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Comparison · Analytics
ardlverse vs seriation
A side-by-side editorial comparison of ardlverse and seriation — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
ardlverse vs seriation: at a glance
What is ardlverse?
An outside audit against Stata found seven errors in ardlverse's panel estimator, including regressions with no intercept.
A small R package for autoregressive distributed lag models, with only three releases on record. The first two were administrative — a CRAN version note and a Zenodo metadata update. The third, 2.0.0, is a correction release built entirely from an external audit of panel_ardl() against Stata's xtpmg, and it is the only entry here with substantive content.
What is seriation?
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.
ardlverse vs seriation: editorial side-by-side
ardlverse
0.0ANALYTICS
An outside audit against Stata found seven errors in ardlverse's panel estimator, including regressions with no intercept.
◆ Current state
A small R package for autoregressive distributed lag models, with only three releases on record. The first two were administrative — a CRAN version note and a Zenodo metadata update. The third, 2.0.0, is a correction release built entirely from an external audit of panel_ardl() against Stata's xtpmg, and it is the only entry here with substantive content.
◆ Where it's heading
The package's direction is now set by verification against an established reference implementation rather than by feature work. The seven fixes bring panel_ardl() into strict alignment with the original Pesaran, Shin and Smith framework, and the most serious of them is structural: internal regressions used lm.fit(), which unlike lm() does not append an intercept, so every short-run regression across the PMG, MG and DFE estimators was forced through the origin. Design matrices now carry a column of ones and DFE reconstructs the grand-mean intercept to match standard fixed-effects output.
◆ Prediction
Expect the next releases to extend the same audit approach to the remaining estimators, since a package that has been validated against xtpmg on one function invites the same question about the rest.
seriation
0.0ANALYTICS
seriation stopped shipping algorithms and started shipping a way to pick between them.
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
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.
Alternatives to ardlverse and seriation
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 ardlverse or seriation.
geodist
geodist stays dependency-free and fast, and warns you when 'cheap' distances stop being honest.
errors
errors keeps making uncertainty print the way each scientific field expects.
CMAQ
CMAQ went global in v5.5, and has been patching that surface ever since.
enpls
enpls has not changed its statistics since 2016 — only its website, twice.
grex
grex is a lookup table with a version number — it ships when the annotation moves.
See all ardlverse alternatives → · See all seriation alternatives →
Recent activity from ardlverse and seriation
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoardlverseSeven panel_ardl fixes after an audit against Stata's xtpmg
- 5mo agoardlverseZenodo metadata updated with ORCID
- 5mo agoardlverseardlverse v1.1.3
- 0y agoseriationseriation 1.5.8 realigns linear criterion with Hubert and Schultz
- 1y agoseriationseriation 1.5.7 adds BK_unconstrained, handles tiny inputs
- 1y agoseriationseriation 1.5.6 replaces FORTRAN BEA with C, modernizes allocation
- 2y agoseriationseriation 1.5.5 digest: AOE method, rep parameter, MDS_angle fix
- 3y agoseriationseriation 1.5.1 refines pimage, permute and hmap
- 3y agoseriationseriation 1.5.0 adds seriate_best and parallel repeated search ⚡
Frequently asked questions
What is the difference between ardlverse and seriation?
Both compete on the same themes — r-package — within Analytics. ardlverse 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.
Is ardlverse better than seriation?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ardlverse 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.
What are the best alternatives to ardlverse?
Top ardlverse alternatives in Analytics are ranked by recent ship velocity. Browse the "ardlverse alternatives" section above for the current picks, or visit /alternatives/ardlverse-r for the full list with editorial commentary on each.
What are the best alternatives to seriation?
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.