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Comparison · Analytics

EpiNow2 vs modeltime.resample

A side-by-side editorial comparison of EpiNow2 and modeltime.resample — release velocity, themes, recent moves, and the top alternatives to consider.

EpiNow2 vs modeltime.resample: at a glance

FeatureEpiNow2modeltime.resample
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesepidemiology, bayesian-modelling, reproduction-number, r-packagetime series, cross-validation, tidymodels, compatibility maintenance
Last editorial update2h ago1h ago
WebsiteVisit →Visit →

What is EpiNow2?

EpiNow2 unified its model interface, then went back to deepen the estimators behind it

EpiNow2 estimates reproduction numbers, infections and delay distributions from incomplete epidemiological reporting data. 1.8.0 was the structural turning point: every main modelling function now returns a consistent S3 object with `fit`, `args` and `observations`, reachable through shared accessors. The releases either side of it work on estimator quality — accumulation of irregularly reported data in 1.7.0, and a substantial expansion of `estimate_truncation()` in 1.9.0.

Read the full EpiNow2 trajectory →

What is modeltime.resample?

modeltime.resample exists to keep backtesting working as tidymodels shifts underneath it.

modeltime.resample runs time series cross-validation over modeltime models, returning per-resample predictions and accuracy plots. Version 0.3.0 is the substantive release in view: tune 2.0.0 compatibility, deterministic seeding via withr, guaranteed .predictions output, and clearer failures when resample fits break. The three releases before it are dependency chores.

Read the full modeltime.resample trajectory →

EpiNow2 vs modeltime.resample: editorial side-by-side

E
EpiNow2
ANALYTICS
0.0

EpiNow2 unified its model interface, then went back to deepen the estimators behind it

◆ Current state

EpiNow2 estimates reproduction numbers, infections and delay distributions from incomplete epidemiological reporting data. 1.8.0 was the structural turning point: every main modelling function now returns a consistent S3 object with `fit`, `args` and `observations`, reachable through shared accessors. The releases either side of it work on estimator quality — accumulation of irregularly reported data in 1.7.0, and a substantial expansion of `estimate_truncation()` in 1.9.0.

◆ Where it's heading

The package spent this window paying down interface debt and is now extending from the tidier base. Options that existed only for `estimate_infections()` have been propagated outward: `estimate_truncation()` gained the full `dist_spec` delay families, `obs_opts()` observation model selection between Poisson and negative binomial, and the `likelihood` and `return_likelihood` settings that make prior-only fits and loo-compatible output possible. Hardcoded assumptions are being replaced by specifiable ones in the same motion — the truncation model's additive noise term was a fixed `sigma ~ normal(0, 1)` prior and is now a `dist_spec` argument.

◆ Prediction

Expect the remaining modelling functions to keep converging on the shared options interface, since the last two releases have each moved another function onto it. A new `estimate_dist()` for interval-censored linelist data suggests delay estimation is the area still gaining surface.

M0.0

modeltime.resample exists to keep backtesting working as tidymodels shifts underneath it.

◆ Current state

modeltime.resample runs time series cross-validation over modeltime models, returning per-resample predictions and accuracy plots. Version 0.3.0 is the substantive release in view: tune 2.0.0 compatibility, deterministic seeding via withr, guaranteed .predictions output, and clearer failures when resample fits break. The three releases before it are dependency chores.

◆ Where it's heading

Every entry here is compatibility work against something upstream — hardhat 1.0.0, workflows regression mode, then tune 2.0.0 twice. The 0.3.0 notes show a second concern emerging alongside it: making failures legible, with .notes on failed fits, actionable errors from unnest_modeltime_resamples(), and fallback logic when prediction columns go missing across versions. Reproducibility gets the same treatment through explicit seeding.

◆ Prediction

Expect the next release to track the next tidymodels breaking change, with any new work continuing on error reporting rather than resampling strategies.

Alternatives to EpiNow2 and modeltime.resample

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 EpiNow2 or modeltime.resample.

See all EpiNow2 alternatives → · See all modeltime.resample alternatives →

Recent activity from EpiNow2 and modeltime.resample

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

  1. 1mo agoEpiNow2estimate_truncation gains full delay and observation options
  2. 6mo agoEpiNow2Unified return objects and shared accessors across all models
  3. 11mo agomodeltime.resampletune 2.0 support, deterministic seeding, clearer errors
  4. 11mo agomodeltime.resampleDependency cleanup ahead of the next tune release
  5. 1y agoEpiNow2Patch for an upstream rstan issue
  6. 1y agoEpiNow2Accumulation for irregularly reported data; unified priors
  7. 1y agoEpiNow2Matern kernel spectral density fix and GP prior revert
  8. 1y agoEpiNow2Gaussian Process model improvements and explicit defaults
  9. 3y agomodeltime.resampleFixes workflows in regression mode
  10. 4y agomodeltime.resampleUpdates for hardhat 1.0.0

Frequently asked questions

What is the difference between EpiNow2 and modeltime.resample?

They serve adjacent needs but don't currently overlap on shipped themes. EpiNow2 and modeltime.resample 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 EpiNow2 better than modeltime.resample?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. EpiNow2 and modeltime.resample 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 EpiNow2?

Top EpiNow2 alternatives in Analytics are ranked by recent ship velocity. Browse the "EpiNow2 alternatives" section above for the current picks, or visit /alternatives/epinow2 for the full list with editorial commentary on each.

What are the best alternatives to modeltime.resample?

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