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ardlverse vs fairmodels

A side-by-side editorial comparison of ardlverse and fairmodels — release velocity, themes, recent moves, and the top alternatives to consider.

ardlverse vs fairmodels: at a glance

Featureardlversefairmodels
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themeseconometrics, panel-data, ardl, r-packagefairness auditing, bias detection, dalex, r package
Last editorial update46m ago4h ago
WebsiteVisit →Visit →

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.

Read the full ardlverse trajectory →

What is fairmodels?

fairmodels sits dormant for three years, resurfacing only to satisfy a CRAN check.

fairmodels audits classification models for bias, built around fairness_check() and parity-loss metrics on top of DALEX explainers. The last substantive work dates from 2021; the 2025 release is a single-line change swapping ifelse for if/else in fairness_heatmap. Version 0.2.2 set the package's core design when it superseded metric differences with ratios.

Read the full fairmodels trajectory →

ardlverse vs fairmodels: editorial side-by-side

A
ardlverse
ANALYTICS
0.0

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.

F
fairmodels
ANALYTICS
0.0

fairmodels sits dormant for three years, resurfacing only to satisfy a CRAN check.

◆ Current state

fairmodels audits classification models for bias, built around fairness_check() and parity-loss metrics on top of DALEX explainers. The last substantive work dates from 2021; the 2025 release is a single-line change swapping ifelse for if/else in fairness_heatmap. Version 0.2.2 set the package's core design when it superseded metric differences with ratios.

◆ Where it's heading

The release history describes a package that reached its intended shape early and has been custodial since — the gap from August 2022 to October 2025 carries no functional change at all. What movement exists is CRAN-driven: documentation compliance, example runtimes, coding-style notes. The fairness metrics themselves have not changed since the parity_loss corrections of 2020.

◆ Prediction

On this cadence the next release is most likely another CRAN-prompted one-liner rather than new fairness metrics; nothing in these entries points to active development.

Alternatives to ardlverse and fairmodels

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 fairmodels.

See all ardlverse alternatives → · See all fairmodels alternatives →

Recent activity from ardlverse and fairmodels

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 1mo agoardlverseSeven panel_ardl fixes after an audit against Stata's xtpmg
  2. 5mo agoardlverseZenodo metadata updated with ORCID
  3. 5mo agoardlverseardlverse v1.1.3
  4. 9mo agofairmodelsOne-line fix in fairness_heatmap
  5. 3y agofairmodelsCRAN compliance fixes and citation update
  6. 4y agofairmodelsCRAN v1.2.0
  7. 5y agofairmodelsCRAN v1.1.0
  8. 5y agofairmodelsDocumentation fixes and trimmed example runtimes
  9. 5y agofairmodelsCorrects parity_loss in the cutoff functions

Frequently asked questions

What is the difference between ardlverse and fairmodels?

They serve adjacent needs but don't currently overlap on shipped themes. ardlverse and fairmodels 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 fairmodels?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ardlverse and fairmodels 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 fairmodels?

Top fairmodels alternatives in Analytics are ranked by recent ship velocity. Browse the "fairmodels alternatives" section above for the current picks, or visit /alternatives/fairmodels for the full list with editorial commentary on each.