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DHARMa vs modelbpp

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

Shared themes:r-package

DHARMa vs modelbpp: at a glance

FeatureDHARMamodelbpp
SectorAnalyticsAnalytics
Velocity score0.02.5
Sparks · 30d00
Top themesresidual-diagnostics, glmm, breaking-change, bayesianstructural-equation-modeling, statistics, r-package, cran
Last editorial update44m ago1h 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 modelbpp?

A structural-equation model comparison package whose feed carries links, not release notes.

modelbpp computes model-implied Bayesian posterior probabilities for structural equation models, one of several R packages from the same author covering moderation, mediation and model-comparison workflows. Its release feed is not a changelog: every entry points at the package website rather than describing what changed, so the substance of each release is not visible here. Version numbering has moved steadily from 0.1.x to 0.4.0 across roughly three years.

Read the full modelbpp trajectory →

DHARMa vs modelbpp: 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.

M
modelbpp
ANALYTICS
2.5

A structural-equation model comparison package whose feed carries links, not release notes.

◆ Current state

modelbpp computes model-implied Bayesian posterior probabilities for structural equation models, one of several R packages from the same author covering moderation, mediation and model-comparison workflows. Its release feed is not a changelog: every entry points at the package website rather than describing what changed, so the substance of each release is not visible here. Version numbering has moved steadily from 0.1.x to 0.4.0 across roughly three years.

◆ Where it's heading

What can be read from this feed is cadence rather than content — releases clustered noticeably more tightly through 2026 than in the preceding two years, with three in five months against two in the prior eighteen. Because the entries carry no detail, any statement about what is being built would be speculation. The pattern of a stable CRAN package accelerating its release rate is the only reliable signal available.

◆ Prediction

The feed does not describe its changes, so the direction of development cannot be read from these entries; the accelerating 2026 cadence is the only thing it supports.

Alternatives to DHARMa and modelbpp

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 modelbpp.

See all DHARMa alternatives → · See all modelbpp alternatives →

Recent activity from DHARMa and modelbpp

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

  1. 29d agomodelbppCRAN Release 0.4.0
  2. 2mo agoDHARMaConditional simulation becomes the GLMM default, changing residuals
  3. 3mo agomodelbppCRAN Release 0.3.0
  4. 5mo agomodelbppCRAN Release 0.2.0
  5. 1y agoDHARMaDHARMa 0.4.7
  6. 2y agomodelbppCRAN Release 0.1.3
  7. 2y agomodelbppCRAN Release 0.1.2
  8. 3y agoDHARMaDHARMa 0.4.6
  9. 4y agoDHARMaDHARMa 0.4.5
  10. 4y agoDHARMaDHARMa 0.4.4
  11. 5y agoDHARMaDHARMa 0.4.3

Frequently asked questions

What is the difference between DHARMa and modelbpp?

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

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

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