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DHARMa vs SSN2

A side-by-side editorial comparison of DHARMa and SSN2 — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-package

DHARMa vs SSN2: at a glance

FeatureDHARMaSSN2
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesresidual-diagnostics, glmm, breaking-change, bayesianspatial-statistics, stream-networks, kriging, large-data
Last editorial update1h ago22m ago
WebsiteVisit →Visit →

What is DHARMa?

DHARMa changed how GLMM residuals are simulated, so the same code now returns different numbers.

DHARMa generates scaled quantile residuals for fitted GLMMs and runs the dispersion, uniformity, and autocorrelation tests built on them. Version 0.5.0 changed the default simulation for hierarchical models from the model's own default, mostly unconditional, to conditional simulation, and states plainly that residuals will differ from those computed by older versions. The same release added brms to the supported model set and reworked how predictors are passed to plotting and testing functions.

Read the full DHARMa trajectory →

What is SSN2?

Stream-network spatial models learning to run on data that no longer fits in memory

SSN2 fits spatial statistical models on stream networks, where covariance follows flow-connected distance along the network rather than straight-line distance. It is the maintained successor to the original SSN package, published through JOSS in 2024, and leans on spmodel for its underlying model machinery. Recent releases have concentrated on the constraint that binds this class of model hardest: the distance matrix.

Read the full SSN2 trajectory →

DHARMa vs SSN2: editorial side-by-side

D
DHARMa
ANALYTICS
0.0

DHARMa changed how GLMM residuals are simulated, so the same code now returns different numbers.

◆ Current state

DHARMa generates scaled quantile residuals for fitted GLMMs and runs the dispersion, uniformity, and autocorrelation tests built on them. Version 0.5.0 changed the default simulation for hierarchical models from the model's own default, mostly unconditional, to conditional simulation, and states plainly that residuals will differ from those computed by older versions. The same release added brms to the supported model set and reworked how predictors are passed to plotting and testing functions.

◆ Where it's heading

The package has spent several releases widening which model backends it can diagnose, from glmmTMB through mgcv, phylolm and now brms, while methodological work has gone into handling correlated residuals via the rotation argument. Version 0.5.0 shifts from adding coverage to changing defaults for statistical power. The formula interface arriving across plotResiduals, testCategorical, testQuantiles and the autocorrelation tests suggests the API is being unified rather than extended function by function.

◆ Prediction

The next releases will likely broaden brms support past the simple-model restriction and continue converting remaining functions to the formula interface.

S
SSN2
ANALYTICS
0.0

Stream-network spatial models learning to run on data that no longer fits in memory

◆ Current state

SSN2 fits spatial statistical models on stream networks, where covariance follows flow-connected distance along the network rather than straight-line distance. It is the maintained successor to the original SSN package, published through JOSS in 2024, and leans on spmodel for its underlying model machinery. Recent releases have concentrated on the constraint that binds this class of model hardest: the distance matrix.

◆ Where it's heading

The first year was about establishing credibility and interoperability — a JOSS review, geopackage import support, deprecation of the SSN-to-SSN2 bridge, marginal means through emmeans. The 2025 releases turn to scale, moving distance matrices onto disk via filematrix and routing estimation and prediction through the local approximation. The 0.4.0 default change is the visible consequence: the neighbourhood size rises from 100 to 200, buying accuracy now that the surrounding machinery can afford it.

◆ Prediction

With the large-data path established and its default just retuned, the next work most likely tightens that approximation further or extends it to the model classes the local argument does not yet cover.

Alternatives to DHARMa and SSN2

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 DHARMa or SSN2.

See all DHARMa alternatives → · See all SSN2 alternatives →

Recent activity from DHARMa and SSN2

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

  1. 2mo agoDHARMaConditional simulation becomes the GLMM default, changing residuals
  2. 9mo agoSSN2Local neighbourhood default doubled to 200
  3. 10mo agoSSN2partition_factor regression fixed, spmodel floor raised
  4. 0y agoSSN2On-disk distance matrices lift the large-data ceiling
  5. 1y agoDHARMaDHARMa 0.4.7
  6. 1y agoSSN2Numeric stability fix for beta-family models
  7. 2y agoSSN2Geopackage import support and the JOSS review release
  8. 2y agoSSN2Snapshot tag marking JOSS acceptance
  9. 3y agoDHARMaDHARMa 0.4.6
  10. 4y agoDHARMaDHARMa 0.4.5
  11. 4y agoDHARMaDHARMa 0.4.4
  12. 5y agoDHARMaDHARMa 0.4.3

Frequently asked questions

What is the difference between DHARMa and SSN2?

Both compete on the same themes — r-package — within Analytics. DHARMa and SSN2 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 DHARMa better than SSN2?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. DHARMa and SSN2 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 DHARMa?

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

What are the best alternatives to SSN2?

Top SSN2 alternatives in Analytics are ranked by recent ship velocity. Browse the "SSN2 alternatives" section above for the current picks, or visit /alternatives/ssn2 for the full list with editorial commentary on each.