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cfrnow vs tulpa

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

cfrnow vs tulpa: at a glance

Featurecfrnowtulpa
SectorAnalyticsAnalytics
Velocity score5.07.5
Sparks · 30d02
Top themesepidemiology, bayesian-modelling, cfr-estimation, r-packagebayesian-inference, cran-release, r-packages, spatial-modeling
Last editorial update4d ago8h 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 tulpa?

The 0.0.x train stops at CRAN: tulpa's engine ships to the ecosystem it already anchors.

tulpa is the C++/R Bayesian spatial inference engine sitting under gcol33's family of ecological occupancy packages, tagging 0.0.x releases several times a week. 0.1.0 is its first CRAN release, and the notes state outright that the engine surface is unchanged from 0.0.198 — the work is packaging discipline: local T bindings rebound to n_t/n_times, OpenMP teams capped under R CMD check, the pkgdown deploy narrowed, an aspell dictionary added. The window behind it splits between the S3 generics conversion and numerical-correctness work in the nested-Laplace grid.

Read the full tulpa trajectory →

cfrnow vs tulpa: 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.

T
tulpa
ANALYTICS
7.5

The 0.0.x train stops at CRAN: tulpa's engine ships to the ecosystem it already anchors.

◆ Current state

tulpa is the C++/R Bayesian spatial inference engine sitting under gcol33's family of ecological occupancy packages, tagging 0.0.x releases several times a week. 0.1.0 is its first CRAN release, and the notes state outright that the engine surface is unchanged from 0.0.198 — the work is packaging discipline: local T bindings rebound to n_t/n_times, OpenMP teams capped under R CMD check, the pkgdown deploy narrowed, an aspell dictionary added. The window behind it splits between the S3 generics conversion and numerical-correctness work in the nested-Laplace grid.

◆ Where it's heading

Two moves in nine days point at the same destination: the generics conversion made tulpa extensible by downstream packages, and CRAN admission makes it installable by them. The current cadence — several tags a week, some existing only to record a measurement that produced no code change — does not survive CRAN's submission overhead, so the release rhythm has to slow whether or not the project intends it. The correctness work still clusters on the joint nested-Laplace driver, and 0.1.0 extends the same diagnostics habit with .NL_AXIS_SD_REASONS, a closed vocabulary for an outer axis whose grid does not contain its own posterior mode.

◆ Prediction

Expect tulpaObs to follow tulpa onto CRAN, since it is the consumer whose registrations the engine has spent this window unblocking, and expect the version line to move in larger, less frequent steps now that each one carries a submission.

Alternatives to cfrnow and tulpa

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 tulpa.

See all cfrnow alternatives → · See all tulpa alternatives →

Recent activity from cfrnow and tulpa

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

  1. 18h agotulpaFirst CRAN release: engine surface unchanged from 0.0.198
  2. 4d agotulpatulpa_re_aghq() exposes the mode/theta cross-Hessian
  3. 8d agotulpaDense batched joint path could silently drop a grid cell
  4. 8d agotulpaCalibration and goodness-of-fit entry points become S3 generics
  5. 9d agotulpaCUDA backend had two definitions; link order decided if it ran
  6. 9d agotulpaHyperparameter bounds now flag when they leave the node range
  7. 9d agocfrnowdistspec dependency moves to CRAN
  8. 10d agocfrnowStratified CFR fits, Weibull delays, posterior-predictive checks
  9. 1mo agocfrnowFirst release: real-time CFR from a Bayesian mixture-cure model

Frequently asked questions

What is the difference between cfrnow and tulpa?

They serve adjacent needs but don't currently overlap on shipped themes. tulpa is currently shipping more aggressively (velocity 7.5 vs 5.0), with 2 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 tulpa?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. tulpa is currently shipping more aggressively (velocity 7.5 vs 5.0), with 2 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 tulpa?

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