qqman
The Manhattan-plot package for GWAS results, finished and dormant since 2017.
Comparison · Analytics
ggdemetra vs weird
A side-by-side editorial comparison of ggdemetra and weird — release velocity, themes, recent moves, and the top alternatives to consider.
ggdemetra vs weird: at a glance
What is ggdemetra?
A ggplot2 layer for seasonal adjustment output, filling in one plot type at a time.
ggdemetra is a thin, focused bridge: it puts RJDemetra's seasonal adjustment results — TRAMO-SEATS and X-13 models — into ggplot2 geoms and autoplot methods. Development runs in short bursts separated by long quiet stretches, and the most recent work has been correcting SI ratio handling rather than adding surface. The API is small enough that a single function rename counts as the notable change in a release.
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.
ggdemetra vs weird: editorial side-by-side
ggdemetra
0.0ANALYTICS
A ggplot2 layer for seasonal adjustment output, filling in one plot type at a time.
◆ Current state
ggdemetra is a thin, focused bridge: it puts RJDemetra's seasonal adjustment results — TRAMO-SEATS and X-13 models — into ggplot2 geoms and autoplot methods. Development runs in short bursts separated by long quiet stretches, and the most recent work has been correcting SI ratio handling rather than adding surface. The API is small enough that a single function rename counts as the notable change in a release.
◆ Where it's heading
The package has been steadily completing its coverage of the seasonal adjustment output surface: component extractors and autoplot methods in 0.2.3, SI ratio plotting in 0.2.5, then two releases of corrections to make SI ratios behave under TRAMO-SEATS jSA models and when no seasonal component is exported. Alongside that, the naming is being tidied — y_forecast() became raw(), and init_ggplot() shortened the setup boilerplate. This reads as a package approaching the edge of its intended scope and spending its effort on correctness.
◆ Prediction
Two consecutive releases fixing SI ratios under TRAMO-SEATS suggest that code path is the least settled part of the package, so further corrections there are the most likely next move. The entries give no indication of new model families or plot types being planned.
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 ggdemetra 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 ggdemetra or weird.
constants
The R package for CODATA constants rebuilt its symbol table on NIST's naming so future updates stop being hand work.
austraits
The R client for AusTraits spends its releases chasing the dataset it reads.
fossilsim
A fossil-record simulator that quietly grew a trait-evolution engine.
tern.rbmi
Reference-based multiple imputation tables, shipping only what CRAN checks demand.
tern.mmrm
An MMRM tabulation package that has published nothing since its 2024 CRAN releases.
See all ggdemetra alternatives → · See all weird alternatives →
Recent activity from ggdemetra 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
- 2y agoweirdWine reviews dataset replaced with a fetch function
- 2y agoggdemetrasiratio() fix when TRAMO-SEATS exports no seasonal component
- 2y agoggdemetraraw() replaces y_forecast(); new init_ggplot() helper
- 2y agoggdemetraRe-tagged moments later as 0.2.7
- 3y agoggdemetraSI ratio plotting functions land
- 5y agoggdemetrats2df() conversion helper and geom_outlier date fix
- 6y agoggdemetrageom_arima no longer recomputes the model
Frequently asked questions
What is the difference between ggdemetra and weird?
They serve adjacent needs but don't currently overlap on shipped themes. ggdemetra 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 ggdemetra better than weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggdemetra 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 ggdemetra?
Top ggdemetra alternatives in Analytics are ranked by recent ship velocity. Browse the "ggdemetra alternatives" section above for the current picks, or visit /alternatives/ggdemetra 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.