HydroPortailStats
France's national flood statistics, ported out of Fortran and into R.
A side-by-side editorial comparison of bolasso and finnts — release velocity, themes, recent moves, and the top alternatives to consider.
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.
Microsoft's automated forecasting framework, still mostly a one-maintainer effort.
finnts automates time-series forecasting end to end — feature engineering, model selection, hierarchical reconciliation — on a tidymodels backbone. Recent releases have concentrated on global models (one model fitted across many series) and on hierarchical reconciliation, which has needed repeated correction at weekly granularity. Release notes are auto-generated pull-request lists, so the detail lives in the PRs rather than in the changelog.
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.
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.
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.
finnts automates time-series forecasting end to end — feature engineering, model selection, hierarchical reconciliation — on a tidymodels backbone. Recent releases have concentrated on global models (one model fitted across many series) and on hierarchical reconciliation, which has needed repeated correction at weekly granularity. Release notes are auto-generated pull-request lists, so the detail lives in the PRs rather than in the changelog.
Cadence is roughly annual and the commit history is almost entirely one maintainer, with occasional outside contributions. The direction across the last four releases is consolidation of the forecasting internals — multi-horizon models, feature selection, reconciliation fixes — rather than new surface for users. The changelogs themselves are unedited PR dumps, which makes the arc harder to read than the work probably warrants.
Hierarchical reconciliation has produced a bug fix in three of the last four releases, so the next one likely touches it again; nothing in the entries points to a specific new capability.
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 finnts.
France's national flood statistics, ported out of Fortran and into R.
Sign, zero and narrative restrictions brought into the bsvars ecosystem.
Fast design-based estimators for experiments, coasting on CRAN patches.
The grammar of uncertainty visualization, now drawing the uncertainty in its own estimates.
IP address vectors for R that hit 1.0 and then went quiet.
A column-key toolkit for stitching decades of ecological field data into one table.
See all bolasso alternatives → · See all finnts alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Infra & APIs. bolasso and finnts are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). 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. bolasso and finnts are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Infra & APIs products to evaluate alongside.
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.
Top finnts alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "finnts alternatives" section above for the current picks, or visit /alternatives/finnts for the full list with editorial commentary on each.