paleobuddy
paleobuddy can now simulate trait-dependent diversification, not just birth-death.
A side-by-side editorial comparison of rjdqa and seriation — release velocity, themes, recent moves, and the top alternatives to consider.
rjdqa keeps refining one screen: the seasonal adjustment quality dashboard
rjdqa builds quality assessment dashboards for seasonal adjustment models produced by JDemetra+, aimed at official statisticians reviewing adjusted series. Essentially all development goes into two functions, simple_dashboard() and its denser variant simple_dashboard2(). Version 0.1.6 adds parameters to append observations to the forecast and to control whether the residual trading-days test is printed, defaulting to monthly series only, plus outlier table layout work and user-defined calendar regressor support in sc_dashboard().
seriation stopped shipping algorithms and started shipping a way to pick between them.
seriation finds meaningful orderings for matrices, distance objects and dendrograms, and carries a large registry of methods from classic combinatorial criteria to t-SNE and UMAP embeddings. The 1.5.0 release added a layer above that registry — seriate_best(), seriate_rep() and seriate_improve() — which run randomized methods repeatedly, in parallel, and keep the best result. Recent work is definitional and numeric rather than additive: 1.5.8 corrects the linear seriation criterion to match Hubert and Schultz's original 1976 definition.
rjdqa builds quality assessment dashboards for seasonal adjustment models produced by JDemetra+, aimed at official statisticians reviewing adjusted series. Essentially all development goes into two functions, simple_dashboard() and its denser variant simple_dashboard2(). Version 0.1.6 adds parameters to append observations to the forecast and to control whether the residual trading-days test is printed, defaulting to monthly series only, plus outlier table layout work and user-defined calendar regressor support in sc_dashboard().
The package has converged on a single deliverable and is tuning it against reviewer practice. Each release adds a parameter that lets the analyst include or exclude one element of the dashboard, or adjusts how densely information is packed into the fixed space of the layout. The td_effect default — print the test only for monthly series — is characteristic: the knowledge about when a diagnostic is meaningful is being encoded into the tool rather than left to the reader.
The pattern of adding one toggle per diagnostic per release points at the same thing again, most likely another test given a conditional default, rather than a new dashboard function alongside the two that exist.
seriation finds meaningful orderings for matrices, distance objects and dendrograms, and carries a large registry of methods from classic combinatorial criteria to t-SNE and UMAP embeddings. The 1.5.0 release added a layer above that registry — seriate_best(), seriate_rep() and seriate_improve() — which run randomized methods repeatedly, in parallel, and keep the best result. Recent work is definitional and numeric rather than additive: 1.5.8 corrects the linear seriation criterion to match Hubert and Schultz's original 1976 definition.
The package has shifted from breadth to judgment. Through 1.3.x the additions were new methods; from 1.5.0 the registry started carrying metadata about the methods — whether they are randomized, what criterion they optimize — so the package could choose and evaluate on the user's behalf. The 1.5.6 replacement of FORTRAN with C for BEA and ME points the same way, reducing the legacy surface underneath that machinery.
Further criterion audits are the likeliest next move, since 1.5.8 shows a published definition being reconciled against the implementation and the registry now records what each method optimizes. Expect corrections rather than new seriation algorithms.
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 rjdqa or seriation.
paleobuddy can now simulate trait-dependent diversification, not just birth-death.
geodist stays dependency-free and fast, and warns you when 'cheap' distances stop being honest.
errors keeps making uncertainty print the way each scientific field expects.
CMAQ went global in v5.5, and has been patching that surface ever since.
enpls has not changed its statistics since 2016 — only its website, twice.
grex is a lookup table with a version number — it ships when the annotation moves.
See all rjdqa alternatives → · See all seriation alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. rjdqa and seriation 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. rjdqa and seriation 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 rjdqa alternatives in Analytics are ranked by recent ship velocity. Browse the "rjdqa alternatives" section above for the current picks, or visit /alternatives/rjdqa-r for the full list with editorial commentary on each.
Top seriation alternatives in Analytics are ranked by recent ship velocity. Browse the "seriation alternatives" section above for the current picks, or visit /alternatives/seriation-r for the full list with editorial commentary on each.