EvoPhylo
EvoPhylo added BEAST2 alongside MrBayes, then went quiet for two years.
Comparison · Analytics
qtl vs weird
A side-by-side editorial comparison of qtl and weird — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
qtl vs weird: at a glance
What is qtl?
R/qtl is in pure custodial mode: every recent release answers a compiler, not a user
R/qtl is the long-established R package for QTL mapping in experimental crosses, covering interval mapping, composite interval mapping, multiple-QTL model fitting and the associated cross data formats. Nothing in the recent release history adds capability. Version 1.74 removes an include that started warning on CRAN, 1.72 improves an error message in cim(), and 1.70 migrates the C code from Calloc/Realloc/Free to their R_-prefixed equivalents for R-devel.
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.
qtl vs weird: editorial side-by-side
qtl
0.0ANALYTICS
R/qtl is in pure custodial mode: every recent release answers a compiler, not a user
◆ Current state
R/qtl is the long-established R package for QTL mapping in experimental crosses, covering interval mapping, composite interval mapping, multiple-QTL model fitting and the associated cross data formats. Nothing in the recent release history adds capability. Version 1.74 removes an include that started warning on CRAN, 1.72 improves an error message in cim(), and 1.70 migrates the C code from Calloc/Realloc/Free to their R_-prefixed equivalents for R-devel.
◆ Where it's heading
The package is being maintained, not developed. The work divides cleanly into keeping the compiled code building against successive R and toolchain versions, and fixing narrow bugs reported through the issue tracker. The C-level migrations in particular are compliance with R's tightening of its C interface rather than anything chosen. Users should read the stability as maturity: the analysis surface has been fixed for years and the maintainer is keeping it installable.
◆ Prediction
R has continued to restrict its non-API C entry points, and this package has already made two such migrations, so further compile-time compliance work is the most likely content of the next release.
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 qtl 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 qtl or weird.
treespace
treespace ships once every year or two, and only when CRAN or a user forces it.
Rpath
Rpath 1.1.0 learns to read Ecopath's own files, easing migration off the desktop tool.
TAF
TAF keeps turning ICES stock assessments into reproducible, dependency-pinned projects.
parallelDist
parallelDist is in pure preservation mode — one build fix every few years.
cairo
Cairo finally catches R's graphics engine, all the way to GE API 16.
Recent activity from qtl 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
- 8mo agoqtlRemove R_ext/PrtUtil.h include flagged by CRAN
- 8mo agoqtlClearer cim() error when multiple phenotypes are passed
- 1y agoqtlC memory calls migrated to R_Calloc/R_Realloc/R_Free
- 2y agoweirdWine reviews dataset replaced with a fetch function
- 2y agoqtlFix Rprintf call and remaining compiler warnings
- 2y agoqtlFix summary.scanone() thresholds and csvs phenotype reading
- 3y agoqtlFix addint()/addcovarint() with X chromosome QTL and missing phenotypes
Frequently asked questions
What is the difference between qtl and weird?
Both compete on the same themes — r-package — within Analytics. qtl 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 qtl better than weird?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. qtl 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 qtl?
Top qtl alternatives in Analytics are ranked by recent ship velocity. Browse the "qtl alternatives" section above for the current picks, or visit /alternatives/qtl-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.