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Comparison · Analytics

cfrnow vs fasster

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

cfrnow vs fasster: at a glance

Featurecfrnowfasster
SectorAnalyticsAnalytics
Velocity score5.00.0
Sparks · 30d00
Top themesepidemiology, bayesian-modelling, cfr-estimation, r-packagetime series forecasting, state space models, fable, multiple seasonality
Last editorial update2h ago1h ago
WebsiteVisit →Visit →

What is cfrnow?

A Bayesian real-time CFR estimator that now ships stratified fits and posterior-predictive checks

cfrnow estimates case fatality ratios from line-list data while an outbreak is still running, using a Bayesian mixture-cure survival model registered as an `epidist` model type. Three releases in a month took it from first public release to stratified, partially-pooled fits with posterior-predictive checking. It leans on `distspec` for delay parameterisation, which reached CRAN alongside the 0.2.1 patch.

Read the full cfrnow trajectory →

What is fasster?

fasster arrives as a fable-compatible state space model for switching seasonality.

fasster implements FASSTER, a state space model with a switching component in the measurement equation, aimed at series carrying several seasonal patterns and abrupt structural change. Version 0.2.0 is the first substantive release: a formula interface with trend(), season(), fourier(), ARMA() and xreg() plus the %S% switching and %?% conditional operators, and the full fable method set. Parameters come from a filtering-and-smoothing heuristic rather than full optimisation.

Read the full fasster trajectory →

cfrnow vs fasster: editorial side-by-side

C
cfrnow
ANALYTICS
5.0

A Bayesian real-time CFR estimator that now ships stratified fits and posterior-predictive checks

◆ Current state

cfrnow estimates case fatality ratios from line-list data while an outbreak is still running, using a Bayesian mixture-cure survival model registered as an `epidist` model type. Three releases in a month took it from first public release to stratified, partially-pooled fits with posterior-predictive checking. It leans on `distspec` for delay parameterisation, which reached CRAN alongside the 0.2.1 patch.

◆ Where it's heading

The arc runs from producing a single corrected CFR number toward supporting a full model-checking workflow. 0.2.0 added the pieces a modeller needs to defend an estimate: replicate line lists replayed through the real-time truncation, an ascertainment-ratio correction for when fatal and non-fatal cases enter the line list at different rates, and per-group CFRs from `brms` formulas. Delay coverage widened from LogNormal and Gamma to Weibull in the same release.

◆ Prediction

Expect the next release to keep widening covariate and pooling support rather than adding new outcome types, since every 0.2.0 addition extended the existing formula interface instead of replacing it.

F
fasster
ANALYTICS
0.0

fasster arrives as a fable-compatible state space model for switching seasonality.

◆ Current state

fasster implements FASSTER, a state space model with a switching component in the measurement equation, aimed at series carrying several seasonal patterns and abrupt structural change. Version 0.2.0 is the first substantive release: a formula interface with trend(), season(), fourier(), ARMA() and xreg() plus the %S% switching and %?% conditional operators, and the full fable method set. Parameters come from a filtering-and-smoothing heuristic rather than full optimisation.

◆ Where it's heading

The package sat at a 2018 development version for over seven years, so the news is that it exists as a usable model at all. Implementing the whole fable contract — forecast(), refit(), stream(), interpolate(), components() — means it slots into an existing forecasting workflow instead of asking for its own. The heuristic estimator is the open question these entries leave unanswered.

◆ Prediction

The obvious next step is supplementing the heuristic parameter estimates with proper optimisation, though the two entries here give no direct signal on timing.

Alternatives to cfrnow and fasster

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 cfrnow or fasster.

See all cfrnow alternatives → · See all fasster alternatives →

Recent activity from cfrnow and fasster

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

  1. 5d agocfrnowdistspec dependency moves to CRAN
  2. 5d agocfrnowStratified CFR fits, Weibull delays, posterior-predictive checks
  3. 1mo agocfrnowFirst release: real-time CFR from a Bayesian mixture-cure model
  4. 6mo agofassterFASSTER lands as a complete fable model
  5. 7y agofassterEarly development build

Frequently asked questions

What is the difference between cfrnow and fasster?

They serve adjacent needs but don't currently overlap on shipped themes. cfrnow is currently shipping more aggressively (velocity 5.0 vs 0.0), with 0 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 cfrnow better than fasster?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. cfrnow is currently shipping more aggressively (velocity 5.0 vs 0.0), with 0 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 cfrnow?

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

What are the best alternatives to fasster?

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