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Comparison · Infra & APIs

bolasso vs nuggets

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

Shared themes:r-package

bolasso vs nuggets: at a glance

Featurebolassonuggets
SectorInfra & APIsInfra & APIs
Velocity score0.02.5
Sparks · 30d00
Top themesvariable-selection, lasso, r-package, bootstrappattern-mining, association-rules, guha, cpp-performance
Last editorial update1d ago1h ago
WebsiteVisit →Visit →

What is bolasso?

Bootstrap lasso got a fast mode, a second selection rule, and multinomial support

bolasso implements the bootstrapped lasso, refitting a regularized regression across bootstrap replicates and selecting variables by how consistently they survive. The 0.3.0 release reshaped it: a fast argument computes one cross-validated lambda on the full dataset instead of cross-validating inside every replicate, and selected_variables() gained a choice between the variable inclusion probability rule and a quantile rule based on bootstrap confidence intervals. Since then 0.4.0 exposed the bootstrap indices through bootstrap_samples(), and 0.5.0 extended the whole surface to multinomial responses, returning one list element per outcome level.

Read the full bolasso 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 →

bolasso vs nuggets: editorial side-by-side

B
bolasso
INFRA · APIS
0.0

Bootstrap lasso got a fast mode, a second selection rule, and multinomial support

◆ Current state

bolasso implements the bootstrapped lasso, refitting a regularized regression across bootstrap replicates and selecting variables by how consistently they survive. The 0.3.0 release reshaped it: a fast argument computes one cross-validated lambda on the full dataset instead of cross-validating inside every replicate, and selected_variables() gained a choice between the variable inclusion probability rule and a quantile rule based on bootstrap confidence intervals. Since then 0.4.0 exposed the bootstrap indices through bootstrap_samples(), and 0.5.0 extended the whole surface to multinomial responses, returning one list element per outcome level.

◆ Where it's heading

The package spent 2022 dormant after its initial releases and has been actively developed since late 2024, moving from a single algorithm toward a workbench. The additions cluster around inspection rather than estimation: tidy() for bootstrap-level coefficients, plot_selection_thresholds() for selection stability across thresholds, plot_selected_variables() for the surviving covariates, and now the extracted bootstrap indices. Documented gaps remain, with mgaussian unsupported and multinomial prediction limited to class output.

◆ Prediction

The two stated limitations in 0.5.0 - no mgaussian family and class-only multinomial prediction - are the most likely next targets, since the maintainer flagged both as possible later additions.

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 bolasso 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 bolasso or nuggets.

See all bolasso alternatives → · See all nuggets alternatives →

Recent activity from bolasso 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. 4mo agobolassoMultinomial responses supported across selection and plotting
  4. 5mo agonuggetsexplore() covers contrasts and correlations; dig_ancestors() added
  5. 6mo agonuggetsCritical explore() bug fixed; is_logicalish() added
  6. 6mo agonuggetsShiny deps moved to Suggests; BH and RcppThread dropped
  7. 8mo agonuggetscluster_associations() and add_interest() arrive; C++ condition parser
  8. 10mo agobolassoBootstrap indices exposed via bootstrap_samples()
  9. 1y agobolassoFast estimation mode and a second variable selection rule
  10. 4y agobolassoBolasso v0.2.0
  11. 4y agobolassoBolasso v0.1.0

Frequently asked questions

What is the difference between bolasso 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 bolasso 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 bolasso?

Top bolasso alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "bolasso alternatives" section above for the current picks, or visit /alternatives/bolasso 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.