rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
Comparison · Analytics
rstatix vs weird
A side-by-side editorial comparison of rstatix and weird — release velocity, themes, recent moves, and the top alternatives to consider.
rstatix vs weird: at a glance
What is rstatix?
rstatix hit 1.0 by unrounding every p-value it has ever returned
rstatix is the pipe-friendly test wrapper behind most ggpubr annotation workflows — t-tests, Wilcoxon, ANOVA, post-hoc comparisons, effect sizes, all returning tidy data frames. After three quiet years of CRAN-compat patching, it shipped 1.0.0 and 1.1.0 three weeks apart in mid-2026. Both releases push in the same direction: interval estimates and full-precision output for numbers the package previously rounded or omitted.
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.
rstatix vs weird: editorial side-by-side
rstatix
2.5ANALYTICS
rstatix hit 1.0 by unrounding every p-value it has ever returned
◆ Current state
rstatix is the pipe-friendly test wrapper behind most ggpubr annotation workflows — t-tests, Wilcoxon, ANOVA, post-hoc comparisons, effect sizes, all returning tidy data frames. After three quiet years of CRAN-compat patching, it shipped 1.0.0 and 1.1.0 three weeks apart in mid-2026. Both releases push in the same direction: interval estimates and full-precision output for numbers the package previously rounded or omitted.
◆ Where it's heading
The work is about matching what dedicated effect-size packages give you without taking on their dependencies. Confidence intervals for partial eta squared and for Cohen's d are both computed in base R from noncentral distributions and both check against effectsize; compact letter displays are computed in base R against multcompView. The pattern is deliberate — reproduce the reference implementation, add no imports. Alongside that, the package has started correcting statistical hygiene it got wrong for years, most visibly by no longer rounding p-values before adjusting them.
◆ Prediction
The analytic-interval machinery now exists for eta squared and Cohen's d; the untouched effect sizes in the package — eta squared for nonparametric tests, Cramer's V, rank-biserial correlation — are the obvious next targets for the same base-R noncentral treatment.
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 rstatix 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 rstatix 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 rstatix alternatives → · See all weird alternatives →
Recent activity from rstatix and weird
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 22d agorstatixrstatix 1.1.0 gives Cohen's d a deterministic confidence interval
- 1mo agoweirdOutlier maps, biplot projections, and Gaussian mixtures as distributional objects
- 1mo agorstatixrstatix 1.0.0 stops rounding p-values before adjusting them ⚡
- 3mo agoweirdsurprisals() reaches glm objects; lookout dependency dropped
- 6mo agoweirdPackage refactored onto distributional objects; density_scores becomes surprisals
- 9mo agorstatixrstatix 0.7.3
- 2y agoweirdWine reviews dataset replaced with a fetch function
- 3y agorstatixrstatix 0.7.2
- 3y agorstatixrstatix 0.7.1
- 5y agorstatixrstatix 0.7.0
Frequently asked questions
What is the difference between rstatix and weird?
They serve adjacent needs but don't currently overlap on shipped themes. rstatix is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is rstatix better than weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. rstatix is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to rstatix?
Top rstatix alternatives in Analytics are ranked by recent ship velocity. Browse the "rstatix alternatives" section above for the current picks, or visit /alternatives/rstatix 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.