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collapse vs SimInf

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

Shared themes:r-package

collapse vs SimInf: at a glance

FeaturecollapseSimInf
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesdata-transformation, performance, simd, grouped-statisticsepidemiology, stochastic-simulation, bayesian-inference, 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 SimInf?

SimInf 10.0 turns an epidemic simulator into a tool that fits models to real time series

SimInf simulates stochastic disease spread over networks of nodes, with a model parser that compiles user-specified transitions to C. Version 10.0.0 was a deliberate major break: the SimInf_pfilter S4 class and the bootstrap filtering interface were redesigned, a replicates slot was added to SimInf_model, a multi-particle variant of the split-step solver arrived, and the package gained Particle Markov Chain Monte Carlo fitting against observed time series. The follow-up 10.1.0 is a single zero-length memcpy fix found by CRAN's M1 checks.

Read the full SimInf trajectory →

collapse vs SimInf: 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.

S
SimInf
ANALYTICS
0.0

SimInf 10.0 turns an epidemic simulator into a tool that fits models to real time series

◆ Current state

SimInf simulates stochastic disease spread over networks of nodes, with a model parser that compiles user-specified transitions to C. Version 10.0.0 was a deliberate major break: the SimInf_pfilter S4 class and the bootstrap filtering interface were redesigned, a replicates slot was added to SimInf_model, a multi-particle variant of the split-step solver arrived, and the package gained Particle Markov Chain Monte Carlo fitting against observed time series. The follow-up 10.1.0 is a single zero-length memcpy fix found by CRAN's M1 checks.

◆ Where it's heading

The package has been moving from simulation toward inference for several releases. The 9.x line built the input side — utilities for cleaning raw individual event data, variables and enumeration constants in the model parser — and 10.0.0 closed the loop by making the simulator fittable to data through PMCMC. The version number was incremented precisely because that required breaking the particle filter interface.

◆ Prediction

Fitting machinery this new usually needs a second pass on usability, so the next releases most likely focus on diagnostics and documentation around PMCMC rather than on the simulation core, which has been stable across the whole 9.x and 10.x history.

Alternatives to collapse and SimInf

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 SimInf.

See all collapse alternatives → · See all SimInf alternatives →

Recent activity from collapse and SimInf

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 agoSimInfAvoid memcpy on zero-length continuous state vector
  5. 9mo agoSimInfPMCMC fitting arrives; particle filter interface redesigned
  6. 9mo agocollapseCustom unlist() preserves attributes
  7. 0y agocollapseAssorted bug fixes
  8. 1y agocollapsena_insert gains by-reference mode; gsplit and pivot speed up
  9. 2y agoSimInfDocumentation link anchors; parser dependency fix
  10. 2y agoSimInfModel parser gains variables and enumeration constants
  11. 2y agoSimInfindividual_events() added for raw event data cleaning
  12. 3y agoSimInfConfigure script uses R to locate the compiler

Frequently asked questions

What is the difference between collapse and SimInf?

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

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

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