rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
Comparison · Analytics
n1qn1c vs weird
A side-by-side editorial comparison of n1qn1c and weird — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
n1qn1c vs weird: at a glance
What is n1qn1c?
A Fortran-descended optimizer got thread-safe, then found two flags that never worked.
n1qn1c is a quasi-Newton optimization routine translated from Fortran to C, used as a solver backend by the nlmixr2 modeling stack rather than called directly by most users. Its two 2026 releases are a concentrated safety pass: global state converted to thread_local, static removed from local variables in the translated code, integer overflow guards added, and memory leaks closed in the R callback wrappers — plus the gcc-asan and valgrind fixes CRAN asked for.
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.
n1qn1c vs weird: editorial side-by-side
n1qn1c
0.0ANALYTICS
A Fortran-descended optimizer got thread-safe, then found two flags that never worked.
◆ Current state
n1qn1c is a quasi-Newton optimization routine translated from Fortran to C, used as a solver backend by the nlmixr2 modeling stack rather than called directly by most users. Its two 2026 releases are a concentrated safety pass: global state converted to thread_local, static removed from local variables in the translated code, integer overflow guards added, and memory leaks closed in the R callback wrappers — plus the gcc-asan and valgrind fixes CRAN asked for.
◆ Where it's heading
The package is being hardened for use inside a parallel modeling framework rather than extended. The audit that produced the thread-safety work also surfaced two plain bugs in features users would have assumed worked: restart = TRUE left the mode at 2 instead of 3 because of a typo, and assign = TRUE referenced the wrong field name so the compressed Hessian was never written to the supplied environment. Earlier work points the same direction — the 6.0.1-12 function-pointer interface exists so nlmixr2est does not need resubmission when this package changes.
◆ Prediction
Expect further memory-safety and sanitizer work rather than algorithmic change; a Fortran-translated numerical core under CRAN's checking regime generates that kind of release indefinitely.
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 n1qn1c 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 n1qn1c 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 n1qn1c alternatives → · See all weird alternatives →
Recent activity from n1qn1c 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
- 4mo agon1qn1crestart and assign flags fixed; sanitizer issues cleared
- 4mo agon1qn1cGlobal state converted to thread_local
- 6mo agoweirdPackage refactored onto distributional objects; density_scores becomes surprisals
- 1y agon1qn1cFunction-pointer API decouples nlmixr2est releases
- 2y agoweirdWine reviews dataset replaced with a fetch function
Frequently asked questions
What is the difference between n1qn1c and weird?
Both compete on the same themes — r-package — within Analytics. n1qn1c 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 n1qn1c better than weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. n1qn1c 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 n1qn1c?
Top n1qn1c alternatives in Analytics are ranked by recent ship velocity. Browse the "n1qn1c alternatives" section above for the current picks, or visit /alternatives/n1qn1c 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.