qqman
The Manhattan-plot package for GWAS results, finished and dormant since 2017.
Comparison · Analytics
tern.mmrm vs weird
A side-by-side editorial comparison of tern.mmrm and weird — release velocity, themes, recent moves, and the top alternatives to consider.
tern.mmrm vs weird: at a glance
What is tern.mmrm?
An MMRM tabulation package that has published nothing since its 2024 CRAN releases.
tern.mmrm wraps mixed models for repeated measures into the tern tabulation and plotting layer used by teal clinical modules. The visible history ends with three CRAN releases in mid-to-late 2024; before that the feed carries only automated version bumps from 2022, three of which have just been backfilled into the record. The most recent substantive change adds axis limit arguments to the LS-means plot.
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.mmrm vs weird: editorial side-by-side
tern.mmrm
0.0ANALYTICS
An MMRM tabulation package that has published nothing since its 2024 CRAN releases.
◆ Current state
tern.mmrm wraps mixed models for repeated measures into the tern tabulation and plotting layer used by teal clinical modules. The visible history ends with three CRAN releases in mid-to-late 2024; before that the feed carries only automated version bumps from 2022, three of which have just been backfilled into the record. The most recent substantive change adds axis limit arguments to the LS-means plot.
◆ Where it's heading
Content per release is thin and largely organisational: a maintainer change, replacing scda with random.cdisc.data in vignettes, and adapting to new {mmrm} versions. The package appears to be in maintenance, tracking its upstream dependency rather than developing independently. The 2022 entries now visible are release-automation commits, not releases in any meaningful sense.
◆ Prediction
Nothing here signals new functionality; the realistic next event is another compatibility release when {mmrm} or {rtables} changes underneath it.
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.mmrm 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.mmrm 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.rbmi
Reference-based multiple imputation tables, shipping only what CRAN checks demand.
See all tern.mmrm alternatives → · See all weird alternatives →
Recent activity from tern.mmrm 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.mmrmAxis limits added to the LS-means plot
- 2y agotern.mmrmMaintainer change and scda replaced in vignettes
- 2y agotern.mmrmCRAN 0.3.0 release, mostly workflow housekeeping
- 2y agoweirdWine reviews dataset replaced with a fetch function
- 3y agotern.mmrmAutomated version bump to 0.2.1
- 3y agotern.mmrmPre-release branch merge
- 4y agotern.mmrmCI automation commit, no release content
Frequently asked questions
What is the difference between tern.mmrm and weird?
They serve adjacent needs but don't currently overlap on shipped themes. tern.mmrm 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.mmrm better than weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. tern.mmrm 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.mmrm?
Top tern.mmrm alternatives in Analytics are ranked by recent ship velocity. Browse the "tern.mmrm alternatives" section above for the current picks, or visit /alternatives/tern-mmrm 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.