r2rtf
The clinical-report table engine learned Chinese, then learned to leave RTF entirely
Comparison · Analytics
mapsf vs tabnet
A side-by-side editorial comparison of mapsf and tabnet — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
mapsf vs tabnet: at a glance
What is mapsf?
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.
What is tabnet?
A tabular deep-learning model in R that keeps widening what counts as a tabular task.
tabnet ports the TabNet attentive tabular architecture to R on torch, wired into tidymodels through parsnip so it slots into workflows, tuning, and case weights like any other engine. The model surface has grown well past plain supervised fitting: unsupervised pretraining, missing values in predictors, multi-outcome fitting, hierarchical multi-label classification, and built-in explainability via tabnet_explain(). The 0.9.x line has been consolidating rather than adding, with 0.9.0 finally making hierarchical classification work correctly by accounting for the ancestor matrix.
mapsf vs tabnet: editorial side-by-side
mapsf
0.0ANALYTICS
Thematic mapping in base R that finally got a theming system, then spent two years polishing legends.
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
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.
tabnet
2.5ANALYTICS
A tabular deep-learning model in R that keeps widening what counts as a tabular task.
◆ Current state
tabnet ports the TabNet attentive tabular architecture to R on torch, wired into tidymodels through parsnip so it slots into workflows, tuning, and case weights like any other engine. The model surface has grown well past plain supervised fitting: unsupervised pretraining, missing values in predictors, multi-outcome fitting, hierarchical multi-label classification, and built-in explainability via tabnet_explain(). The 0.9.x line has been consolidating rather than adding, with 0.9.0 finally making hierarchical classification work correctly by accounting for the ancestor matrix.
◆ Where it's heading
Two threads run through the release history. The first is task surface — each minor version tends to admit a class of problem the model previously could not express, from missing data to hierarchy to imbalanced binary outcomes. The second is torch-level performance and correctness, visible in the torch_ignite_adam default that cut pretraining time roughly 30% and the fix for optimizers frozen after checkpointing on cuda and mps. Tidymodels integration is treated as a first-class obligation, with parsnip breaking changes tracked release by release.
◆ Prediction
The hierarchical path is the least finished: 0.5.0 introduced it and 0.9.0 only just made it effective, so the next releases most likely extend evaluation and explainability to hierarchical fits rather than adding another task type.
Alternatives to mapsf and tabnet
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 mapsf or tabnet.
DoseFinding
New stewardship at openpharma, then two releases adding the methods MCP-Mod was missing
webmockr
The stubbing library added httr2 support, then spent a year cutting itself free of everything else
crul
crul took mocking back from webmockr and made it a property of the client itself
dendroNetwork
Six releases, six identical bodies — the feed carries the package abstract instead of release notes
chattr
chattr deleted every LLM integration it had written and outsourced the lot to ellmer
See all mapsf alternatives → · See all tabnet alternatives →
Recent activity from mapsf and tabnet
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 21d agotabnetvip dependency moves to r-universe
- 1mo agomapsfLabel placement arguments and deterministic distribution plots
- 2mo agotabnetHierarchical classification made effective, augment() added
- 2mo agomapsfBackground and extent control across the drawing functions
- 6mo agotabnetentmax15 and sparsemax15 masks, AUM loss for imbalanced data
- 7mo agomapsfPNG resolution control and legend number formatting
- 1y agomapsf1.0.0 introduces theming and deprecates eight style arguments ⚡
- 1y agotabnetBugfix release for R 4.5 and dials tuning
- 1y agomapsfPencil-sketch layers, ckmeans breaks, and border extraction
- 2y agomapsfGraticule label display fix
- 2y agotabnetCase weights and warm-start parameters via parsnip
- 2y agotabnetHierarchical multi-label classification via data.tree ⚡
Frequently asked questions
What is the difference between mapsf and tabnet?
Both compete on the same themes — r-package — within Analytics. tabnet is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is mapsf better than tabnet?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. tabnet is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to mapsf?
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.
What are the best alternatives to tabnet?
Top tabnet alternatives in Analytics are ranked by recent ship velocity. Browse the "tabnet alternatives" section above for the current picks, or visit /alternatives/tabnet for the full list with editorial commentary on each.