tern.rbmi
Reference-based multiple imputation tables, shipping only what CRAN checks demand.
Comparison · Analytics
errors vs spmodel
A side-by-side editorial comparison of errors and spmodel — release velocity, themes, recent moves, and the top alternatives to consider.
errors vs spmodel: at a glance
What is errors?
errors keeps making uncertainty print the way each scientific field expects.
errors attaches uncertainty to numeric vectors and propagates it automatically through arithmetic, as part of the r-quantities family alongside units. The propagation core is settled; recent releases concentrate on presentation and integration — PDG rounding rules in 0.4.2, decimal support in parenthesis notation in 0.4.3, and ggplot2 deprecation tracking in 0.4.1 and 0.4.4.
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.
errors vs spmodel: editorial side-by-side
errors
0.0ANALYTICS
errors keeps making uncertainty print the way each scientific field expects.
◆ Current state
errors attaches uncertainty to numeric vectors and propagates it automatically through arithmetic, as part of the r-quantities family alongside units. The propagation core is settled; recent releases concentrate on presentation and integration — PDG rounding rules in 0.4.2, decimal support in parenthesis notation in 0.4.3, and ggplot2 deprecation tracking in 0.4.1 and 0.4.4.
◆ Where it's heading
Two threads run through this history. One is formatting convergence: uncertainty has field-specific conventions, and the package has been absorbing them one contributed pull request at a time rather than imposing a single style. The other is keeping the errors class first-class everywhere R users work — vctrs methods for dplyr 1.0, a geom_errors() layer for ggplot2, missing-value and duplicate handling. Both are integration work, which is what a type-extension package mostly is.
◆ Prediction
Expect further formatting conventions to arrive as contributions, following PDG rounding and the decimals option, plus continued upkeep against ggplot2 aesthetic deprecations that have forced two of the last four releases.
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 errors 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 errors or spmodel.
tern.mmrm
An MMRM tabulation package that has published nothing since its 2024 CRAN releases.
ggmagnify
A single-purpose ggplot2 inset tool, refining the same three arguments.
symengine
An R symbolic-maths binding whose changelog is really the C++ core's release notes.
gtfstools
gtfstools stopped guarding its own object model and started accepting everyone else's.
quantities
The glue package that makes R carry units and uncertainty through the same calculation.
See all errors alternatives → · See all spmodel alternatives →
Recent activity from errors 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
- 1y agospmodelBlock kriging for areal averages and their uncertainty ⚡
- 1y agoerrorserrors 0.4.4 replaces deprecated geom_errorbarh()
- 1y agospmodelRobust semivariogram and new covariance types for areal models
- 1y agoerrorserrors 0.4.3 supports decimals in parenthesis notation
- 1y agospmodelRange constraint option and redefined covariance type names
- 2y agoerrorserrors 0.4.2 adds PDG rounding rules
- 2y agoerrorserrors 0.4.1 handles missing values, fixes na.rm
- 3y agoerrorserrors 0.4.0 adds geom_errors() for automatic errorbars
- 5y agoerrorserrors 0.3.6
Frequently asked questions
What is the difference between errors and spmodel?
They serve adjacent needs but don't currently overlap on shipped themes. errors 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 errors better than spmodel?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. errors 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 errors?
Top errors alternatives in Analytics are ranked by recent ship velocity. Browse the "errors alternatives" section above for the current picks, or visit /alternatives/errors-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.