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distributional vs SimInf

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

Shared themes:r-package

distributional vs SimInf: at a glance

FeaturedistributionalSimInf
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesr-package, probability-distributions, distribution-arithmetic, numerical-methodsepidemiology, stochastic-simulation, bayesian-inference, r-package
Last editorial update3h ago54m ago
WebsiteVisit →Visit →

What is distributional?

distributional taught + and - to work on any pair of distributions, closing the algebra it started with.

The R package providing vectorised distribution objects — the substrate that forecasting and anomaly tooling in the same ecosystem builds on. Cadence has picked up sharply, with four releases in the six months to June 2026 against roughly one a year before that. Two kinds of work alternate: adding distribution families (Dirichlet, Horseshoe, Laplace, multivariate t, g-and-k, the extreme-value pair) and deepening what can be computed generically across all of them.

Read the full distributional 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 →

distributional vs SimInf: editorial side-by-side

D0.0

distributional taught + and - to work on any pair of distributions, closing the algebra it started with.

◆ Current state

The R package providing vectorised distribution objects — the substrate that forecasting and anomaly tooling in the same ecosystem builds on. Cadence has picked up sharply, with four releases in the six months to June 2026 against roughly one a year before that. Two kinds of work alternate: adding distribution families (Dirichlet, Horseshoe, Laplace, multivariate t, g-and-k, the extreme-value pair) and deepening what can be computed generically across all of them.

◆ Where it's heading

The generic-computation thread is the one that matters and it has been building steadily: a Monte Carlo default method for cdf(), has_symmetry() to let algorithms specialise, hdr() moving to exact results for symmetric distributions and 4096 quantiles elsewhere, open-versus-closed support intervals. Version 0.8.0 is where that thread arrives somewhere — arithmetic on arbitrary distributions, with closed forms used when they exist and numerical convolution when they do not. The package is positioning itself as a computational layer rather than a catalogue, which is consistent with how weird and the forecasting packages consume it.

◆ Prediction

Expect the numerical machinery behind dist_convolved() to be reused for other operators, and more generics like has_symmetry() that let downstream algorithms take exact paths when a distribution supports them.

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 distributional 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 distributional or SimInf.

See all distributional alternatives → · See all SimInf alternatives →

Recent activity from distributional and SimInf

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

  1. 1mo agodistributionalConditional S3 registration so the package loads on R before 4.3
  2. 1mo agodistributionalDistribution arithmetic: FFT convolution behind the + and - operators
  3. 2mo agodistributionalVectorised p in quantile() for inflated distributions; open brackets on infinite bounds
  4. 5mo agodistributionalDirichlet and Horseshoe distributions added
  5. 7mo agodistributionalhas_symmetry() generic, exact HDRs for symmetric distributions
  6. 9mo agoSimInfAvoid memcpy on zero-length continuous state vector
  7. 9mo agoSimInfPMCMC fitting arrives; particle filter interface redesigned
  8. 1y agodistributionalMonte Carlo cdf() default method; g-and-k, g-and-h and extreme-value families
  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 distributional and SimInf?

Both compete on the same themes — r-package — within Analytics. distributional 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 distributional better than SimInf?

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

Top distributional alternatives in Analytics are ranked by recent ship velocity. Browse the "distributional alternatives" section above for the current picks, or visit /alternatives/distributional-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.