tern.rbmi
Reference-based multiple imputation tables, shipping only what CRAN checks demand.
Comparison · Analytics
CMAQ vs weird
A side-by-side editorial comparison of CMAQ and weird — release velocity, themes, recent moves, and the top alternatives to consider.
CMAQ vs weird: at a glance
What is CMAQ?
CMAQ went global in v5.5, and has been patching that surface ever since.
CMAQ is the EPA's Community Multiscale Air Quality modeling system, used for regulatory and research air quality simulation. Its release rhythm is strictly two-tier: numbered major versions carry new science and fresh benchmark datasets, while the x.y.z.n updates carry bug fixes against documentation and benchmarks that stay pinned to the parent version. The current line is v5.5, which introduced CRACMM2 chemistry and coupling to MPAS-A meteorology, followed by three patch rollups.
What is weird?
weird rebuilt itself on distributional objects, and now the anomaly tooling composes with everything else.
An R package for anomaly detection and unusual-observation diagnostics, at four releases with a long gap between the 2024 patch and the 2026 major line. The current shape is set by 2.0.0, which refactored the package onto distributional objects and renamed its central concept from density_scores() to surprisals(). Since then the work has been filling that structure in: surprisals for more model classes, faster bandwidth and probability calculations, and new visual diagnostics.
CMAQ vs weird: editorial side-by-side
CMAQ
0.0ANALYTICS
CMAQ went global in v5.5, and has been patching that surface ever since.
◆ Current state
CMAQ is the EPA's Community Multiscale Air Quality modeling system, used for regulatory and research air quality simulation. Its release rhythm is strictly two-tier: numbered major versions carry new science and fresh benchmark datasets, while the x.y.z.n updates carry bug fixes against documentation and benchmarks that stay pinned to the parent version. The current line is v5.5, which introduced CRACMM2 chemistry and coupling to MPAS-A meteorology, followed by three patch rollups.
◆ Where it's heading
The v5.5 patches cluster around the newest and most sensitive components. ISAM source apportionment and DDM-3D sensitivity analysis account for corrections in every one of the three updates, and CRACMM2 needed fixes within months of release. That is the expected shape after a major version lands: the science is stable, the instrumentation built on top of it is not. Parallel I/O work in the latest patch suggests the global configurations are now being run at scales that expose throughput limits.
◆ Prediction
The next major version will fold these fixes in with new science, documentation and benchmark data — the release notes state this explicitly each time. Until then, expect further ISAM and DDM-3D corrections, which have appeared in every patch so far.
weird
0.0ANALYTICS
weird rebuilt itself on distributional objects, and now the anomaly tooling composes with everything else.
◆ Current state
An R package for anomaly detection and unusual-observation diagnostics, at four releases with a long gap between the 2024 patch and the 2026 major line. The current shape is set by 2.0.0, which refactored the package onto distributional objects and renamed its central concept from density_scores() to surprisals(). Since then the work has been filling that structure in: surprisals for more model classes, faster bandwidth and probability calculations, and new visual diagnostics.
◆ Where it's heading
The refactor onto a shared distribution representation is the decision everything else follows from. It let 2.1.0 add hdr() and parameters() methods for kde objects rather than bespoke accessors, and it let 3.0.0 bring in dist_mclust() to turn a Gaussian mixture model into the same object type — so a mixture, a kernel density estimate and a fitted distribution all flow through one interface. The 3.0.0 additions lean visual and multivariate: outlier maps plotting score distance against orthogonal distance, biplot projections with variable axes overlaid, and an augment() method for robust PCA objects. Dependencies have been shed steadily along the way — lookout, interpolation — while mvscale() moved out and then back in.
◆ Prediction
Expect surprisals() coverage to keep extending to further model classes, and the multivariate and robust-PCA diagnostics introduced in 3.0.0 to gain the same distributional-object treatment as the univariate side.
Alternatives to CMAQ and weird
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 CMAQ or weird.
tern.mmrm
An MMRM tabulation package that has published nothing since its 2024 CRAN releases.
ggmagnify
A single-purpose ggplot2 inset tool, refining the same three arguments.
symengine
An R symbolic-maths binding whose changelog is really the C++ core's release notes.
gtfstools
gtfstools stopped guarding its own object model and started accepting everyone else's.
quantities
The glue package that makes R carry units and uncertainty through the same calculation.
Recent activity from CMAQ and weird
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoweirdOutlier maps, biplot projections, and Gaussian mixtures as distributional objects
- 3mo agoweirdsurprisals() reaches glm objects; lookout dependency dropped
- 6mo agoweirdPackage refactored onto distributional objects; density_scores becomes surprisals
- 1y agoCMAQCMAQ 5.5.0.3 enables parallel I/O, fixes DDM-3D control files
- 1y agoCMAQCMAQ 5.5.0.2 corrects ISAM aerosol and cloud processing
- 1y agoCMAQCMAQ 5.5.0.1 fixes SAPRC mechanism runs and NLCD mapping
- 1y agoCMAQCMAQ 5.5 adds global simulation coupled to MPAS-A ⚡
- 1y agoCMAQCMAQ 5.4.0.5: minor ELMO output fixes
- 2y agoCMAQCMAQ 5.4.0.4 fixes RBSTATS under ROS3, corrects a molecular weight
- 2y agoweirdWine reviews dataset replaced with a fetch function
Frequently asked questions
What is the difference between CMAQ and weird?
They serve adjacent needs but don't currently overlap on shipped themes. CMAQ and weird 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 CMAQ better than weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. CMAQ and weird 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 CMAQ?
Top CMAQ alternatives in Analytics are ranked by recent ship velocity. Browse the "CMAQ alternatives" section above for the current picks, or visit /alternatives/cmaq for the full list with editorial commentary on each.
What are the best alternatives to weird?
Top weird alternatives in Analytics are ranked by recent ship velocity. Browse the "weird alternatives" section above for the current picks, or visit /alternatives/weird-r for the full list with editorial commentary on each.