parallelDist
parallelDist is in pure preservation mode — one build fix every few years.
Comparison · Analytics
SimInf vs weird
A side-by-side editorial comparison of SimInf and weird — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
SimInf vs weird: 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 weird?
weird rebuilt itself on distributional objects, and now the anomaly tooling composes with everything else.
An R package for anomaly detection and unusual-observation diagnostics, at four releases with a long gap between the 2024 patch and the 2026 major line. The current shape is set by 2.0.0, which refactored the package onto distributional objects and renamed its central concept from density_scores() to surprisals(). Since then the work has been filling that structure in: surprisals for more model classes, faster bandwidth and probability calculations, and new visual diagnostics.
SimInf vs weird: 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.
weird
0.0ANALYTICS
weird rebuilt itself on distributional objects, and now the anomaly tooling composes with everything else.
◆ Current state
An R package for anomaly detection and unusual-observation diagnostics, at four releases with a long gap between the 2024 patch and the 2026 major line. The current shape is set by 2.0.0, which refactored the package onto distributional objects and renamed its central concept from density_scores() to surprisals(). Since then the work has been filling that structure in: surprisals for more model classes, faster bandwidth and probability calculations, and new visual diagnostics.
◆ Where it's heading
The refactor onto a shared distribution representation is the decision everything else follows from. It let 2.1.0 add hdr() and parameters() methods for kde objects rather than bespoke accessors, and it let 3.0.0 bring in dist_mclust() to turn a Gaussian mixture model into the same object type — so a mixture, a kernel density estimate and a fitted distribution all flow through one interface. The 3.0.0 additions lean visual and multivariate: outlier maps plotting score distance against orthogonal distance, biplot projections with variable axes overlaid, and an augment() method for robust PCA objects. Dependencies have been shed steadily along the way — lookout, interpolation — while mvscale() moved out and then back in.
◆ Prediction
Expect surprisals() coverage to keep extending to further model classes, and the multivariate and robust-PCA diagnostics introduced in 3.0.0 to gain the same distributional-object treatment as the univariate side.
Alternatives to SimInf and weird
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 weird.
cairo
Cairo finally catches R's graphics engine, all the way to GE API 16.
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
See all SimInf alternatives → · See all weird alternatives →
Recent activity from SimInf and weird
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoweirdOutlier maps, biplot projections, and Gaussian mixtures as distributional objects
- 3mo agoweirdsurprisals() reaches glm objects; lookout dependency dropped
- 6mo agoweirdPackage refactored onto distributional objects; density_scores becomes surprisals
- 9mo agoSimInfAvoid memcpy on zero-length continuous state vector
- 9mo agoSimInfPMCMC fitting arrives; particle filter interface redesigned ⚡
- 2y agoSimInfDocumentation link anchors; parser dependency fix
- 2y agoSimInfModel parser gains variables and enumeration constants
- 2y agoweirdWine reviews dataset replaced with a fetch function
- 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 weird?
Both compete on the same themes — r-package — within Analytics. SimInf and weird 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 weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. SimInf and weird 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 weird?
Top weird alternatives in Analytics are ranked by recent ship velocity. Browse the "weird alternatives" section above for the current picks, or visit /alternatives/weird-r for the full list with editorial commentary on each.