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EpiNow2 vs fastplyr

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

Shared themes:r-package

EpiNow2 vs fastplyr: at a glance

FeatureEpiNow2fastplyr
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesepidemiology, bayesian-modelling, reproduction-number, r-packagedataframe-performance, dplyr-alternative, query-optimization, cran-policy
Last editorial update3h ago49m 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 fastplyr?

A fast dplyr stand-in that keeps finding new places to skip work entirely.

fastplyr reimplements the dplyr verbs on a faster backend, exposing f_summarise, f_mutate, f_reframe and a set of group metadata helpers alongside optimized joins and quantiles. The most recent release removes non-API C functions and raises the floor to R 4.5.0, a steep requirement that follows the C++17 requirement introduced a release earlier. The verb surface itself has been stable since 0.9.0.

Read the full fastplyr trajectory →

EpiNow2 vs fastplyr: 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.

F
fastplyr
ANALYTICS
0.0

A fast dplyr stand-in that keeps finding new places to skip work entirely.

◆ Current state

fastplyr reimplements the dplyr verbs on a faster backend, exposing f_summarise, f_mutate, f_reframe and a set of group metadata helpers alongside optimized joins and quantiles. The most recent release removes non-API C functions and raises the floor to R 4.5.0, a steep requirement that follows the C++17 requirement introduced a release earlier. The verb surface itself has been stable since 0.9.0.

◆ Where it's heading

The optimization strategy has shifted from making individual functions fast to reasoning about expressions before evaluating them — 0.9.9 began marking simple operators as group-unaware so expressions built only from them are evaluated across the whole data frame rather than per group. That is a structural bet: the package increasingly inspects what you wrote to decide how much work is actually needed. Running alongside it is a steady tightening of build requirements, with C++17, R 4.5.0 and CRAN's C API rules all landing within a year.

◆ Prediction

Expect the group-unaware classification to widen to more functions, since each addition compounds across every grouped expression, and expect the dependency floors to keep rising as the package tracks CRAN's compiled-code policy.

Alternatives to EpiNow2 and fastplyr

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 fastplyr.

See all EpiNow2 alternatives → · See all fastplyr alternatives →

Recent activity from EpiNow2 and fastplyr

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

  1. 1mo agoEpiNow2estimate_truncation gains full delay and observation options
  2. 4mo agofastplyrNon-API C functions dropped, R 4.5.0 now required
  3. 6mo agoEpiNow2Unified return objects and shared accessors across all models
  4. 8mo agofastplyrIn-place sorting arrives with a C++17 requirement
  5. 10mo agofastplyrGroup-unaware expressions evaluated on the whole frame
  6. 1y agofastplyrf_mutate and f_reframe complete the verb set
  7. 1y agoEpiNow2Patch for an upstream rstan issue
  8. 1y agoEpiNow2Accumulation for irregularly reported data; unified priors
  9. 1y agofastplyrDynamic argument evaluation and f_pull
  10. 1y agofastplyrf_fill added and grouped joins repaired
  11. 1y agoEpiNow2Matern kernel spectral density fix and GP prior revert
  12. 1y agoEpiNow2Gaussian Process model improvements and explicit defaults

Frequently asked questions

What is the difference between EpiNow2 and fastplyr?

Both compete on the same themes — r-package — within Analytics. EpiNow2 and fastplyr 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 fastplyr?

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

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