tern.rbmi
Reference-based multiple imputation tables, shipping only what CRAN checks demand.
Comparison · Analytics
geodist vs weird
A side-by-side editorial comparison of geodist and weird — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
geodist vs weird: at a glance
What is geodist?
geodist stays dependency-free and fast, and warns you when 'cheap' distances stop being honest.
geodist computes geodesic distances between coordinate pairs in C with no dependencies, offering several measures that trade accuracy for speed — including a 'cheap' approximation used by default. The API is small and largely finished; 0.1.0 added geodist_min() for nearest-match lookups and 0.1.1 is a compiler warning fix.
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.
geodist vs weird: editorial side-by-side
geodist
0.0ANALYTICS
geodist stays dependency-free and fast, and warns you when 'cheap' distances stop being honest.
◆ Current state
geodist computes geodesic distances between coordinate pairs in C with no dependencies, offering several measures that trade accuracy for speed — including a 'cheap' approximation used by default. The API is small and largely finished; 0.1.0 added geodist_min() for nearest-match lookups and 0.1.1 is a compiler warning fix.
◆ Where it's heading
Development has been about making the speed-accuracy trade visible rather than hiding it. The 0.0.6 release added messages telling users to pick a different measure once the default cheap approximation is applied beyond 100km, where its error stops being negligible. Around that, the work is input handling — tibble support, better lon/lat column matching, vector inputs — and hardening the C code. It is a package that treats being small and correct as the feature.
◆ Prediction
Expect continued low-frequency maintenance: compiler warnings and geodesic source updates account for three of the last six releases, and the function surface has grown by only two entries in five years.
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 geodist 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 geodist or weird.
tern.mmrm
An MMRM tabulation package that has published nothing since its 2024 CRAN releases.
ggmagnify
A single-purpose ggplot2 inset tool, refining the same three arguments.
symengine
An R symbolic-maths binding whose changelog is really the C++ core's release notes.
gtfstools
gtfstools stopped guarding its own object model and started accepting everyone else's.
quantities
The glue package that makes R carry units and uncertainty through the same calculation.
See all geodist alternatives → · See all weird alternatives →
Recent activity from geodist 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
- 6mo agoweirdPackage refactored onto distributional objects; density_scores becomes surprisals
- 1y agogeodistgeodist 0.1.1 clears a clang warning in geodesic.c
- 2y agogeodistgeodist 0.1.0 adds geodist_min() for nearest matches
- 2y agoweirdWine reviews dataset replaced with a fetch function
- 3y agogeodistgeodist 0.0.8 updates geodesic source, fixes clang warnings
- 5y agogeodistgeodist 0.0.7 improves lon/lat column matching and tibbles
- 5y agogeodistgeodist 0.0.6 warns when cheap distances exceed 100km
- 6y agogeodistgeodist 0.0.4 adds geodist_vec() for vector inputs
Frequently asked questions
What is the difference between geodist and weird?
Both compete on the same themes — r-package — within Analytics. geodist 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 geodist better than weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. geodist 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 geodist?
Top geodist alternatives in Analytics are ranked by recent ship velocity. Browse the "geodist alternatives" section above for the current picks, or visit /alternatives/geodist-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.