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Comparison · Analytics
affiner vs distributional
A side-by-side editorial comparison of affiner and distributional — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
affiner vs distributional: 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 distributional?
distributional taught + and - to work on any pair of distributions, closing the algebra it started with.
The R package providing vectorised distribution objects — the substrate that forecasting and anomaly tooling in the same ecosystem builds on. Cadence has picked up sharply, with four releases in the six months to June 2026 against roughly one a year before that. Two kinds of work alternate: adding distribution families (Dirichlet, Horseshoe, Laplace, multivariate t, g-and-k, the extreme-value pair) and deepening what can be computed generically across all of them.
affiner vs distributional: 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.
distributional
0.0ANALYTICS
distributional taught + and - to work on any pair of distributions, closing the algebra it started with.
◆ Current state
The R package providing vectorised distribution objects — the substrate that forecasting and anomaly tooling in the same ecosystem builds on. Cadence has picked up sharply, with four releases in the six months to June 2026 against roughly one a year before that. Two kinds of work alternate: adding distribution families (Dirichlet, Horseshoe, Laplace, multivariate t, g-and-k, the extreme-value pair) and deepening what can be computed generically across all of them.
◆ Where it's heading
The generic-computation thread is the one that matters and it has been building steadily: a Monte Carlo default method for cdf(), has_symmetry() to let algorithms specialise, hdr() moving to exact results for symmetric distributions and 4096 quantiles elsewhere, open-versus-closed support intervals. Version 0.8.0 is where that thread arrives somewhere — arithmetic on arbitrary distributions, with closed forms used when they exist and numerical convolution when they do not. The package is positioning itself as a computational layer rather than a catalogue, which is consistent with how weird and the forecasting packages consume it.
◆ Prediction
Expect the numerical machinery behind dist_convolved() to be reused for other operators, and more generics like has_symmetry() that let downstream algorithms take exact paths when a distribution supports them.
Alternatives to affiner and distributional
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 distributional.
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 distributional alternatives →
Recent activity from affiner and distributional
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agodistributionalConditional S3 registration so the package loads on R before 4.3
- 1mo agodistributionalDistribution arithmetic: FFT convolution behind the + and - operators ⚡
- 2mo agodistributionalVectorised p in quantile() for inflated distributions; open brackets on infinite bounds
- 3mo agoaffinerEllipse, polygon and segment objects, with star and n-gon constructors
- 5mo agodistributionalDirichlet and Horseshoe distributions added
- 6mo agoaffinerIntersection, equivalence and parallelism generics across geometric types
- 7mo agodistributionalhas_symmetry() generic, exact HDRs for symmetric distributions
- 1y agoaffinerIsocube border fill forced transparent
- 1y agoaffinerInitial release: affine grob wrappers and multi-unit angle vectors
- 1y agodistributionalMonte Carlo cdf() default method; g-and-k, g-and-h and extreme-value families
Frequently asked questions
What is the difference between affiner and distributional?
Both compete on the same themes — r-package — within Analytics. affiner and distributional 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 distributional?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. affiner and distributional 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 distributional?
Top distributional alternatives in Analytics are ranked by recent ship velocity. Browse the "distributional alternatives" section above for the current picks, or visit /alternatives/distributional-r for the full list with editorial commentary on each.