exametrika
A test-theory package that grew into a graphical-model toolkit, now spending its releases paying down the API debt that growth created.
A side-by-side editorial comparison of nuggets and whirl — release velocity, themes, recent moves, and the top alternatives to consider.
nuggets keeps compounding on the 2.0 rewrite — more pattern families, lighter install.
nuggets searches for association rules, contrasts and other conditional patterns in the GUHA tradition, with a C++ core behind dig() and an interactive explore() app for reading results. Since the 2.0 rewrite of that core, every release has widened the same three surfaces: more pattern families to mine, more of explore() to inspect them in, and steady performance work underneath. The most recent tag optimises dig() on sparse crisp data with a sparse bit chain and adds clustering characteristics to explore() for association rules.
whirl turned script logging into a standardized provenance artifact regulators can read.
A parallel R script runner that produces execution logs, aimed at regulated analysis environments. The 0.3.0 release added write_biocompute(), emitting logs as BioCompute Objects in standardized JSON, and simplified the approved-package check to a plain package@version vector. Since then the work has been about what the log can be trusted to contain: an environment_secrets option to keep secret variables out of it, direct versus indirect package usage distinguished and highlighted against the approved list, and the same approval check extended to Python packages.
nuggets searches for association rules, contrasts and other conditional patterns in the GUHA tradition, with a C++ core behind dig() and an interactive explore() app for reading results. Since the 2.0 rewrite of that core, every release has widened the same three surfaces: more pattern families to mine, more of explore() to inspect them in, and steady performance work underneath. The most recent tag optimises dig() on sparse crisp data with a sparse bit chain and adds clustering characteristics to explore() for association rules.
Two forces are shaping the package. One is coverage: baseline, complement and paired-baseline contrasts, correlations, tautologies, ancestors and clustering have all been added as first-class dig_ or explore_ surfaces, so the same search engine now answers a widening set of questions. The other is weight — Shiny packages moved from Imports to Suggests, BH and RcppThread dropped, XSIMD updated, parse_condition() rewritten in C++ — which keeps a package with an interactive app from forcing that app's dependencies on every user. Deprecations are handled through lifecycle rather than removed abruptly.
Expect the sparse-data optimisation to extend from crisp to fuzzy data, and explore() to keep gaining tabs as each new pattern family lands, on the roughly six-week cadence the 2.2 line has held.
A parallel R script runner that produces execution logs, aimed at regulated analysis environments. The 0.3.0 release added write_biocompute(), emitting logs as BioCompute Objects in standardized JSON, and simplified the approved-package check to a plain package@version vector. Since then the work has been about what the log can be trusted to contain: an environment_secrets option to keep secret variables out of it, direct versus indirect package usage distinguished and highlighted against the approved list, and the same approval check extended to Python packages.
The through-line is the log as evidence rather than as debugging output. Every recent addition either widens what the log proves — which packages were really used, in which language, against which approved list — or narrows what it must not leak. Setting options only through an explicit with_options argument to run() and getting renv library paths right for Quarto both point the same way: reproducible, auditable child sessions with nothing implicit.
Expect the approved-package and provenance machinery to keep expanding across languages and environments, since Python approval checks followed the R ones and both feed the same log.
Other Infra & APIs 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 nuggets or whirl.
A test-theory package that grew into a graphical-model toolkit, now spending its releases paying down the API debt that growth created.
projoint spent a year on CRAN paperwork, then shipped a correctness fix it flagged itself.
eratosthenes spends 0.1.0 hardening inputs rather than adding chronology methods.
dqcheckr adds drift analysis, then removes the YAML a user had to hand-write.
An actuarial mainstay spends its releases on CI plumbing, not on new mathematics.
EDAForge is a data-quality auditor renamed mid-flight, still finding its CRAN footing.
See all nuggets alternatives → · See all whirl alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. nuggets 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. nuggets 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 Infra & APIs products to evaluate alongside.
Top nuggets alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "nuggets alternatives" section above for the current picks, or visit /alternatives/nuggets for the full list with editorial commentary on each.
Top whirl alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "whirl alternatives" section above for the current picks, or visit /alternatives/whirl for the full list with editorial commentary on each.