Basedash
Basedash keeps pushing its data out of the workspace — now to people without accounts
A side-by-side editorial comparison of segregatr and tulpa — release velocity, themes, recent moves, and the top alternatives to consider.
A segregation-analysis tool that keeps widening which pedigrees it can actually handle.
segregatr computes full-likelihood Bayes factors for variant segregation in families, built on pedtools and part of the wider pedsuite ecosystem. Its releases are infrequent but each one lifts a structural restriction: loops, recessive and X-linked models, liability classes, and most recently a proband-free variant of the score. The companion shinyseg app gives the same machinery a clinical front end.
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.
segregatr computes full-likelihood Bayes factors for variant segregation in families, built on pedtools and part of the wider pedsuite ecosystem. Its releases are infrequent but each one lifts a structural restriction: loops, recessive and X-linked models, liability classes, and most recently a proband-free variant of the score. The companion shinyseg app gives the same machinery a clinical front end.
The through-line is coverage of awkward real-world pedigrees rather than new statistics. Loops were handled for the core score in 0.3.0 and then extended to liability classes in 0.4.0, so the same structural capability is being pushed through the codebase feature by feature. The 2025 release moves in a different direction, relaxing the requirement for a designated proband. Development is slow and steady, roughly annual, and tracks its pedtools dependency closely.
Expect the next release to continue relaxing modelling constraints — the pattern of retrofitting each new capability across loops, liability classes and inheritance models is unfinished — rather than expanding beyond segregation scoring.
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.
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.
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.
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 segregatr or tulpa.
Basedash keeps pushing its data out of the workspace — now to people without accounts
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Dovetail spent July opening doors to other tools and August making its own rooms easier to enter.
See all segregatr alternatives → · See all tulpa alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
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.
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.
Top segregatr alternatives in Analytics are ranked by recent ship velocity. Browse the "segregatr alternatives" section above for the current picks, or visit /alternatives/segregatr for the full list with editorial commentary on each.
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.