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SDTM supplemental-qualifier merging got sturdier, then the package went quiet for two years.
Comparison · Analytics
epidemics vs xplainfi
A side-by-side editorial comparison of epidemics and xplainfi — release velocity, themes, recent moves, and the top alternatives to consider.
epidemics vs xplainfi: at a glance
What is epidemics?
epidemics swapped its C++ engine for odin — and quietly inverted how contact matrices must be passed.
epidemics ships composable compartmental model structures — default SEIR-V, Vacamole, diphtheria and Ebola — with classes for populations, interventions and vaccination campaigns. After more than two years without a release, 0.5.0 landed in July 2026 carrying both an engine migration and a breaking input-convention change. Maintainership moved to a new lead back in 0.4.0.
What is xplainfi?
xplainfi treats feature importance as an estimate with error bars, not a number.
xplainfi implements feature importance methods for mlr3 — perturbation-based PFI, CFI and RFI, refit-based LOCO and WVIM, and SAGE. Its defining choice is that importance scores come with inference attached: several confidence-interval methods, including the Nadeau-Bengio correction and a distribution-free option added in 1.1.0. It declared itself released at 1.0.0 in January 2026.
epidemics vs xplainfi: editorial side-by-side
epidemics
3.8ANALYTICS
epidemics swapped its C++ engine for odin — and quietly inverted how contact matrices must be passed.
◆ Current state
epidemics ships composable compartmental model structures — default SEIR-V, Vacamole, diphtheria and Ebola — with classes for populations, interventions and vaccination campaigns. After more than two years without a release, 0.5.0 landed in July 2026 carrying both an engine migration and a breaking input-convention change. Maintainership moved to a new lead back in 0.4.0.
◆ Where it's heading
The package's history is a sequence of deliberate breaking releases: 0.2.0 renamed every model function and vectorised the ODE models, 0.3.0 restructured the Ebola model into two levels and made replicates the default, and 0.5.0 rewrites the compiled core. The direction is toward a smaller, more declarative model definition layer with the numerical work delegated to a dedicated tool.
◆ Prediction
With the default, Vacamole and diphtheria systems now declared in odin, the Ebola model is the obvious remaining candidate for the same treatment.
xplainfi
2.5ANALYTICS
xplainfi treats feature importance as an estimate with error bars, not a number.
◆ Current state
xplainfi implements feature importance methods for mlr3 — perturbation-based PFI, CFI and RFI, refit-based LOCO and WVIM, and SAGE. Its defining choice is that importance scores come with inference attached: several confidence-interval methods, including the Nadeau-Bengio correction and a distribution-free option added in 1.1.0. It declared itself released at 1.0.0 in January 2026.
◆ Where it's heading
Two lines of work run in parallel. The statistical side keeps adding inference options — variance corrections, conditional predictive impact, and the Lei et al. observation-wise loss-difference test — while the computational side attacks the cost of refit-based methods, most recently with a batch_size argument that parallelises refits and a default of one refit per resampling iteration. Support for pre-trained learners in 1.1.0 removes the refit requirement entirely in some workflows.
◆ Prediction
The stated reasoning that budget is better spent on resampling iterations than repeated refits suggests n_repeats may be removed from WVIM and LOCO outright, as the release notes hint.
Alternatives to epidemics and xplainfi
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 epidemics or xplainfi.
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See all epidemics alternatives → · See all xplainfi alternatives →
Recent activity from epidemics and xplainfi
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 20d agoxplainfiRefits parallelise; repeated refits deprioritised in favour of resampling
- 25d agoepidemicsODE models migrate to odin; contact matrices must no longer be transposed ⚡
- 5mo agoxplainfiPre-trained learners supported; distribution-free inference added
- 6mo agoxplainfiVersion bumped to mark the package as released
- 9mo agoxplainfiConfidence intervals arrive for feature importance scores
- 2y agoepidemicsPeak timing and scenario comparison helpers; negative-susceptible bug fixed
- 2y agoepidemicsEbola model accepts vector inputs and defaults to 100 replicates
- 2y agoepidemicsODE models vectorised; every model function renamed
- 2y agoepidemicsFirst release: four composable compartmental model structures
Frequently asked questions
What is the difference between epidemics and xplainfi?
They serve adjacent needs but don't currently overlap on shipped themes. epidemics is currently shipping more aggressively (velocity 3.8 vs 2.5), with 1 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is epidemics better than xplainfi?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. epidemics is currently shipping more aggressively (velocity 3.8 vs 2.5), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to epidemics?
Top epidemics alternatives in Analytics are ranked by recent ship velocity. Browse the "epidemics alternatives" section above for the current picks, or visit /alternatives/epidemics for the full list with editorial commentary on each.
What are the best alternatives to xplainfi?
Top xplainfi alternatives in Analytics are ranked by recent ship velocity. Browse the "xplainfi alternatives" section above for the current picks, or visit /alternatives/xplainfi for the full list with editorial commentary on each.