constants
The R package for CODATA constants rebuilt its symbol table on NIST's naming so future updates stop being hand work.
Comparison · Analytics
qqman vs weird
A side-by-side editorial comparison of qqman and weird — release velocity, themes, recent moves, and the top alternatives to consider.
qqman vs weird: at a glance
What is qqman?
The Manhattan-plot package for GWAS results, finished and dormant since 2017.
qqman does two things: manhattan() and qq() plots for genome-wide association study results. Its six visible releases run from 2014 to a single 2017 packaging fix, and the last release with any user-facing change shipped in 2015. The archive is non-monotonic — a 0.0.0 tag published after 0.1.1 archives the pre-package standalone script — so version order and publication order disagree.
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.
qqman vs weird: editorial side-by-side
qqman
0.0ANALYTICS
The Manhattan-plot package for GWAS results, finished and dormant since 2017.
◆ Current state
qqman does two things: manhattan() and qq() plots for genome-wide association study results. Its six visible releases run from 2014 to a single 2017 packaging fix, and the last release with any user-facing change shipped in 2015. The archive is non-monotonic — a 0.0.0 tag published after 0.1.1 archives the pre-package standalone script — so version order and publication order disagree.
◆ Where it's heading
The real development window was 2014 to 2015. The 0.1.2 release did the substantive work, replacing the assumption that SNPs are evenly distributed across chromosomes and handing users control of axis limits, labels and log transformation; 0.1.3 then added annotation by p-value threshold and top-SNP-per-chromosome. After that the package stops. Notably, the archival 0.0.0 entry records that the original script had confidence intervals on QQ plots and richer highlighting than the released package ever regained.
◆ Prediction
With one packaging fix in the last decade, these entries support no prediction of further releases. The package reads as complete for its narrow purpose rather than abandoned mid-arc.
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 qqman 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 qqman or weird.
austraits
The R client for AusTraits spends its releases chasing the dataset it reads.
ggdemetra
A ggplot2 layer for seasonal adjustment output, filling in one plot type at a time.
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.
Recent activity from qqman 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
- 9y agoqqmanREADME image path fix for pandoc
- 11y agoqqmanAnnotate SNPs by p-value threshold or per-chromosome top hit
- 11y agoqqmanChromosome ticks stop assuming even SNP spacing; axis control opens up
- 12y agoqqmanArchival tag for the pre-package standalone script
- 12y agoqqmanVignette touch-up
- 12y agoqqmanZenodo archival tag, no code change
Frequently asked questions
What is the difference between qqman and weird?
They serve adjacent needs but don't currently overlap on shipped themes. qqman 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 qqman better than weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. qqman 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 qqman?
Top qqman alternatives in Analytics are ranked by recent ship velocity. Browse the "qqman alternatives" section above for the current picks, or visit /alternatives/qqman-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.