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Comparison · Infra & APIs

fdacluster vs nuggets

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

Shared themes:r-package

fdacluster vs nuggets: at a glance

Featurefdaclusternuggets
SectorInfra & APIsInfra & APIs
Velocity score0.02.5
Sparks · 30d00
Top themesfunctional-data-analysis, clustering, r-package, rcpppattern-mining, association-rules, guha, cpp-performance
Last editorial update1d ago1h ago
WebsiteVisit →Visit →

What is fdacluster?

Functional data clustering grew from one algorithm into a comparable suite

fdacluster clusters functional data while separating amplitude from phase variation, aligning curves as part of the clustering rather than before it. The algorithm set covers k-means, hierarchical clustering and DBSCAN, all producing a common caps result object so runs can be compared directly. Version 0.4.0 tightened the interface with is_domain_interval and transformation arguments describing the input data, added compatibility checking between incompatible option combinations, and split the L2 and normalized L2 distances into separate C++ classes to enforce that plain L2 cannot be combined with dilation or affine warping it is not invariant to.

Read the full fdacluster trajectory →

What is nuggets?

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.

Read the full nuggets trajectory →

fdacluster vs nuggets: editorial side-by-side

F
fdacluster
INFRA · APIS
0.0

Functional data clustering grew from one algorithm into a comparable suite

◆ Current state

fdacluster clusters functional data while separating amplitude from phase variation, aligning curves as part of the clustering rather than before it. The algorithm set covers k-means, hierarchical clustering and DBSCAN, all producing a common caps result object so runs can be compared directly. Version 0.4.0 tightened the interface with is_domain_interval and transformation arguments describing the input data, added compatibility checking between incompatible option combinations, and split the L2 and normalized L2 distances into separate C++ classes to enforce that plain L2 cannot be combined with dilation or affine warping it is not invariant to.

◆ Where it's heading

The trajectory runs from method implementation toward guardrails and portability. Early releases added capability; recent ones prevent misuse and reduce weight - dplyr, forcats, tidyr and purrr removed in 0.4.0, furrr swapped for future.apply - while 0.4.2 is entirely C++ correctness, replacing Armadillo's whole-object finiteness check with scalar std::isfinite and fixing an integer overflow in linear index computation that broke large datasets. Cadence is roughly one release a year.

◆ Prediction

Given that the last two releases were dependency reduction and numerical correctness rather than method work, expect the next to continue in that vein unless a new clustering algorithm is contributed.

N
nuggets
INFRA · APIS
2.5

nuggets keeps compounding on the 2.0 rewrite — more pattern families, lighter install.

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

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.

Alternatives to fdacluster and nuggets

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 fdacluster or nuggets.

See all fdacluster alternatives → · See all nuggets alternatives →

Recent activity from fdacluster and nuggets

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

  1. 27d agonuggetsSparse bit chain speeds dig(); explore() gains clustering
  2. 2mo agonuggetspartition() gains .subsets; geom_diamond() layout improved
  3. 5mo agonuggetsexplore() covers contrasts and correlations; dig_ancestors() added
  4. 6mo agonuggetsCritical explore() bug fixed; is_logicalish() added
  5. 6mo agonuggetsShiny deps moved to Suggests; BH and RcppThread dropped
  6. 7mo agofdaclusterInteger overflow fixed for large datasets, C++ finiteness checks corrected
  7. 8mo agonuggetscluster_associations() and add_interest() arrive; C++ condition parser
  8. 1y agofdaclusterParallel worker setup and an acronym correction
  9. 1y agofdaclusterInput description arguments and enforced distance-warping compatibility
  10. 3y agofdaclusterMedian centroids and centroids defined on unioned grids
  11. 3y agofdaclusterNamespace notation and optional dependency guards
  12. 3y agofdaclusterHierarchical clustering, DBSCAN and a shared result class arrive together

Frequently asked questions

What is the difference between fdacluster and nuggets?

Both compete on the same themes — r-package — within Infra & APIs. 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.

Is fdacluster better than nuggets?

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.

What are the best alternatives to fdacluster?

Top fdacluster alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "fdacluster alternatives" section above for the current picks, or visit /alternatives/fdacluster for the full list with editorial commentary on each.

What are the best alternatives to nuggets?

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.