rjdqa
rjdqa keeps refining one screen: the seasonal adjustment quality dashboard
A side-by-side editorial comparison of oblicubes and spmodel — release velocity, themes, recent moves, and the top alternatives to consider.
A tiny grid renderer for oblique-projection cubes, complete since its first release.
oblicubes draws 3D cubes and cuboids in oblique projection as grid grobs, with ggplot2 geom wrappers and a height-matrix helper for turning elevation data into coordinates. The entire feature set arrived in the initial 0.1.2 release, adapted from coolbutuseless's isocubes and cj-holmes's isocuboids. The two releases since have widened compatibility rather than added anything.
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.
oblicubes draws 3D cubes and cuboids in oblique projection as grid grobs, with ggplot2 geom wrappers and a height-matrix helper for turning elevation data into coordinates. The entire feature set arrived in the initial 0.1.2 release, adapted from coolbutuseless's isocubes and cj-holmes's isocuboids. The two releases since have widened compatibility rather than added anything.
The package is finished and its maintainer is treating it that way. 1.0.0 removed the R 4.1 native pipe from examples specifically so earlier R versions could use it — reaching backward, not forward — and added image alt text. The only change since is swapping a deprecated dplyr call in examples.
Expect nothing beyond occasional dependency deprecation fixes; the release pattern shows a small, deliberately complete package being kept installable.
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.
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.
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.
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 oblicubes or spmodel.
rjdqa keeps refining one screen: the seasonal adjustment quality dashboard
epikit narrows to field-epidemiology helpers, handing proportions to a sibling package
SimInf 10.0 turns an epidemic simulator into a tool that fits models to real time series
A young package porting Stata's egen row-wise helpers to the tidyverse, one function per release
A statistician's personal toolbox, growing one plotting utility at a time
R/qtl is in pure custodial mode: every recent release answers a compiler, not a user
See all oblicubes alternatives → · See all spmodel alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. oblicubes 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. oblicubes 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.
Top oblicubes alternatives in Analytics are ranked by recent ship velocity. Browse the "oblicubes alternatives" section above for the current picks, or visit /alternatives/oblicubes-r for the full list with editorial commentary on each.
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.