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nanoparquet is chasing byte-level agreement with the Java and Rust Parquet readers, not feature count.
Comparison · Analytics
mapsf vs xplainfi
A side-by-side editorial comparison of mapsf and xplainfi — release velocity, themes, recent moves, and the top alternatives to consider.
mapsf vs xplainfi: 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 xplainfi?
xplainfi treats feature importance as an estimate with error bars, not a number.
xplainfi implements feature importance methods for mlr3 — perturbation-based PFI, CFI and RFI, refit-based LOCO and WVIM, and SAGE. Its defining choice is that importance scores come with inference attached: several confidence-interval methods, including the Nadeau-Bengio correction and a distribution-free option added in 1.1.0. It declared itself released at 1.0.0 in January 2026.
mapsf vs xplainfi: 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.
xplainfi
2.5ANALYTICS
xplainfi treats feature importance as an estimate with error bars, not a number.
◆ Current state
xplainfi implements feature importance methods for mlr3 — perturbation-based PFI, CFI and RFI, refit-based LOCO and WVIM, and SAGE. Its defining choice is that importance scores come with inference attached: several confidence-interval methods, including the Nadeau-Bengio correction and a distribution-free option added in 1.1.0. It declared itself released at 1.0.0 in January 2026.
◆ Where it's heading
Two lines of work run in parallel. The statistical side keeps adding inference options — variance corrections, conditional predictive impact, and the Lei et al. observation-wise loss-difference test — while the computational side attacks the cost of refit-based methods, most recently with a batch_size argument that parallelises refits and a default of one refit per resampling iteration. Support for pre-trained learners in 1.1.0 removes the refit requirement entirely in some workflows.
◆ Prediction
The stated reasoning that budget is better spent on resampling iterations than repeated refits suggests n_repeats may be removed from WVIM and LOCO outright, as the release notes hint.
Alternatives to mapsf and xplainfi
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 xplainfi.
poissonreg
poissonreg gave its models away to parsnip and kept the glue — now it just keeps glmnet honest.
S7
S7 has stopped adding surface and started proving it holds up against R itself.
torchvision
R's torchvision is porting PyTorch's vision stack one task at a time — instance segmentation just landed.
datefixR
The messy-date parser rewrote its core in Rust and came out 300x faster.
maplegend
The legend engine mapsf spun out, now covering legend types the parent map package can draw.
See all mapsf alternatives → · See all xplainfi alternatives →
Recent activity from mapsf and xplainfi
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 20d agoxplainfiRefits parallelise; repeated refits deprioritised in favour of resampling
- 1mo agomapsfLabel placement arguments and deterministic distribution plots
- 2mo agomapsfBackground and extent control across the drawing functions
- 5mo agoxplainfiPre-trained learners supported; distribution-free inference added
- 6mo agoxplainfiVersion bumped to mark the package as released
- 7mo agomapsfPNG resolution control and legend number formatting
- 9mo agoxplainfiConfidence intervals arrive for feature importance scores
- 1y agomapsf1.0.0 introduces theming and deprecates eight style arguments ⚡
- 1y agomapsfPencil-sketch layers, ckmeans breaks, and border extraction
- 2y agomapsfGraticule label display fix
Frequently asked questions
What is the difference between mapsf and xplainfi?
They serve adjacent needs but don't currently overlap on shipped themes. xplainfi 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 xplainfi?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. xplainfi 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 xplainfi?
Top xplainfi alternatives in Analytics are ranked by recent ship velocity. Browse the "xplainfi alternatives" section above for the current picks, or visit /alternatives/xplainfi for the full list with editorial commentary on each.