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Comparison · Analytics
SimInf vs spmodel
A side-by-side editorial comparison of SimInf and spmodel — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
SimInf vs spmodel: at a glance
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.
What is spmodel?
Spatial regression in R, adding block kriging and then tuning the numerics underneath it
spmodel fits spatial linear and generalised linear models, for both point-referenced and areal data, with prediction and diagnostics attached. Block prediction arrived in 0.11.0 and the releases since have refined it. The most recent release changes optimiser behaviour: the default Nelder-Mead relative stopping tolerance tightens from 1e-4 to 1e-6 to reduce convergence on local rather than global maxima.
SimInf vs spmodel: editorial side-by-side
SimInf
0.0ANALYTICS
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.
spmodel
0.0ANALYTICS
Spatial regression in R, adding block kriging and then tuning the numerics underneath it
◆ Current state
spmodel fits spatial linear and generalised linear models, for both point-referenced and areal data, with prediction and diagnostics attached. Block prediction arrived in 0.11.0 and the releases since have refined it. The most recent release changes optimiser behaviour: the default Nelder-Mead relative stopping tolerance tightens from 1e-4 to 1e-6 to reduce convergence on local rather than global maxima.
◆ Where it's heading
Two threads run in parallel. The first is expanding what can be predicted — point predictions, then areal averages over a region via block kriging, then better accuracy and efficiency for that path as the block size default moved from 1000 to 4000 in 0.12.0. The second is numerical trustworthiness, and it is unusually prominent here: a range-constraint option for stability in 0.9.0, a corrected log determinant of the fixed effects in the restricted log likelihood in 0.11.0, a cloud semivariogram that had been doubling the semivariance fixed in 0.11.1, and now a tighter optimiser tolerance. Several of these silently changed results before they were caught.
◆ Prediction
Expect the maintainers to keep publishing explicit reproduction instructions alongside numerical default changes, as 0.13.0 does by documenting the `control = list(reltol = 1e-4)` escape hatch. The entries give no signal of expansion beyond the current model families.
Alternatives to SimInf and spmodel
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 SimInf or spmodel.
grex
grex is a lookup table with a version number — it ships when the annotation moves.
protr
protr's feature set is finished; the work now is surviving Bioconductor's churn.
seriation
seriation stopped shipping algorithms and started shipping a way to pick between them.
arulesViz
arulesViz finished its move to ggplot2 and has been coasting on maintenance since.
EvoPhylo
EvoPhylo added BEAST2 alongside MrBayes, then went quiet for two years.
See all SimInf alternatives → · See all spmodel alternatives →
Recent activity from SimInf and spmodel
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agospmodelTighter optimiser tolerance to avoid local maxima
- 6mo agospmodelEmpirical autocovariance function and better block kriging accuracy
- 9mo agoSimInfAvoid memcpy on zero-length continuous state vector
- 9mo agospmodelCloud semivariogram doubling fixed; geometry warnings added
- 9mo agoSimInfPMCMC fitting arrives; particle filter interface redesigned ⚡
- 1y agospmodelBlock kriging for areal averages and their uncertainty ⚡
- 1y agospmodelRobust semivariogram and new covariance types for areal models
- 1y agospmodelRange constraint option and redefined covariance type names
- 2y agoSimInfDocumentation link anchors; parser dependency fix
- 2y agoSimInfModel parser gains variables and enumeration constants
- 2y agoSimInfindividual_events() added for raw event data cleaning
- 3y agoSimInfConfigure script uses R to locate the compiler
Frequently asked questions
What is the difference between SimInf and spmodel?
Both compete on the same themes — r-package — within Analytics. SimInf and spmodel 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 SimInf better than spmodel?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. SimInf and spmodel 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 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.
What are the best alternatives to spmodel?
Top spmodel alternatives in Analytics are ranked by recent ship velocity. Browse the "spmodel alternatives" section above for the current picks, or visit /alternatives/spmodel for the full list with editorial commentary on each.