S7
S7 has stopped adding surface and started proving it holds up against R itself.
A side-by-side editorial comparison of dfms and mapsf — 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.
Thematic mapping in base R that finally got a theming system, then spent two years polishing legends.
mapsf produces thematic maps on R's base graphics device — choropleths, proportional symbols, typology maps, rasters, and their combinations, with legends, scale bars, north arrows, and insets as composable elements. Version 1.0.0 was the structural release, introducing a theming system that deprecated eight scattered styling arguments and adding mf_png() and mf_svg() export helpers plus alpha transparency across map types. The 1.1.x and 1.2.x line since then has been steady refinement: background and extent control on the drawing functions, decimal and thousands-separator control in legends, and label placement arguments.
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.
mapsf produces thematic maps on R's base graphics device — choropleths, proportional symbols, typology maps, rasters, and their combinations, with legends, scale bars, north arrows, and insets as composable elements. Version 1.0.0 was the structural release, introducing a theming system that deprecated eight scattered styling arguments and adding mf_png() and mf_svg() export helpers plus alpha transparency across map types. The 1.1.x and 1.2.x line since then has been steady refinement: background and extent control on the drawing functions, decimal and thousands-separator control in legends, and label placement arguments.
The package has been consolidating control into fewer, more consistent places. Legend handling moved out to the maplegend package in 0.8.0 and the per-element mf_legend_* functions were deprecated in favor of arguments on the map calls themselves; theming replaced ad-hoc style arguments in 1.0.0; and recent releases keep propagating the same argument vocabulary — bg, extent, leg_val_rnd, leg_val_dec, leg_val_big — across every function that should accept it. Determinism is a visible concern too, with 1.2.1 fixing a seed so mf_distr() point positions stop moving between runs.
The recent releases are almost entirely argument-parity work across existing functions, so expect that to continue until the vocabulary is uniform rather than any new map type appearing.
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 mapsf.
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.
The legend engine mapsf spun out, now covering legend types the parent map package can draw.
R help pages translated on demand by whichever LLM you point it at.
qualtRics moved its contact functions onto XM Directory days before the old endpoints died.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. dfms and mapsf 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 mapsf 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 mapsf alternatives in Analytics are ranked by recent ship velocity. Browse the "mapsf alternatives" section above for the current picks, or visit /alternatives/mapsf for the full list with editorial commentary on each.