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paleobuddy vs spatstat.model

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

paleobuddy vs spatstat.model: at a glance

Featurepaleobuddyspatstat.model
SectorAnalyticsAnalytics
Velocity score0.02.5
Sparks · 30d00
Top themesphylogenetics, diversification, fossil-record, simulationspatial-statistics, point-processes, model-fitting, r-package
Last editorial update1h ago10h ago
WebsiteVisit →Visit →

What is paleobuddy?

paleobuddy can now simulate trait-dependent diversification, not just birth-death.

paleobuddy simulates diversification, fossil records and phylogenetic trees, with rates that can be arbitrary functions of time — its founding idea, implemented through rexp.var() generalizing exponential and Weibull draws. The 1.1.0 release adds state-dependent speciation and extinction simulation at roughly MuHiSSE generality, and lets simulations stop at a target number of extant species instead of conditioning on time.

Read the full paleobuddy trajectory →

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.

Read the full spatstat.model trajectory →

paleobuddy vs spatstat.model: editorial side-by-side

P
paleobuddy
ANALYTICS
0.0

paleobuddy can now simulate trait-dependent diversification, not just birth-death.

◆ Current state

paleobuddy simulates diversification, fossil records and phylogenetic trees, with rates that can be arbitrary functions of time — its founding idea, implemented through rexp.var() generalizing exponential and Weibull draws. The 1.1.0 release adds state-dependent speciation and extinction simulation at roughly MuHiSSE generality, and lets simulations stop at a target number of extant species instead of conditioning on time.

◆ Where it's heading

Releases track the maintainer's publications rather than a product cadence — 1.0.0 accompanied the MEE manuscript, 1.0.0.1 exists purely as a Zenodo citation anchor, and 1.1.0 is stated as going with a paper on SSE model accuracy for trees including fossil data. That framing sets the direction: the package grows whichever capability the next study needs to test. The stated SSE limits, no quantitative traits and no cladogenetic transitions, mark exactly where that boundary currently sits.

◆ Prediction

Quantitative traits and cladogenetic transitions are named as missing, which makes them the obvious next targets, though on this history the timing will follow a paper rather than a roadmap.

S2.5

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 paleobuddy 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 paleobuddy or spatstat.model.

See all paleobuddy alternatives → · See all spatstat.model alternatives →

Recent activity from paleobuddy and spatstat.model

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

  1. 18d agospatstat.modelVariance-covariance and diagnostics for determinantal models
  2. 2mo agospatstat.modelMore intensity and repul methods; boundary-aware predictions
  3. 6mo agospatstat.modelComposite likelihood for cluster processes
  4. 8mo agospatstat.modelReplicated network models and partial residuals
  5. 10mo agospatstat.modelintensity.ppm improvements for Geyer models
  6. 1y agospatstat.modelROC curve support substantially extended
  7. 1y agopaleobuddypaleobuddy 1.1.0 adds SSE trait-dependent simulation
  8. 3y agopaleobuddypaleobuddy 1.0.0.1: Zenodo citation release
  9. 4y agopaleobuddypaleobuddy 1.0.0: first release with time-varying rates

Frequently asked questions

What is the difference between paleobuddy and spatstat.model?

They serve adjacent needs but don't currently overlap on shipped themes. 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 paleobuddy 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 paleobuddy?

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