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epiflows vs modeltime.ensemble

A side-by-side editorial comparison of epiflows and modeltime.ensemble — release velocity, themes, recent moves, and the top alternatives to consider.

epiflows vs modeltime.ensemble: at a glance

Featureepiflowsmodeltime.ensemble
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesr, epidemiology, dormant, maintenancetime series forecasting, ensembles, tidymodels, compatibility maintenance
Last editorial update1h ago6h ago
WebsiteVisit →Visit →

What is epiflows?

epiflows has shipped four releases in eight years, none of which changed the code.

epiflows predicts the spread of infectious disease along population flows between locations, and it is effectively dormant. The whole visible history spans 2018 to 2026 in four entries: the first CRAN release, a Zenodo archival tag, a Roxygen patch whose notes state the functionality is unchanged, and a 2026 release replacing deprecated ggplot2 and tibble calls. No entry describes new epidemiological capability.

Read the full epiflows trajectory →

What is modeltime.ensemble?

modeltime.ensemble wakes after four years, and the work is all tune 2.0 compatibility.

modeltime.ensemble builds average, weighted and stacked ensembles over modeltime forecast models. After a four-year gap it shipped twice in a fortnight during August and September 2025, both releases devoted to tracking breaking changes in tidymodels' tune package — new resampling column conventions, key uniqueness across resamples, recipe preparation. The tidyverse dependency was dropped in the same pass.

Read the full modeltime.ensemble trajectory →

epiflows vs modeltime.ensemble: editorial side-by-side

E
epiflows
ANALYTICS
0.0

epiflows has shipped four releases in eight years, none of which changed the code.

◆ Current state

epiflows predicts the spread of infectious disease along population flows between locations, and it is effectively dormant. The whole visible history spans 2018 to 2026 in four entries: the first CRAN release, a Zenodo archival tag, a Roxygen patch whose notes state the functionality is unchanged, and a 2026 release replacing deprecated ggplot2 and tibble calls. No entry describes new epidemiological capability.

◆ Where it's heading

The package is being kept installable rather than developed. The one recent release is dependency maintenance contributed from outside, which is the pattern for RECON-era epidemiology packages that have outlived their original project funding. Two separate entries are both labelled version 0.2.1, so even the version history is not a reliable guide to what changed.

◆ Prediction

Any further releases will most likely be more deprecation cleanup to keep the package on CRAN; there is nothing in the record suggesting active development has resumed.

M0.0

modeltime.ensemble wakes after four years, and the work is all tune 2.0 compatibility.

◆ Current state

modeltime.ensemble builds average, weighted and stacked ensembles over modeltime forecast models. After a four-year gap it shipped twice in a fortnight during August and September 2025, both releases devoted to tracking breaking changes in tidymodels' tune package — new resampling column conventions, key uniqueness across resamples, recipe preparation. The tidyverse dependency was dropped in the same pass.

◆ Where it's heading

This is a package whose forecasting capability was settled by 2021 — recursive ensembles, per-series calibration — and whose recent life is dictated entirely by upstream tidymodels churn. New contributors did that compatibility work, including one from the tidymodels side. It now requires tune 2.0.0 and modeltime.resample 0.3.0, pinning it to the current tidymodels generation rather than straddling versions.

◆ Prediction

Expect the next release to follow the next tune or modeltime.resample breaking change rather than to introduce new ensembling methods.

Alternatives to epiflows and modeltime.ensemble

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 epiflows or modeltime.ensemble.

See all epiflows alternatives → · See all modeltime.ensemble alternatives →

Recent activity from epiflows and modeltime.ensemble

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 5mo agoepiflowsDeprecated ggplot2 and tibble calls replaced
  2. 11mo agomodeltime.ensembleRealigned for tune 2.0.0 resampling changes
  3. 11mo agomodeltime.ensembleDrops the tidyverse dependency ahead of tune 2.0
  4. 3y agoepiflowsRoxygen patch for CRAN checks
  5. 5y agomodeltime.ensemblePer-series calibration IDs and parallel refitting
  6. 5y agomodeltime.ensembleRecursive ensembles for single and panel series
  7. 7y agoepiflowsFirst Zenodo archival tag
  8. 8y agoepiflowsFirst CRAN release

Frequently asked questions

What is the difference between epiflows and modeltime.ensemble?

They serve adjacent needs but don't currently overlap on shipped themes. epiflows and modeltime.ensemble 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 epiflows better than modeltime.ensemble?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. epiflows and modeltime.ensemble 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 epiflows?

Top epiflows alternatives in Analytics are ranked by recent ship velocity. Browse the "epiflows alternatives" section above for the current picks, or visit /alternatives/epiflows-r for the full list with editorial commentary on each.

What are the best alternatives to modeltime.ensemble?

Top modeltime.ensemble alternatives in Analytics are ranked by recent ship velocity. Browse the "modeltime.ensemble alternatives" section above for the current picks, or visit /alternatives/modeltime-ensemble for the full list with editorial commentary on each.