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dipsaus vs EpiNow2

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

Shared themes:r-package

dipsaus vs EpiNow2: at a glance

FeaturedipsausEpiNow2
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesshiny, parallel-computing, developer-tools, r-packageepidemiology, bayesian-modelling, reproduction-number, r-package
Last editorial update55m ago8h ago
WebsiteVisit →Visit →

What is dipsaus?

dipsaus sheds five dependencies and rebuilds its native layer on Rcpp

dipsaus is a utility toolbox for R and Shiny developers — parallel helpers, fast map and queue wrappers, RStudio integrations, and custom Shiny inputs — and the foundation layer under the RAVE neuroimaging stack. The 0.3.x line dropped magrittr, remotes, glue, base64url and startup, moved off RcppParallel and TBB, and switched to Rcpp specifically to stop calling R's internal ENCLOS and CLOSENV interfaces. On the user-facing side it added fancyDirectoryInput, a Shiny widget for uploading whole directories with streaming and a progress bar.

Read the full dipsaus trajectory →

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 →

dipsaus vs EpiNow2: editorial side-by-side

D
dipsaus
ANALYTICS
0.0

dipsaus sheds five dependencies and rebuilds its native layer on Rcpp

◆ Current state

dipsaus is a utility toolbox for R and Shiny developers — parallel helpers, fast map and queue wrappers, RStudio integrations, and custom Shiny inputs — and the foundation layer under the RAVE neuroimaging stack. The 0.3.x line dropped magrittr, remotes, glue, base64url and startup, moved off RcppParallel and TBB, and switched to Rcpp specifically to stop calling R's internal ENCLOS and CLOSENV interfaces. On the user-facing side it added fancyDirectoryInput, a Shiny widget for uploading whole directories with streaming and a progress bar.

◆ Where it's heading

Two long-running threads run through every release: cut dependencies, and make asynchronous work in R less fragile. The package has been removing packages it once required — synchronicity, qs, RcppRedis, htmltools, stringr, now five more — while successively replacing its own async machinery (make_async_evaluator, then async_workers, then lapply_callr and lapply_async with automatic global handling). The Rcpp move adds a third pressure: staying inside R's supported C interfaces as the non-API surface is closed off.

◆ Prediction

The dependency-shedding pattern points at the remaining soft-deprecated pieces — dipsaus_lock/unlock and PersistContainer have both been marked for removal for several releases and are the obvious next things to go.

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.

Alternatives to dipsaus and EpiNow2

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 dipsaus or EpiNow2.

See all dipsaus alternatives → · See all EpiNow2 alternatives →

Recent activity from dipsaus and EpiNow2

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. 7mo agodipsausDirectory upload widget lands; native layer moves to Rcpp
  4. 1y agoEpiNow2Patch for an upstream rstan issue
  5. 1y agoEpiNow2Accumulation for irregularly reported data; unified priors
  6. 1y agoEpiNow2Matern kernel spectral density fix and GP prior revert
  7. 1y agoEpiNow2Gaussian Process model improvements and explicit defaults
  8. 4y agodipsausrs_edit_file, constrained %OF% operator, nested rs_exec
  9. 4y agodipsausget_credential added; synchronicity dependency dropped
  10. 4y agodipsauslapply_callr replaces async_workers; qs and RcppRedis removed
  11. 5y agodipsausBackground job scheduling and parallel covariance helpers
  12. 6y agodipsausRStudio-aware helpers with console fallbacks

Frequently asked questions

What is the difference between dipsaus and EpiNow2?

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

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

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

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.