compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
A side-by-side editorial comparison of DHARMa and mritc — release velocity, themes, recent moves, and the top alternatives to consider.
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.
A dormant MRI tissue-classification package revived under a new maintainer.
mritc performs MRI tissue classification in R using Gaussian mixture and hidden Markov models. After a long dormancy it changed hands to a new maintainer, and the three releases in this window all land within weeks of each other — two of them seconds apart, a backfill of the handover release alongside the first substantive one. The work so far is modernisation rather than new methodology.
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.
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.
The next releases will likely broaden brms support past the simple-model restriction and continue converting remaining functions to the formula interface.
mritc performs MRI tissue classification in R using Gaussian mixture and hidden Markov models. After a long dormancy it changed hands to a new maintainer, and the three releases in this window all land within weeks of each other — two of them seconds apart, a backfill of the handover release alongside the first substantive one. The work so far is modernisation rather than new methodology.
The clear direction is reducing what the package demands of the systems it installs on: heavyweight visualisation dependencies moved to optional, tkrplot dropped entirely, and the default plotting backend switched to a package already present in the dependency tree. A test suite and coverage tooling arrived where there had been none. The remaining releases are CRAN-check fallout from that restructuring, which is the expected shape of a revival.
Expect further consolidation under the new maintainer — CRAN check fixes and test coverage — before any change to the classification methods themselves.
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 mritc.
An MLE package rebuilt around composable solvers, then renamed to match.
nabla dropped its C++ engine to chase exact derivatives at any order.
Eight months from first release to keyring caching and workload identity.
A research-project workflow package where the interesting work is in the plumbing.
A cyclomatic complexity checker that ships once every couple of years, and lands when it does.
Extreme value sampling in pure upkeep mode, mostly answering to Rcpp and CRAN.
See all DHARMa alternatives → · See all mritc alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. mritc is currently shipping more aggressively (velocity 5.0 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mritc is currently shipping more aggressively (velocity 5.0 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.
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.
Top mritc alternatives in Analytics are ranked by recent ship velocity. Browse the "mritc alternatives" section above for the current picks, or visit /alternatives/mritc for the full list with editorial commentary on each.