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EpiNow2 vs spatstat.random

A side-by-side editorial comparison of EpiNow2 and spatstat.random — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-package

EpiNow2 vs spatstat.random: at a glance

FeatureEpiNow2spatstat.random
SectorAnalyticsAnalytics
Velocity score0.02.5
Sparks · 30d00
Top themesepidemiology, bayesian-modelling, reproduction-number, r-packagespatial-statistics, point-processes, simulation, r-package
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

What is EpiNow2?

EpiNow2 unified its model interface, then went back to deepen the estimators behind it

EpiNow2 estimates reproduction numbers, infections and delay distributions from incomplete epidemiological reporting data. 1.8.0 was the structural turning point: every main modelling function now returns a consistent S3 object with `fit`, `args` and `observations`, reachable through shared accessors. The releases either side of it work on estimator quality — accumulation of irregularly reported data in 1.7.0, and a substantial expansion of `estimate_truncation()` in 1.9.0.

Read the full EpiNow2 trajectory →

What is spatstat.random?

spatstat's simulation engine pushes point process generation into three dimensions

spatstat.random generates random point patterns and simulates point process models for the spatstat family. Its recent releases have moved along two lines at once: filling out three-dimensional simulation, and adding conditional simulation to the established cluster process generators. 3.5-1 is a narrow follow-up adding a random Dirichlet-Voronoi tessellation without edge effects.

Read the full spatstat.random trajectory →

EpiNow2 vs spatstat.random: editorial side-by-side

E
EpiNow2
ANALYTICS
0.0

EpiNow2 unified its model interface, then went back to deepen the estimators behind it

◆ Current state

EpiNow2 estimates reproduction numbers, infections and delay distributions from incomplete epidemiological reporting data. 1.8.0 was the structural turning point: every main modelling function now returns a consistent S3 object with `fit`, `args` and `observations`, reachable through shared accessors. The releases either side of it work on estimator quality — accumulation of irregularly reported data in 1.7.0, and a substantial expansion of `estimate_truncation()` in 1.9.0.

◆ Where it's heading

The package spent this window paying down interface debt and is now extending from the tidier base. Options that existed only for `estimate_infections()` have been propagated outward: `estimate_truncation()` gained the full `dist_spec` delay families, `obs_opts()` observation model selection between Poisson and negative binomial, and the `likelihood` and `return_likelihood` settings that make prior-only fits and loo-compatible output possible. Hardcoded assumptions are being replaced by specifiable ones in the same motion — the truncation model's additive noise term was a fixed `sigma ~ normal(0, 1)` prior and is now a `dist_spec` argument.

◆ Prediction

Expect the remaining modelling functions to keep converging on the shared options interface, since the last two releases have each moved another function onto it. A new `estimate_dist()` for interval-censored linelist data suggests delay estimation is the area still gaining surface.

S2.5

spatstat's simulation engine pushes point process generation into three dimensions

◆ Current state

spatstat.random generates random point patterns and simulates point process models for the spatstat family. Its recent releases have moved along two lines at once: filling out three-dimensional simulation, and adding conditional simulation to the established cluster process generators. 3.5-1 is a narrow follow-up adding a random Dirichlet-Voronoi tessellation without edge effects.

◆ Where it's heading

The clearest arc is dimensional. 3.5-0 carried inhomogeneous Poisson processes, non-uniform random points and Simple Sequential Inhibition into 3D in a single release, and the sibling geometry package followed two months later with more capabilities for three-dimensional point patterns. Alongside that, the generators have been gaining theoretical range — Gaussian random fields in 3.4-4, a new class of theoretical cluster process models and random diffusion in 3.5-0 — while earlier releases concentrated on conditional simulation and efficiency in the existing 2D routines.

◆ Prediction

Expect the 3D work to continue propagating into the model-fitting and geometry packages before spatstat.random adds another dimension-independent generator, since the 3D features here have already begun appearing downstream. The entries do not indicate which estimator gets 3D support next.

Alternatives to EpiNow2 and spatstat.random

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 EpiNow2 or spatstat.random.

See all EpiNow2 alternatives → · See all spatstat.random alternatives →

Recent activity from EpiNow2 and spatstat.random

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

  1. 20d agospatstat.randomEdge-effect-free random Dirichlet-Voronoi tessellation
  2. 1mo agoEpiNow2estimate_truncation gains full delay and observation options
  3. 2mo agospatstat.randomThree-dimensional point process simulation arrives
  4. 6mo agoEpiNow2Unified return objects and shared accessors across all models
  5. 6mo agospatstat.randomGaussian random field generation added
  6. 10mo agospatstat.randomrunifdisc efficiency and fixed-count simulation options
  7. 1y agospatstat.randomConditional simulation for the cluster process generators
  8. 1y agospatstat.randomFaster rpoispp for tessellation-defined intensity
  9. 1y agoEpiNow2Patch for an upstream rstan issue
  10. 1y agoEpiNow2Accumulation for irregularly reported data; unified priors
  11. 1y agoEpiNow2Matern kernel spectral density fix and GP prior revert
  12. 1y agoEpiNow2Gaussian Process model improvements and explicit defaults

Frequently asked questions

What is the difference between EpiNow2 and spatstat.random?

Both compete on the same themes — r-package — within Analytics. spatstat.random 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 EpiNow2 better than spatstat.random?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. spatstat.random 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 EpiNow2?

Top EpiNow2 alternatives in Analytics are ranked by recent ship velocity. Browse the "EpiNow2 alternatives" section above for the current picks, or visit /alternatives/epinow2 for the full list with editorial commentary on each.

What are the best alternatives to spatstat.random?

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