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Comparison · Analytics
affiner vs weird
A side-by-side editorial comparison of affiner and weird — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
affiner vs weird: at a glance
What is affiner?
affiner is quietly turning a grid transformation helper into a small computational geometry library.
An R package that began as a wrapper around grid's affine transformation primitives, with an angle vector class supporting degrees, radians, half-turns, turns and gradians so users need not convert by hand. Four releases in roughly eighteen months. The recent two have expanded well past that starting point into geometric objects and the predicates that operate on them.
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.
affiner vs weird: editorial side-by-side
affiner
0.0ANALYTICS
affiner is quietly turning a grid transformation helper into a small computational geometry library.
◆ Current state
An R package that began as a wrapper around grid's affine transformation primitives, with an angle vector class supporting degrees, radians, half-turns, turns and gradians so users need not convert by hand. Four releases in roughly eighteen months. The recent two have expanded well past that starting point into geometric objects and the predicates that operate on them.
◆ Where it's heading
The direction is clear from the order things arrived. Version 0.2.1 added the predicate layer first — has_intersection(), intersection(), is_equivalent() and is_parallel() as S3 generics working across angle vectors, points, lines and planes. Version 0.3.1 then supplied the objects those generics need, with Ellipse2D, Polygon2D and Segment2D R6 classes plus constructors for rectangles, regular n-gons and isotoxal star polygons, and dot products at one, two and three dimensions. Building the operations before the shapes is unusual ordering but it means each new object type arrives already composable with everything else.
◆ Prediction
Expect more 2D and 3D object types filling out the same generic interface, and the geometry side to keep outgrowing the grid-transformation wrapper the package was named for.
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 affiner 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 affiner or weird.
epiworldR
epiworldR is a thin R shell whose releases track the C++ simulator underneath it
watina
A groundwater database client that has started doing the domain analysis too
inbospatial
A thin R wrapper over Flemish geospatial services, adding one standard at a time
Fluent Bit
Fluent Bit keeps two lines alive while the 5.x branch quietly opens 5.1.
OpenCTI
OpenCTI is rebuilding its connector layer into a marketplace and wiring the platform to XTM Hub
See all affiner alternatives → · See all weird alternatives →
Recent activity from affiner and weird
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoweirdOutlier maps, biplot projections, and Gaussian mixtures as distributional objects
- 3mo agoaffinerEllipse, polygon and segment objects, with star and n-gon constructors
- 3mo agoweirdsurprisals() reaches glm objects; lookout dependency dropped
- 6mo agoaffinerIntersection, equivalence and parallelism generics across geometric types
- 6mo agoweirdPackage refactored onto distributional objects; density_scores becomes surprisals
- 1y agoaffinerIsocube border fill forced transparent
- 1y agoaffinerInitial release: affine grob wrappers and multi-unit angle vectors
- 2y agoweirdWine reviews dataset replaced with a fetch function
Frequently asked questions
What is the difference between affiner and weird?
Both compete on the same themes — r-package — within Analytics. affiner 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 affiner better than weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. affiner 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 affiner?
Top affiner alternatives in Analytics are ranked by recent ship velocity. Browse the "affiner alternatives" section above for the current picks, or visit /alternatives/affiner-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.