rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
Comparison · Analytics
broadcast vs weird
A side-by-side editorial comparison of broadcast and weird — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
broadcast vs weird: at a glance
What is broadcast?
broadcast is filling in NumPy-style array broadcasting for R, operator by operator.
broadcast brings dimension-broadcasting semantics to R arrays and lists — elementwise operations between arrays of mismatched shape, plus casting methods between hierarchical lists and dimensional structures. It reached CRAN in September 2025 and has released roughly monthly since, accumulating operators (nor, nand, longest common substring), casting methods (cast_shallow2atomic, cast_hier2dim, hiernames2dimnames), and helpers (vector2array, undim, mbroadcasters).
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.
broadcast vs weird: editorial side-by-side
broadcast
0.0ANALYTICS
broadcast is filling in NumPy-style array broadcasting for R, operator by operator.
◆ Current state
broadcast brings dimension-broadcasting semantics to R arrays and lists — elementwise operations between arrays of mismatched shape, plus casting methods between hierarchical lists and dimensional structures. It reached CRAN in September 2025 and has released roughly monthly since, accumulating operators (nor, nand, longest common substring), casting methods (cast_shallow2atomic, cast_hier2dim, hiernames2dimnames), and helpers (vector2array, undim, mbroadcasters).
◆ Where it's heading
The package is in its post-launch consolidation year, and the release notes read accordingly: roughly half of each entry is a consistency correction rather than an addition. Zero-length results now carry the right type, comparison operators accept integer and logical inputs, the comment attribute survives operations, and the nand operator was found to be wrongly defined against C++ short-circuit evaluation. That ratio is what a young package looks like while its edge cases are being found.
◆ Prediction
Expect more operators and casting methods on the same cadence, with continued type-consistency corrections as users exercise unusual input combinations. Nothing in the entries points at an architectural change.
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 broadcast 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 broadcast 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 broadcast alternatives → · See all weird alternatives →
Recent activity from broadcast and weird
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoweirdOutlier maps, biplot projections, and Gaussian mixtures as distributional objects
- 2mo agobroadcastnor and longest-common-substring operators added; nand corrected
- 3mo agoweirdsurprisals() reaches glm objects; lookout dependency dropped
- 5mo agobroadcastcheckNULL, checkNA and ecumprob added
- 6mo agoweirdPackage refactored onto distributional objects; density_scores becomes surprisals
- 8mo agobroadcastZero-length results and attribute preservation made consistent
- 9mo agobroadcastacast dimnames bug fixed; casting and helper surface widens
- 10mo agobroadcastrecurse_classed replaced by recurse_all in casting methods
- 11mo agobroadcastTitle case fixed for CRAN submission
- 2y agoweirdWine reviews dataset replaced with a fetch function
Frequently asked questions
What is the difference between broadcast and weird?
Both compete on the same themes — r-package — within Analytics. broadcast 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 broadcast better than weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. broadcast 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 broadcast?
Top broadcast alternatives in Analytics are ranked by recent ship velocity. Browse the "broadcast alternatives" section above for the current picks, or visit /alternatives/broadcast-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.