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Comparison · Analytics
TAF vs weird
A side-by-side editorial comparison of TAF and weird — release velocity, themes, recent moves, and the top alternatives to consider.
TAF vs weird: at a glance
What is TAF?
TAF keeps turning ICES stock assessments into reproducible, dependency-pinned projects.
TAF is the R tooling behind the ICES Transparent Assessment Framework, which standardizes how fish stock assessments are laid out, sourced and rerun. The 4.3.0 release is the largest in years, adding roughly ten functions covering dependency installation and analysis, software version checks, directory inspection and README drafting. The package has carried zero non-base dependencies since 4.0.0, and the new work is careful not to break that.
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.
TAF vs weird: editorial side-by-side
TAF
0.0ANALYTICS
TAF keeps turning ICES stock assessments into reproducible, dependency-pinned projects.
◆ Current state
TAF is the R tooling behind the ICES Transparent Assessment Framework, which standardizes how fish stock assessments are laid out, sourced and rerun. The 4.3.0 release is the largest in years, adding roughly ten functions covering dependency installation and analysis, software version checks, directory inspection and README drafting. The package has carried zero non-base dependencies since 4.0.0, and the new work is careful not to break that.
◆ Where it's heading
The arc runs from analysis runner to project toolkit. Early 3.x releases built out the bootstrap and metadata machinery; 4.0.0 renamed the package and stripped every external dependency; 4.2.0 cleaned up vocabulary that confused users. 4.3.0 turns outward to the people running assessments — install.deps(), pdeps() and check.software() address reproducing someone else's environment, while draft.readme(), taf.example() and dir.tree() address understanding an unfamiliar project.
◆ Prediction
Expect the follow-up work to harden the new dependency functions rather than add more surface, since 4.3.1 arrived immediately to fix wide2long() compatibility with older R and that batch of ten functions has had little field exposure.
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 TAF 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 TAF or weird.
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.
Recent activity from TAF 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
- 10mo agoTAFTAF 4.3.1 reworks wide2long() for older R
- 10mo agoTAFTAF 4.3.0 adds dependency and project-scaffolding tooling
- 2y agoweirdWine reviews dataset replaced with a fetch function
- 3y agoTAFTAF 4.2.0 renames 'bootstrap' to 'boot', keeps back-compat
- 3y agoTAFTAF 4.1.0 adds taf2html() and dot.case function aliases
- 5y agoTAFTAF 4.0.0 refocuses on ICES and drops every external dependency ⚡
- 5y agoTAFTAF 3.6.0 adds metadata tooling and drops the bibtex dependency
Frequently asked questions
What is the difference between TAF and weird?
They serve adjacent needs but don't currently overlap on shipped themes. TAF 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 TAF better than weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. TAF 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 TAF?
Top TAF alternatives in Analytics are ranked by recent ship velocity. Browse the "TAF alternatives" section above for the current picks, or visit /alternatives/taf-r 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.