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dvir vs tulpa

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

dvir vs tulpa: at a glance

Featuredvirtulpa
SectorAnalyticsAnalytics
Velocity score0.07.5
Sparks · 30d02
Top themesforensic genetics, victim identification, pipeline, scalabilitybayesian-inference, cran-release, r-packages, spatial-modeling
Last editorial update3d ago8h ago
WebsiteVisit →Visit →

What is dvir?

dvir keeps making disaster victim identification a single call instead of a workflow.

dvir handles disaster victim identification: matching unidentified remains against reference families using pedigree likelihoods. The package has consolidated around dviSolve(), a complete pipeline introduced in 3.2.1 and rewritten in 3.3.0 to use generalised likelihood ratios for families with several missing persons. Recent releases have been about making that pipeline survive large cases, adding dviGridSize() and a maxAssign cutoff to skip joint analysis when the combination count explodes, plus per-step timings.

Read the full dvir 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 →

dvir vs tulpa: editorial side-by-side

D
dvir
ANALYTICS
0.0

dvir keeps making disaster victim identification a single call instead of a workflow.

◆ Current state

dvir handles disaster victim identification: matching unidentified remains against reference families using pedigree likelihoods. The package has consolidated around dviSolve(), a complete pipeline introduced in 3.2.1 and rewritten in 3.3.0 to use generalised likelihood ratios for families with several missing persons. Recent releases have been about making that pipeline survive large cases, adding dviGridSize() and a maxAssign cutoff to skip joint analysis when the combination count explodes, plus per-step timings.

◆ Where it's heading

The arc is from a toolbox of functions toward one supervised pipeline, with the older jointDVI() now emitting a legacy message. The current constraint is combinatorial: joint analysis over many victims and missing persons blows up, so the work has gone to measuring the blowup and bailing out of it. Parallelism is mid-migration, with the parallel and pbapply implementation removed and a mirai replacement stated as planned but not yet shipped, leaving numCores accepted and ignored with a warning.

◆ Prediction

The mirai-based parallelisation is announced as coming, so expect it next, most likely applied to the joint analysis step that maxAssign currently exists to avoid.

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 dvir 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 dvir or tulpa.

See all dvir alternatives → · See all tulpa alternatives →

Recent activity from dvir and tulpa

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

  1. 17h 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. 1mo agodvirCombination sizing and a cutoff for joint analysis
  8. 3mo agodvirJoint tables added to solver output
  9. 3mo agodvirVictim and database accessors, plus solver refinements
  10. 1y agodvirSolver rewritten around generalised likelihood ratios
  11. 2y agodvirdviSolve() pipeline and manual pairing controls
  12. 2y agodvirMaintainer handover and small reporting additions

Frequently asked questions

What is the difference between dvir and tulpa?

They serve adjacent needs but don't currently overlap on shipped themes. tulpa is currently shipping more aggressively (velocity 7.5 vs 0.0), 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 dvir 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 0.0), 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 dvir?

Top dvir alternatives in Analytics are ranked by recent ship velocity. Browse the "dvir alternatives" section above for the current picks, or visit /alternatives/dvir 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.