. The cost shows up immediately in 0.3.1, which exists solely to absorb a change in ellmer's token object.
◆ Prediction
Expect chattr's releases to now track ellmer's, as 0.3.1 already does, with the package's own work concentrating on the IDE experience rather than model connectivity. New provider support will arrive without a chattr release at all.
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.
Alternatives to chattr and dfms
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 chattr or dfms.
Tplyr
Tplyr made clinical summary tables explain where every number came from.
rlistings
Clinical listings that keep inheriting their hardest problem — pagination — from the layer below.
hoardr
A cache-directory helper that has shipped nothing but CRAN-triggered patches for seven years.
gigs
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
riem
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
Recent activity from chattr and dfms
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
- 0y agochattrAdapts to ellmer's token object change
- 1y agodfmsFixes estimation with a single quarterly variable
- 1y agochattrAll model integration moves to ellmer, direct backends removed ⚡
- 1y agodfmsAdds mixed-frequency estimation via quarterly.vars
- 2y agochattrDatabricks foundation models added; per-provider error handling fixed
- 2y agochattrFirst release: LLM chat in the RStudio console and app
Frequently asked questions
What is the difference between chattr and dfms?
They serve adjacent needs but don't currently overlap on shipped themes. chattr and dfms 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 chattr better than dfms?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. chattr and dfms 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 chattr?
Top chattr alternatives in Analytics are ranked by recent ship velocity. Browse the "chattr alternatives" section above for the current picks, or visit /alternatives/chattr for the full list with editorial commentary on each.
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.