Whatagraph
Whatagraph keeps fixing what breaks when one account runs a thousand sources.
A side-by-side editorial comparison of affiner and tulpa — release velocity, themes, recent moves, and the top alternatives to consider.
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.
The 0.0.x train stops at CRAN: tulpa's engine ships to the ecosystem it already anchors.
tulpa is the C++/R Bayesian spatial inference engine sitting under gcol33's family of ecological occupancy packages, tagging 0.0.x releases several times a week. 0.1.0 is its first CRAN release, and the notes state outright that the engine surface is unchanged from 0.0.198 — the work is packaging discipline: local T bindings rebound to n_t/n_times, OpenMP teams capped under R CMD check, the pkgdown deploy narrowed, an aspell dictionary added. The window behind it splits between the S3 generics conversion and numerical-correctness work in the nested-Laplace grid.
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.
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.
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.
tulpa is the C++/R Bayesian spatial inference engine sitting under gcol33's family of ecological occupancy packages, tagging 0.0.x releases several times a week. 0.1.0 is its first CRAN release, and the notes state outright that the engine surface is unchanged from 0.0.198 — the work is packaging discipline: local T bindings rebound to n_t/n_times, OpenMP teams capped under R CMD check, the pkgdown deploy narrowed, an aspell dictionary added. The window behind it splits between the S3 generics conversion and numerical-correctness work in the nested-Laplace grid.
Two moves in nine days point at the same destination: the generics conversion made tulpa extensible by downstream packages, and CRAN admission makes it installable by them. The current cadence — several tags a week, some existing only to record a measurement that produced no code change — does not survive CRAN's submission overhead, so the release rhythm has to slow whether or not the project intends it. The correctness work still clusters on the joint nested-Laplace driver, and 0.1.0 extends the same diagnostics habit with .NL_AXIS_SD_REASONS, a closed vocabulary for an outer axis whose grid does not contain its own posterior mode.
Expect tulpaObs to follow tulpa onto CRAN, since it is the consumer whose registrations the engine has spent this window unblocking, and expect the version line to move in larger, less frequent steps now that each one carries a submission.
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 tulpa.
Whatagraph keeps fixing what breaks when one account runs a thousand sources.
Lightdash keeps handing authoring to outside agents and keeping the governed layer for itself.
A 4.4.0 tag appears, but the feed carries only its release plumbing
distributions3 0.3.0 adds sample-based distributions and likelihood derivatives
Basedash keeps pushing its data out of the workspace — now to people without accounts
RStudio ships through release branches, and the notes are commit messages
See all affiner alternatives → · See all tulpa alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. tulpa is currently shipping more aggressively (velocity 7.5 vs 0.0), with 2 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. tulpa is currently shipping more aggressively (velocity 7.5 vs 0.0), with 2 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
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.
Top tulpa alternatives in Analytics are ranked by recent ship velocity. Browse the "tulpa alternatives" section above for the current picks, or visit /alternatives/tulpa for the full list with editorial commentary on each.