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Comparison · Analytics
qtl vs spmodel
A side-by-side editorial comparison of qtl and spmodel — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
qtl vs spmodel: 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 spmodel?
Spatial regression in R, adding block kriging and then tuning the numerics underneath it
spmodel fits spatial linear and generalised linear models, for both point-referenced and areal data, with prediction and diagnostics attached. Block prediction arrived in 0.11.0 and the releases since have refined it. The most recent release changes optimiser behaviour: the default Nelder-Mead relative stopping tolerance tightens from 1e-4 to 1e-6 to reduce convergence on local rather than global maxima.
qtl vs spmodel: 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.
spmodel
0.0ANALYTICS
Spatial regression in R, adding block kriging and then tuning the numerics underneath it
◆ Current state
spmodel fits spatial linear and generalised linear models, for both point-referenced and areal data, with prediction and diagnostics attached. Block prediction arrived in 0.11.0 and the releases since have refined it. The most recent release changes optimiser behaviour: the default Nelder-Mead relative stopping tolerance tightens from 1e-4 to 1e-6 to reduce convergence on local rather than global maxima.
◆ Where it's heading
Two threads run in parallel. The first is expanding what can be predicted — point predictions, then areal averages over a region via block kriging, then better accuracy and efficiency for that path as the block size default moved from 1000 to 4000 in 0.12.0. The second is numerical trustworthiness, and it is unusually prominent here: a range-constraint option for stability in 0.9.0, a corrected log determinant of the fixed effects in the restricted log likelihood in 0.11.0, a cloud semivariogram that had been doubling the semivariance fixed in 0.11.1, and now a tighter optimiser tolerance. Several of these silently changed results before they were caught.
◆ Prediction
Expect the maintainers to keep publishing explicit reproduction instructions alongside numerical default changes, as 0.13.0 does by documenting the `control = list(reltol = 1e-4)` escape hatch. The entries give no signal of expansion beyond the current model families.
Alternatives to qtl and spmodel
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 spmodel.
enpls
enpls has not changed its statistics since 2016 — only its website, twice.
grex
grex is a lookup table with a version number — it ships when the annotation moves.
protr
protr's feature set is finished; the work now is surviving Bioconductor's churn.
seriation
seriation stopped shipping algorithms and started shipping a way to pick between them.
arulesViz
arulesViz finished its move to ggplot2 and has been coasting on maintenance since.
Recent activity from qtl and spmodel
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agospmodelTighter optimiser tolerance to avoid local maxima
- 6mo agospmodelEmpirical autocovariance function and better block kriging accuracy
- 8mo agoqtlRemove R_ext/PrtUtil.h include flagged by CRAN
- 8mo agoqtlClearer cim() error when multiple phenotypes are passed
- 9mo agospmodelCloud semivariogram doubling fixed; geometry warnings added
- 1y agospmodelBlock kriging for areal averages and their uncertainty ⚡
- 1y agospmodelRobust semivariogram and new covariance types for areal models
- 1y agospmodelRange constraint option and redefined covariance type names
- 1y agoqtlC memory calls migrated to R_Calloc/R_Realloc/R_Free
- 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 spmodel?
Both compete on the same themes — r-package — within Analytics. qtl and spmodel 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 spmodel?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. qtl and spmodel 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 spmodel?
Top spmodel alternatives in Analytics are ranked by recent ship velocity. Browse the "spmodel alternatives" section above for the current picks, or visit /alternatives/spmodel for the full list with editorial commentary on each.