poissonreg
poissonreg gave its models away to parsnip and kept the glue — now it just keeps glmnet honest.
A side-by-side editorial comparison of dfms and maplegend — 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.
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.
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.
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.
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.
Expect the remaining combined map types to acquire matching legends and the val_* formatting arguments to reach the types that still lack them.
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.
poissonreg gave its models away to parsnip and kept the glue — now it just keeps glmnet honest.
S7 has stopped adding surface and started proving it holds up against R itself.
R's torchvision is porting PyTorch's vision stack one task at a time — instance segmentation just landed.
The messy-date parser rewrote its core in Rust and came out 300x faster.
R help pages translated on demand by whichever LLM you point it at.
Thematic mapping in base R that finally got a theming system, then spent two years polishing legends.
See all dfms alternatives → · See all maplegend 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 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.
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.
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 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.