cairo
Cairo finally catches R's graphics engine, all the way to GE API 16.
Comparison · Analytics
SimInf vs spatstat.model
A side-by-side editorial comparison of SimInf and spatstat.model — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
SimInf vs spatstat.model: 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 spatstat.model?
spatstat's inference layer builds out determinantal and cluster process fitting
spatstat.model fits point process models and provides the diagnostics that go with them. The recent window is dominated by determinantal point process work — a variance-covariance matrix and more diagnostics in 3.7-2, additional `intensity` and `repul` methods in 3.7-1, and ROC curves for determinantal models in 3.5-0. Cluster and Cox process inference has advanced in parallel, with Waagepetersen's composite likelihood arriving in 3.6-1.
SimInf vs spatstat.model: 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.
spatstat.model
2.5ANALYTICS
spatstat's inference layer builds out determinantal and cluster process fitting
◆ Current state
spatstat.model fits point process models and provides the diagnostics that go with them. The recent window is dominated by determinantal point process work — a variance-covariance matrix and more diagnostics in 3.7-2, additional `intensity` and `repul` methods in 3.7-1, and ROC curves for determinantal models in 3.5-0. Cluster and Cox process inference has advanced in parallel, with Waagepetersen's composite likelihood arriving in 3.6-1.
◆ Where it's heading
The pattern is that model classes enter the package as fitting machinery first and only later gain the apparatus that makes them usable in practice — standard errors, diagnostics, residuals, model checking. Determinantal processes are visibly midway through that progression, reaching variance-covariance estimation only in the most recent release. Around this, the package has been broadening where models can be fitted at all: replicated point patterns on linear networks in 3.5-0, extended spatial logistic regression, and conversion of recursively partitioned models to tessellations.
◆ Prediction
Expect determinantal model support to keep filling out along the same path other model classes took, since variance estimation has only just arrived and partial residuals already exist for the cluster and Cox families. The entries do not signal a move into three dimensions here, unlike the geometry and simulation packages.
Alternatives to SimInf and spatstat.model
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 spatstat.model.
rjdqa
rjdqa keeps refining one screen: the seasonal adjustment quality dashboard
epikit
epikit narrows to field-epidemiology helpers, handing proportions to a sibling package
mutagen
A young package porting Stata's egen row-wise helpers to the tidyverse, one function per release
broman
A statistician's personal toolbox, growing one plotting utility at a time
qtl
R/qtl is in pure custodial mode: every recent release answers a compiler, not a user
See all SimInf alternatives → · See all spatstat.model alternatives →
Recent activity from SimInf and spatstat.model
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 18d agospatstat.modelVariance-covariance and diagnostics for determinantal models
- 2mo agospatstat.modelMore intensity and repul methods; boundary-aware predictions
- 6mo agospatstat.modelComposite likelihood for cluster processes
- 8mo agospatstat.modelReplicated network models and partial residuals
- 9mo agoSimInfAvoid memcpy on zero-length continuous state vector
- 9mo agoSimInfPMCMC fitting arrives; particle filter interface redesigned ⚡
- 10mo agospatstat.modelintensity.ppm improvements for Geyer models
- 1y agospatstat.modelROC curve support substantially extended
- 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 spatstat.model?
Both compete on the same themes — r-package — within Analytics. spatstat.model is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is SimInf better than spatstat.model?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. spatstat.model is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. 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 spatstat.model?
Top spatstat.model alternatives in Analytics are ranked by recent ship velocity. Browse the "spatstat.model alternatives" section above for the current picks, or visit /alternatives/spatstat-model for the full list with editorial commentary on each.