rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
Comparison · Analytics
trias vs weird
A side-by-side editorial comparison of trias and weird — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:data-visualization
trias vs weird: at a glance
What is trias?
Belgium's invasive-species indicator toolkit is in steady refinement, one plotting edge case at a time.
trias computes and visualizes indicators for the Belgian Tracking Invasive Alien Species project — emergence detection via GAMs, introduction pathway breakdowns following CBD categories, and native range trends. The recent releases are narrow: GAM plots can now be produced without textual annotation when the model cannot be fitted, and apply_decision_rules() no longer supplies a default for a required argument.
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.
trias vs weird: editorial side-by-side
trias
0.0ANALYTICS
Belgium's invasive-species indicator toolkit is in steady refinement, one plotting edge case at a time.
◆ Current state
trias computes and visualizes indicators for the Belgian Tracking Invasive Alien Species project — emergence detection via GAMs, introduction pathway breakdowns following CBD categories, and native range trends. The recent releases are narrow: GAM plots can now be produced without textual annotation when the model cannot be fitted, and apply_decision_rules() no longer supplies a default for a required argument.
◆ Where it's heading
Development runs in small, fast patches concentrated on making the indicator functions survive imperfect real-world input — pathways absent from the data, GAMs that will not converge, checklist files with unexpected columns. A second thread trims the package's own surface in favor of the data it ships, deprecating pathways_cbd() in favor of using the pathwayscbd data frame directly, while get_nubkeys() extends reach into GBIF Backbone taxon key resolution.
◆ Prediction
Expect continued patch-level hardening of the visualization functions and further reliance on GBIF services for taxon resolution, with no sign of a structural change to the indicator set.
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 trias 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 trias or weird.
audubon
audubon's release feed is almost entirely Renovate bumping the JavaScript toolchain behind its Japanese text splitter.
affiner
affiner is quietly turning a grid transformation helper into a small computational geometry library.
ageproR
ageproR spent two years chasing a moving file format, then added the recruitment models that justify the effort.
r-ledger
ledger adds a Rust toolchain fallback, so beancount imports work whether or not the Python tooling is installed.
gridpattern
gridpattern keeps widening its catalogue, and the newest patterns finally use the device's own line rendering.
Recent activity from trias 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
- 3mo agotriasGAM plots survive models that cannot be fitted
- 5mo agotriasColumn validation added to the download list update
- 6mo agotriasY-axis tick values corrected in pathway plots
- 6mo agoweirdPackage refactored onto distributional objects; density_scores becomes surprisals
- 6mo agotriasZenodo integration patch removes the DOI badge
- 6mo agotriasget_nubkeys() resolves GBIF Backbone taxon keys
- 6mo agotriaspathways_cbd() deprecated in favor of its data frame
- 2y agoweirdWine reviews dataset replaced with a fetch function
Frequently asked questions
What is the difference between trias and weird?
Both compete on the same themes — data-visualization — within Analytics. trias 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 trias better than weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. trias 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 trias?
Top trias alternatives in Analytics are ranked by recent ship velocity. Browse the "trias alternatives" section above for the current picks, or visit /alternatives/trias-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.