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Comparison · Analytics
audubon vs spmodel
A side-by-side editorial comparison of audubon and spmodel — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
audubon vs spmodel: at a glance
What is audubon?
audubon's release feed is almost entirely Renovate bumping the JavaScript toolchain behind its Japanese text splitter.
An R package for Japanese text processing — normalisation, tokenisation via MeCab and SudachiPy, and phrase splitting through budoux. Ten releases since 2022, but the changelogs are dominated by automated dependency updates to a webpack, babel and prettier toolchain, because the budoux component is JavaScript that has to be bundled. Actual R-facing changes appear in perhaps one release in three.
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.
audubon vs spmodel: editorial side-by-side
audubon
0.0ANALYTICS
audubon's release feed is almost entirely Renovate bumping the JavaScript toolchain behind its Japanese text splitter.
◆ Current state
An R package for Japanese text processing — normalisation, tokenisation via MeCab and SudachiPy, and phrase splitting through budoux. Ten releases since 2022, but the changelogs are dominated by automated dependency updates to a webpack, babel and prettier toolchain, because the budoux component is JavaScript that has to be bundled. Actual R-facing changes appear in perhaps one release in three.
◆ Where it's heading
The package appears feature-stable and in maintenance. The last substantive R-level addition visible here is bind_lr() for bigram LR values back in 0.5.0; everything since has been dependency hygiene, a tokeniser refactor, and platform-specific test fixes. That is a reasonable end state for a wrapper whose value is the binding rather than ongoing invention, but it does mean the release feed carries almost no signal about the package itself — a reader watching this feed would learn more about webpack's version history than about Japanese text processing.
◆ Prediction
Expect the Renovate cadence to continue setting the release rhythm, with R-facing changes arriving only when budoux itself gains capability or a platform breaks.
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 audubon 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 audubon or spmodel.
OpenCTI
OpenCTI is rebuilding its connector layer into a marketplace and wiring the platform to XTM Hub
rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
affiner
affiner is quietly turning a grid transformation helper into a small computational geometry library.
ageproR
ageproR spent two years chasing a moving file format, then added the recruitment models that justify the effort.
r-ledger
ledger adds a Rust toolchain fallback, so beancount imports work whether or not the Python tooling is installed.
See all audubon alternatives → · See all spmodel alternatives →
Recent activity from audubon and spmodel
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agospmodelTighter optimiser tolerance to avoid local maxima
- 3mo agoaudubonM1 Mac locale crash worked around in examples
- 6mo agospmodelEmpirical autocovariance function and better block kriging accuracy
- 7mo agoaudubonaudubon 0.6.2
- 7mo agoaudubonAutomated dependency bumps, including a webpack security update
- 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
- 2y agoaudubonbudoux bumped to 0.6.2; Renovate configured
- 3y agoaudubonMeCab and SudachiPy tokenisers refactored
- 3y agoaudubonbind_lr() computes LR values for bigrams
Frequently asked questions
What is the difference between audubon and spmodel?
Both compete on the same themes — r-package — within Analytics. audubon 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 audubon better than spmodel?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. audubon 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 audubon?
Top audubon alternatives in Analytics are ranked by recent ship velocity. Browse the "audubon alternatives" section above for the current picks, or visit /alternatives/audubon-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.