tibblify
tibblify learned to derive its own specs from OpenAPI, removing the step users disliked most
Comparison · Analytics
dvir vs parglm
A side-by-side editorial comparison of dvir and parglm — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:parallel computing
dvir vs parglm: at a glance
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.
What is parglm?
Under a new maintainer, parglm traded raw speed work for glm parity and memory safety
parglm fits generalized linear models using parallel QR decomposition, targeting datasets where glm() is too slow. Tom Palmer took over maintenance at 0.1.8 in April 2026, and the package has released five times since — a burst of activity after a long quiet period. 0.2.0 in July 2026 is the first release to focus on correctness rather than throughput.
dvir vs parglm: editorial side-by-side
dvir
0.0ANALYTICS
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.
parglm
0.0ANALYTICS
Under a new maintainer, parglm traded raw speed work for glm parity and memory safety
◆ Current state
parglm fits generalized linear models using parallel QR decomposition, targeting datasets where glm() is too slow. Tom Palmer took over maintenance at 0.1.8 in April 2026, and the package has released five times since — a burst of activity after a long quiet period. 0.2.0 in July 2026 is the first release to focus on correctness rather than throughput.
◆ Where it's heading
The arc runs from performance to trustworthiness. 0.1.9 was a large optimization release — deque-based task queues, fused memory passes, upper-triangle-only Fisher information, thread_local IDs — plus ecosystem integration with sandwich and gtsummary. 0.2.0 then fixed an out-of-bounds write triggered by small block_size values and a path where a non-finite working response could poison the QR decomposition, and brought response-type handling in line with glm().
◆ Prediction
With the memory-safety issues addressed and glm parity closed for binomial responses, further work is likely to extend family coverage or the benchmark suite rather than revisit the threading model. The C++17 requirement set at 0.1.8 gives room for more aggressive optimization if the maintainer returns to that.
Alternatives to dvir and parglm
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 parglm.
spsurvey
spsurvey has spent four years consolidating after its 5.0.0 rewrite rather than adding to it
StreamCatTools
StreamCatTools is quietly moving off web services and onto cloud-native GeoParquet
reproducible
reproducible added a windowed read path so remote GeoTiffs never fully download
qcTAF
qcTAF is building an automated checklist for reproducible fisheries assessments, one criterion at a time
rpymat
After three dormant years, rpymat returned to fix the OpenMP crash that breaks R and conda together
Recent activity from dvir and parglm
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoparglmglm-compatible response types plus out-of-bounds and QR safety fixes
- 1mo agodvirCombination sizing and a cutoff for joint analysis
- 2mo agoparglmBuild fixes for macOS SDK and thread pool includes
- 3mo agodvirJoint tables added to solver output
- 3mo agoparglmAdd missing <thread> include for older Apple SDKs
- 3mo agoparglmThreading overhaul, quasi families, and sandwich/gtsummary compatibility
- 3mo agodvirVictim and database accessors, plus solver refinements
- 3mo agoparglmNew maintainer takes over; C++17 now required
- 1y agodvirSolver rewritten around generalised likelihood ratios
- 2y agodvirdviSolve() pipeline and manual pairing controls
- 2y agodvirMaintainer handover and small reporting additions
Frequently asked questions
What is the difference between dvir and parglm?
Both compete on the same themes — parallel computing — within Analytics. dvir and parglm 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 dvir better than parglm?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. dvir and parglm 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 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 parglm?
Top parglm alternatives in Analytics are ranked by recent ship velocity. Browse the "parglm alternatives" section above for the current picks, or visit /alternatives/parglm for the full list with editorial commentary on each.