rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
Comparison · Analytics
climaemet vs weird
A side-by-side editorial comparison of climaemet and weird — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
climaemet vs weird: at a glance
What is climaemet?
climaemet added weather alerts and wildfire risk, then spent two years managing rate limits.
climaemet wraps Spain's AEMET meteorological API — station data, historical climate series, forecasts, and the plotting helpers that go with them. Its capability surface widened decisively in 1.4.0 with meteorological alerts and wildfire risk rasters. Everything since has been about surviving the API rather than extending it: multiple API keys, quota-aware key selection, and httr2 throttling pinned to AEMET's stated 40-connections-per-minute policy.
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.
climaemet vs weird: editorial side-by-side
climaemet
0.0ANALYTICS
climaemet added weather alerts and wildfire risk, then spent two years managing rate limits.
◆ Current state
climaemet wraps Spain's AEMET meteorological API — station data, historical climate series, forecasts, and the plotting helpers that go with them. Its capability surface widened decisively in 1.4.0 with meteorological alerts and wildfire risk rasters. Everything since has been about surviving the API rather than extending it: multiple API keys, quota-aware key selection, and httr2 throttling pinned to AEMET's stated 40-connections-per-minute policy.
◆ Where it's heading
Two forces shape this package, and neither is feature demand. The first is AEMET's own churn — new response codes, a fires endpoint that switched to six risk levels returned as named factors, municipality datasets refreshed annually. The second is the maintainer's cross-package modernization, visible here as the API key store moving to tools::R_user_dir() with automatic migration, a configurable timeout, cli messaging, and an R 4.1 floor. The 1.6.0 refactor is stated as AI-assisted, matching the maintainer's other packages.
◆ Prediction
Expect the next release to track another AEMET endpoint change rather than add a data domain; the throttling and multi-key machinery suggests quota pressure is the constraint the maintainer keeps returning to.
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 climaemet 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 climaemet 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 climaemet alternatives → · See all weird alternatives →
Recent activity from climaemet and weird
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoweirdOutlier maps, biplot projections, and Gaussian mixtures as distributional objects
- 2mo agoclimaemetAPI keys move to R_user_dir; fire risk returns named factors
- 3mo agoweirdsurprisals() reaches glm objects; lookout dependency dropped
- 4mo agoclimaemetVignettes migrated to Quarto
- 6mo agoweirdPackage refactored onto distributional objects; density_scores becomes surprisals
- 7mo agoclimaemetRequest throttling pinned to AEMET's 40-per-minute policy
- 1y agoclimaemetggwindrose rebuilt on coord_radial
- 1y agoclimaemetHighest-quota API key now chosen per call
- 1y agoclimaemetWeather alerts and wildfire risk rasters added ⚡
- 2y agoweirdWine reviews dataset replaced with a fetch function
Frequently asked questions
What is the difference between climaemet and weird?
Both compete on the same themes — r-package — within Analytics. climaemet 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 climaemet better than weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. climaemet 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 climaemet?
Top climaemet alternatives in Analytics are ranked by recent ship velocity. Browse the "climaemet alternatives" section above for the current picks, or visit /alternatives/climaemet 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.