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Comparison · Analytics

maplegend vs xplainfi

A side-by-side editorial comparison of maplegend and xplainfi — release velocity, themes, recent moves, and the top alternatives to consider.

maplegend vs xplainfi: at a glance

Featuremaplegendxplainfi
SectorAnalyticsAnalytics
Velocity score0.02.5
Sparks · 30d00
Top themescartography, legends, base-graphics, spatialmlr3, feature-importance, interpretability, statistical-inference
Last editorial update54m ago4h ago
WebsiteVisit →Visit →

What is maplegend?

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.

Read the full maplegend trajectory →

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.

Read the full xplainfi trajectory →

maplegend vs xplainfi: editorial side-by-side

M
maplegend
ANALYTICS
0.0

The legend engine mapsf spun out, now covering legend types the parent map package can draw.

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

Expect the remaining combined map types to acquire matching legends and the val_* formatting arguments to reach the types that still lack them.

X
xplainfi
ANALYTICS
2.5

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 maplegend 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 maplegend or xplainfi.

See all maplegend alternatives → · See all xplainfi alternatives →

Recent activity from maplegend and xplainfi

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 20d agoxplainfiRefits parallelise; repeated refits deprioritised in favour of resampling
  2. 2mo agomaplegendContinuous legend formatting and aspect-ratio segment fix
  3. 4mo agomaplegendChoropleth legends on points, lines and symbols
  4. 5mo agoxplainfiPre-trained learners supported; distribution-free inference added
  5. 6mo agoxplainfiVersion bumped to mark the package as released
  6. 7mo agomaplegendPackage-wide refactor for non-unity aspect ratios, histogram legends
  7. 7mo agomaplegendSingle-modality legends allowed for typo, symb and prop_line
  8. 9mo agoxplainfiConfidence intervals arrive for feature importance scores
  9. 1y agomaplegendAlpha transparency and redraw on device resize
  10. 1y agomaplegendOffset and symbol sizing aligned with mapsf

Frequently asked questions

What is the difference between maplegend 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 maplegend 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 maplegend?

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.

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.