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Comparison · Analytics
epikit vs spmodel
A side-by-side editorial comparison of epikit and spmodel — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
epikit vs spmodel: at a glance
What is epikit?
epikit narrows to field-epidemiology helpers, handing proportions to a sibling package
epikit is a set of small helpers for applied epidemiology in R — age categorisation, date reconstruction from partial records, and related field-data chores, developed in the R4Epis orbit. Version 0.2.0 moved the proportion functions out to epitabulate, improved how find_date_cause(), find_start_date() and find_end_date() handle dates falling outside the period, and added a floor argument to age_categories() so the lowest band reads as under one rather than zero to zero.
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.
epikit vs spmodel: editorial side-by-side
epikit
0.0ANALYTICS
epikit narrows to field-epidemiology helpers, handing proportions to a sibling package
◆ Current state
epikit is a set of small helpers for applied epidemiology in R — age categorisation, date reconstruction from partial records, and related field-data chores, developed in the R4Epis orbit. Version 0.2.0 moved the proportion functions out to epitabulate, improved how find_date_cause(), find_start_date() and find_end_date() handle dates falling outside the period, and added a floor argument to age_categories() so the lowest band reads as under one rather than zero to zero.
◆ Where it's heading
The package is being scoped down rather than built out. The 0.1.3 restructuring and the 0.2.0 handover of proportions to epitabulate are the same move made twice: push functionality into the package where it belongs and keep epikit to the toolkit that field epidemiologists reach for directly. The rest of the history is dependency compatibility work against dplyr and tibble.
◆ Prediction
With proportions gone and dependencies trimmed, the remaining functions cluster tightly around dates and age bands, so further refinement of the date-reconstruction helpers is more likely than new capability areas.
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 epikit 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 epikit 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 epikit alternatives → · See all spmodel alternatives →
Recent activity from epikit 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 agoepikitProportion functions moved to epitabulate; date helpers warn correctly
- 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
- 3y agoepikitFunctions rearranged across sibling packages
- 5y agoepikitRaise dplyr and tibble minimums; move CI to GitHub Actions
- 5y agoepikitCompatibility release for dplyr 1.0.0
- 6y agoepikitFirst CRAN release
Frequently asked questions
What is the difference between epikit and spmodel?
Both compete on the same themes — r-package — within Analytics. epikit 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 epikit better than spmodel?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. epikit 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 epikit?
Top epikit alternatives in Analytics are ranked by recent ship velocity. Browse the "epikit alternatives" section above for the current picks, or visit /alternatives/epikit-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.