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Comparison · Analytics

Tplyr vs xplainfi

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

Tplyr vs xplainfi: at a glance

FeatureTplyrxplainfi
SectorAnalyticsAnalytics
Velocity score0.02.5
Sparks · 30d00
Top themesclinical-trials, tables, traceability, r-packagemlr3, feature-importance, interpretability, statistical-inference
Last editorial update59m ago7h ago
WebsiteVisit →Visit →

What is Tplyr?

Tplyr made clinical summary tables explain where every number came from.

Tplyr builds clinical summary tables through a layered grammar — count, descriptive statistics, and shift layers assembled onto a table object. The 1.0.0 release added a traceability metadata framework that lets a user ask which source rows produced any given cell, and later releases extended it to cases the first pass missed. The package is maintained by Atorus within the pharmaverse ecosystem.

Read the full Tplyr 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 →

Tplyr vs xplainfi: editorial side-by-side

T
Tplyr
ANALYTICS
0.0

Tplyr made clinical summary tables explain where every number came from.

◆ Current state

Tplyr builds clinical summary tables through a layered grammar — count, descriptive statistics, and shift layers assembled onto a table object. The 1.0.0 release added a traceability metadata framework that lets a user ask which source rows produced any given cell, and later releases extended it to cases the first pass missed. The package is maintained by Atorus within the pharmaverse ecosystem.

◆ Where it's heading

Post-1.0 work has been about completing the metadata story and filling gaps in layer composition rather than adding table types — metadata for missing subjects, add_anti_join(), missing-subject rows, data limiting, and fixes to nested count layers where an inner value appears under several outer groups. Releases cluster tightly after a major version, then go quiet, and the window ends with a patch issued days after the release it corrects.

◆ Prediction

Further releases will most likely continue closing traceability and nested-layer edge cases rather than introducing new layer types, following the pattern of both post-1.0 feature releases.

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 Tplyr 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 Tplyr or xplainfi.

See all Tplyr alternatives → · See all xplainfi alternatives →

Recent activity from Tplyr and xplainfi

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

  1. 21d agoxplainfiRefits parallelise; repeated refits deprioritised in favour of resampling
  2. 5mo agoxplainfiPre-trained learners supported; distribution-free inference added
  3. 6mo agoxplainfiVersion bumped to mark the package as released
  4. 9mo agoxplainfiConfidence intervals arrive for feature importance scores
  5. 2y agoTplyrMissing-subject metadata, add_anti_join(), and nested-layer fixes
  6. 3y agoTplyrMetadata vignette fix and parenthesis hugging
  7. 3y agoTplyrDenominator logic fix ahead of CRAN release
  8. 3y agoTplyrReverse-dependency fix
  9. 3y agoTplyr1.0.0 introduces the traceability metadata framework

Frequently asked questions

What is the difference between Tplyr 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 Tplyr 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 Tplyr?

Top Tplyr alternatives in Analytics are ranked by recent ship velocity. Browse the "Tplyr alternatives" section above for the current picks, or visit /alternatives/tplyr 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.