CMAQ
CMAQ went global in v5.5, and has been patching that surface ever since.
Comparison · Analytics
rjdqa vs spmodel
A side-by-side editorial comparison of rjdqa and spmodel — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
rjdqa vs spmodel: at a glance
What is rjdqa?
rjdqa keeps refining one screen: the seasonal adjustment quality dashboard
rjdqa builds quality assessment dashboards for seasonal adjustment models produced by JDemetra+, aimed at official statisticians reviewing adjusted series. Essentially all development goes into two functions, simple_dashboard() and its denser variant simple_dashboard2(). Version 0.1.6 adds parameters to append observations to the forecast and to control whether the residual trading-days test is printed, defaulting to monthly series only, plus outlier table layout work and user-defined calendar regressor support in sc_dashboard().
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.
rjdqa vs spmodel: editorial side-by-side
rjdqa
0.0ANALYTICS
rjdqa keeps refining one screen: the seasonal adjustment quality dashboard
◆ Current state
rjdqa builds quality assessment dashboards for seasonal adjustment models produced by JDemetra+, aimed at official statisticians reviewing adjusted series. Essentially all development goes into two functions, simple_dashboard() and its denser variant simple_dashboard2(). Version 0.1.6 adds parameters to append observations to the forecast and to control whether the residual trading-days test is printed, defaulting to monthly series only, plus outlier table layout work and user-defined calendar regressor support in sc_dashboard().
◆ Where it's heading
The package has converged on a single deliverable and is tuning it against reviewer practice. Each release adds a parameter that lets the analyst include or exclude one element of the dashboard, or adjusts how densely information is packed into the fixed space of the layout. The td_effect default — print the test only for monthly series — is characteristic: the knowledge about when a diagnostic is meaningful is being encoded into the tool rather than left to the reader.
◆ Prediction
The pattern of adding one toggle per diagnostic per release points at the same thing again, most likely another test given a conditional default, rather than a new dashboard function alongside the two that exist.
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 rjdqa 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 rjdqa 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.
See all rjdqa alternatives → · See all spmodel alternatives →
Recent activity from rjdqa 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
- 9mo agospmodelCloud semivariogram doubling fixed; geometry warnings added
- 9mo agorjdqaForecast observations and conditional trading-days test in dashboards
- 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 agorjdqaFix tail() usage on ts objects
- 2y agorjdqaFix dependency minimums and outlier ordering
- 2y agorjdqasimple_dashboard2() added; deprecated sa_dashboard() removed
- 2y agorjdqasimple_dashboard() introduced; sa_dashboard() deprecated
- 7y agorjdqaFirst CRAN release
Frequently asked questions
What is the difference between rjdqa and spmodel?
Both compete on the same themes — r-package — within Analytics. rjdqa 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 rjdqa better than spmodel?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. rjdqa 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 rjdqa?
Top rjdqa alternatives in Analytics are ranked by recent ship velocity. Browse the "rjdqa alternatives" section above for the current picks, or visit /alternatives/rjdqa-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.