← Back to home
Comparison · Analytics

collapse vs filearray

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

Shared themes:performance

collapse vs filearray: at a glance

Featurecollapsefilearray
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesdata-transformation, performance, simd, grouped-statisticson-disk-arrays, memory-safety, c++, performance
Last editorial update1h ago2h 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 filearray?

The on-disk array layer under RAVE spends its releases hunting segfaults.

filearray stores large arrays on disk and reads them back with little memory overhead, serving as the storage substrate for the RAVE intracranial EEG stack. The 0.2.2 release fixes out-of-bound indexing that caused segfaults along certain margins and an ASAN-flagged signed integer overflow in the load path. The user-facing API has been stable since 0.1.6.

Read the full filearray trajectory →

collapse vs filearray: 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.

F
filearray
ANALYTICS
0.0

The on-disk array layer under RAVE spends its releases hunting segfaults.

◆ Current state

filearray stores large arrays on disk and reads them back with little memory overhead, serving as the storage substrate for the RAVE intracranial EEG stack. The 0.2.2 release fixes out-of-bound indexing that caused segfaults along certain margins and an ASAN-flagged signed integer overflow in the load path. The user-facing API has been stable since 0.1.6.

◆ Where it's heading

This is infrastructure whose release history reads as a memory-safety log: unprotected C++ variables, buffer sizes exceeding array length, allocations one byte short, endianness on big-endian platforms, and now out-of-bound margins caught by sanitizers. The one sustained feature direction is reducing the cost of operating on arrays too large for memory — lazy operator evaluation through a proxy class, fmap-style application, and marginal collapse. Portability work has steadily removed hard requirements, dropping the C++11 declaration and swapping OpenMP for TinyThreads to get parallelism on macOS.

◆ Prediction

Expect continued sanitizer-driven patches rather than new interfaces; the three-year gap before 0.2.2 suggests releases now arrive only when a crash or a CRAN check demands one.

Alternatives to collapse and filearray

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 filearray.

See all collapse alternatives → · See all filearray alternatives →

Recent activity from collapse and filearray

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

  1. 2mo agofilearraySegfault on out-of-bound margins fixed
  2. 2mo agocollapseSIMD accumulators give fmean a 7x speedup without OpenMP
  3. 7mo agocollapseNegative zero now hashes equal to zero across the package
  4. 8mo agocollapsecollap() no longer double-aggregates external weights
  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. 3y agofilearrayLazy operator evaluation and macOS parallelism
  9. 3y agofilearraySequential read bug in fmap corrected
  10. 4y agofilearrayPartition limit removed by opening files on demand
  11. 4y agofilearrayHeader signatures and symbolic link detection
  12. 4y agofilearrayFlush timing left to the operating system

Frequently asked questions

What is the difference between collapse and filearray?

Both compete on the same themes — performance — within Analytics. collapse and filearray 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 filearray?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. collapse and filearray 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 filearray?

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