tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of gigs and xplainfi — release velocity, themes, recent moves, and the top alternatives to consider.
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
gigs implements international newborn and infant growth standards — INTERGROWTH-21st, WHO — converting anthropometric measurements to z-scores and centiles and classifying growth outcomes. The 0.5.0 release rewrote the public API around rOpenSci review feedback; the two releases after it change no code at all, existing purely to get the documentation site building.
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.
gigs implements international newborn and infant growth standards — INTERGROWTH-21st, WHO — converting anthropometric measurements to z-scores and centiles and classifying growth outcomes. The 0.5.0 release rewrote the public API around rOpenSci review feedback; the two releases after it change no code at all, existing purely to get the documentation site building.
The package has moved from vector-in, vector-out conversion helpers to a data.frame-oriented interface with a single classify_growth() entry point that computes whatever outcomes the supplied columns allow. That is a shift from library to tool — the user describes their data rather than picking the right function. The trailing releases suggest the code is settled and the remaining work is packaging and discoverability.
With the API rewrite absorbed and hosting moved to rOpenSci, the next substantive release should add growth standards or outcomes rather than reshape the interface again.
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.
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.
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.
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 gigs or xplainfi.
A tables-listings-graphs package that reached CRAN and then went quiet.
Tplyr made clinical summary tables explain where every number came from.
Clinical listings that keep inheriting their hardest problem — pagination — from the layer below.
A cache-directory helper that has shipped nothing but CRAN-triggered patches for seven years.
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
datasetjson rebuilt its object model to track the CDISC Dataset-JSON 1.1 schema.
See all gigs alternatives → · See all xplainfi alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
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.
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.
Top gigs alternatives in Analytics are ranked by recent ship velocity. Browse the "gigs alternatives" section above for the current picks, or visit /alternatives/gigs for the full list with editorial commentary on each.
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.