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Comparison · Analytics

collapse vs rjdqa

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

Shared themes:r-package

collapse vs rjdqa: at a glance

Featurecollapserjdqa
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesdata-transformation, performance, simd, grouped-statisticsofficial-statistics, seasonal-adjustment, quality-assurance, r-package
Last editorial update5h ago1h ago
WebsiteVisit →Visit →

What is collapse?

collapse got a JSS paper and a 7x fmean speedup in the same release.

collapse provides fast grouped statistical computing and data transformation for R, built on a C backend with its own grouping, hashing and aggregation primitives. The 2.1.x line is a maintenance and optimization series: SIMD multiple-accumulator work delivering roughly 2x on fsum() and 7x on fmean() for systems without OpenMP, a custom internal unlist() with better attribute preservation, and a steady stream of correctness fixes in collap(), pivot() and roworderv().

Read the full collapse trajectory →

What is rjdqa?

rjdqa keeps refining one screen: the seasonal adjustment quality dashboard

rjdqa builds quality assessment dashboards for seasonal adjustment models produced by JDemetra+, aimed at official statisticians reviewing adjusted series. Essentially all development goes into two functions, simple_dashboard() and its denser variant simple_dashboard2(). Version 0.1.6 adds parameters to append observations to the forecast and to control whether the residual trading-days test is printed, defaulting to monthly series only, plus outlier table layout work and user-defined calendar regressor support in sc_dashboard().

Read the full rjdqa trajectory →

collapse vs rjdqa: editorial side-by-side

C
collapse
ANALYTICS
0.0

collapse got a JSS paper and a 7x fmean speedup in the same release.

◆ Current state

collapse provides fast grouped statistical computing and data transformation for R, built on a C backend with its own grouping, hashing and aggregation primitives. The 2.1.x line is a maintenance and optimization series: SIMD multiple-accumulator work delivering roughly 2x on fsum() and 7x on fmean() for systems without OpenMP, a custom internal unlist() with better attribute preservation, and a steady stream of correctness fixes in collap(), pivot() and roworderv().

◆ Where it's heading

The package is consolidating institutionally as much as technically. The repository moved to the fastverse organization with multiple people granted access, the Journal of Statistical Software paper landed as the primary citation, and documentation now includes an AI-generated interactive layer. Technically the focus is the hashing and grouping core — the decision to treat -0 and 0 as equal across funique(), group(), fmatch(), fmode() and their derivatives was made in sync with an equivalent change in Rcpp, and accepted a measured 3% cost to get it. The last release with breaking changes sits outside this six-entry window.

◆ Prediction

Expect further targeted performance work on the grouped statistical functions and continued small correctness fixes; the governance move to fastverse suggests contribution volume rather than direction is what the maintainer is managing.

R
rjdqa
ANALYTICS
0.0

rjdqa keeps refining one screen: the seasonal adjustment quality dashboard

◆ Current state

rjdqa builds quality assessment dashboards for seasonal adjustment models produced by JDemetra+, aimed at official statisticians reviewing adjusted series. Essentially all development goes into two functions, simple_dashboard() and its denser variant simple_dashboard2(). Version 0.1.6 adds parameters to append observations to the forecast and to control whether the residual trading-days test is printed, defaulting to monthly series only, plus outlier table layout work and user-defined calendar regressor support in sc_dashboard().

◆ Where it's heading

The package has converged on a single deliverable and is tuning it against reviewer practice. Each release adds a parameter that lets the analyst include or exclude one element of the dashboard, or adjusts how densely information is packed into the fixed space of the layout. The td_effect default — print the test only for monthly series — is characteristic: the knowledge about when a diagnostic is meaningful is being encoded into the tool rather than left to the reader.

◆ Prediction

The pattern of adding one toggle per diagnostic per release points at the same thing again, most likely another test given a conditional default, rather than a new dashboard function alongside the two that exist.

Alternatives to collapse and rjdqa

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 collapse or rjdqa.

See all collapse alternatives → · See all rjdqa alternatives →

Recent activity from collapse and rjdqa

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

  1. 2mo agocollapseSIMD accumulators give fmean a 7x speedup without OpenMP
  2. 7mo agocollapseNegative zero now hashes equal to zero across the package
  3. 8mo agocollapsecollap() no longer double-aggregates external weights
  4. 9mo agorjdqaForecast observations and conditional trading-days test in dashboards
  5. 9mo agocollapseCustom unlist() preserves attributes
  6. 0y agocollapseAssorted bug fixes
  7. 1y agocollapsena_insert gains by-reference mode; gsplit and pivot speed up
  8. 1y agorjdqaFix tail() usage on ts objects
  9. 2y agorjdqaFix dependency minimums and outlier ordering
  10. 2y agorjdqasimple_dashboard2() added; deprecated sa_dashboard() removed
  11. 2y agorjdqasimple_dashboard() introduced; sa_dashboard() deprecated
  12. 7y agorjdqaFirst CRAN release

Frequently asked questions

What is the difference between collapse and rjdqa?

Both compete on the same themes — r-package — within Analytics. collapse and rjdqa 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.

Is collapse better than rjdqa?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. collapse and rjdqa 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 Analytics products to evaluate alongside.

What are the best alternatives to collapse?

Top collapse alternatives in Analytics are ranked by recent ship velocity. Browse the "collapse alternatives" section above for the current picks, or visit /alternatives/collapse-r for the full list with editorial commentary on each.

What are the best alternatives to rjdqa?

Top rjdqa alternatives in Analytics are ranked by recent ship velocity. Browse the "rjdqa alternatives" section above for the current picks, or visit /alternatives/rjdqa-r for the full list with editorial commentary on each.