qqman
The Manhattan-plot package for GWAS results, finished and dormant since 2017.
Comparison · Analytics
tern.rbmi vs weird
A side-by-side editorial comparison of tern.rbmi and weird — release velocity, themes, recent moves, and the top alternatives to consider.
tern.rbmi vs weird: at a glance
What is tern.rbmi?
Reference-based multiple imputation tables, shipping only what CRAN checks demand.
tern.rbmi renders the output of {rbmi} reference-based multiple imputation analyses into tern tables for clinical reporting. Its three most recent releases exist to satisfy CRAN: restricting vignette builds to gcc, adding V8 to Suggests, and a plain resubmission. No user-facing functionality has changed in the visible window, and the three older entries now backfilled are version-bump automation.
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.
tern.rbmi vs weird: editorial side-by-side
tern.rbmi
0.0ANALYTICS
Reference-based multiple imputation tables, shipping only what CRAN checks demand.
◆ Current state
tern.rbmi renders the output of {rbmi} reference-based multiple imputation analyses into tern tables for clinical reporting. Its three most recent releases exist to satisfy CRAN: restricting vignette builds to gcc, adding V8 to Suggests, and a plain resubmission. No user-facing functionality has changed in the visible window, and the three older entries now backfilled are version-bump automation.
◆ Where it's heading
This is a thin adapter package and behaves like one — it moves when CRAN or an upstream dependency forces it to. Between 2022 and 2024 the feed shows only version bumps, and the 2025 releases are packaging concerns rather than analysis changes. The newly surfaced 2022 entries reinforce rather than change that reading.
◆ Prediction
Expect the next release to be triggered by a CRAN check failure or an {rbmi} update rather than by new tabulation features.
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 tern.rbmi 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 tern.rbmi 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.
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.mmrm
An MMRM tabulation package that has published nothing since its 2024 CRAN releases.
See all tern.rbmi alternatives → · See all weird alternatives →
Recent activity from tern.rbmi 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 agotern.rbmiVignette built only under gcc
- 1y agotern.rbmiV8 added to Suggests
- 1y agotern.rbmiCRAN resubmission with dependency and workflow updates
- 2y agoweirdWine reviews dataset replaced with a fetch function
- 3y agotern.rbmiAutomated release commit for 0.1.1
- 4y agotern.rbmiDevelopment version bump to 0.1.0.9004
- 4y agotern.rbmiCI automation commit, no release content
Frequently asked questions
What is the difference between tern.rbmi and weird?
They serve adjacent needs but don't currently overlap on shipped themes. tern.rbmi 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 tern.rbmi better than weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. tern.rbmi 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 tern.rbmi?
Top tern.rbmi alternatives in Analytics are ranked by recent ship velocity. Browse the "tern.rbmi alternatives" section above for the current picks, or visit /alternatives/tern-rbmi 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.