rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
Comparison · Analytics
dggridR vs weird
A side-by-side editorial comparison of dggridR and weird — release velocity, themes, recent moves, and the top alternatives to consider.
dggridR vs weird: at a glance
What is dggridR?
A discrete global grid generator grew cell traversal and became a usable spatial index.
dggridR builds discrete global grids — icosahedral tessellations of the Earth into equal-area hexagonal or triangular cells — by wrapping the DGGRID C++ engine. The 4.1.0 release adds dgneighbors, dgchildren and dgparent for moving between adjacent cells and across resolutions, plus dgpoints_to_cells and dgbin_points for mapping and aggregating point data into cells. New aperture 7 and mixed-aperture ISEA43H grid types arrive alongside 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.
dggridR vs weird: editorial side-by-side
dggridR
0.0ANALYTICS
A discrete global grid generator grew cell traversal and became a usable spatial index.
◆ Current state
dggridR builds discrete global grids — icosahedral tessellations of the Earth into equal-area hexagonal or triangular cells — by wrapping the DGGRID C++ engine. The 4.1.0 release adds dgneighbors, dgchildren and dgparent for moving between adjacent cells and across resolutions, plus dgpoints_to_cells and dgbin_points for mapping and aggregating point data into cells. New aperture 7 and mixed-aperture ISEA43H grid types arrive alongside them.
◆ Where it's heading
The package changed hands in effect as well as in code: the 4.0.0 engine update to DGGRID v9.0b and the first real test suite were contributed by Sebastian Krantz, who also maintains the upstream engine fork, and 4.1.0's feature burst followed two weeks later. The direction of that burst is unmistakable — away from generating grids for plotting and toward using them as an indexing structure that point data gets binned into and navigated through.
◆ Prediction
Expect the cell hierarchy functions to extend to non-hexagonal apertures and multi-level traversal, closing the remaining gaps against established global indexing systems.
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 dggridR 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 dggridR 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.
See all dggridR alternatives → · See all weird alternatives →
Recent activity from dggridR and weird
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoweirdOutlier maps, biplot projections, and Gaussian mixtures as distributional objects
- 3mo agodggridRCell neighbors, parents and children make the grid navigable ⚡
- 3mo agoweirdsurprisals() reaches glm objects; lookout dependency dropped
- 3mo agodggridRBundled DGGRID engine updated to v9.0b with a test suite
- 3mo agodggridRMaster merged into development ahead of the 4.0 work
- 6mo agoweirdPackage refactored onto distributional objects; density_scores becomes surprisals
- 2y agoweirdWine reviews dataset replaced with a fetch function
Frequently asked questions
What is the difference between dggridR and weird?
They serve adjacent needs but don't currently overlap on shipped themes. dggridR 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 dggridR better than weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. dggridR 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 dggridR?
Top dggridR alternatives in Analytics are ranked by recent ship velocity. Browse the "dggridR alternatives" section above for the current picks, or visit /alternatives/dggridr 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.