tern.rbmi
Reference-based multiple imputation tables, shipping only what CRAN checks demand.
A side-by-side editorial comparison of CMAQ and distributional — release velocity, themes, recent moves, and the top alternatives to consider.
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.
distributional taught + and - to work on any pair of distributions, closing the algebra it started with.
The R package providing vectorised distribution objects — the substrate that forecasting and anomaly tooling in the same ecosystem builds on. Cadence has picked up sharply, with four releases in the six months to June 2026 against roughly one a year before that. Two kinds of work alternate: adding distribution families (Dirichlet, Horseshoe, Laplace, multivariate t, g-and-k, the extreme-value pair) and deepening what can be computed generically across all of them.
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.
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.
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.
The R package providing vectorised distribution objects — the substrate that forecasting and anomaly tooling in the same ecosystem builds on. Cadence has picked up sharply, with four releases in the six months to June 2026 against roughly one a year before that. Two kinds of work alternate: adding distribution families (Dirichlet, Horseshoe, Laplace, multivariate t, g-and-k, the extreme-value pair) and deepening what can be computed generically across all of them.
The generic-computation thread is the one that matters and it has been building steadily: a Monte Carlo default method for cdf(), has_symmetry() to let algorithms specialise, hdr() moving to exact results for symmetric distributions and 4096 quantiles elsewhere, open-versus-closed support intervals. Version 0.8.0 is where that thread arrives somewhere — arithmetic on arbitrary distributions, with closed forms used when they exist and numerical convolution when they do not. The package is positioning itself as a computational layer rather than a catalogue, which is consistent with how weird and the forecasting packages consume it.
Expect the numerical machinery behind dist_convolved() to be reused for other operators, and more generics like has_symmetry() that let downstream algorithms take exact paths when a distribution supports them.
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 distributional.
Reference-based multiple imputation tables, shipping only what CRAN checks demand.
An MMRM tabulation package that has published nothing since its 2024 CRAN releases.
A single-purpose ggplot2 inset tool, refining the same three arguments.
An R symbolic-maths binding whose changelog is really the C++ core's release notes.
gtfstools stopped guarding its own object model and started accepting everyone else's.
The glue package that makes R carry units and uncertainty through the same calculation.
See all CMAQ alternatives → · See all distributional alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. CMAQ and distributional 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. CMAQ and distributional 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.
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.
Top distributional alternatives in Analytics are ranked by recent ship velocity. Browse the "distributional alternatives" section above for the current picks, or visit /alternatives/distributional-r for the full list with editorial commentary on each.