Basedash
Basedash keeps pushing its data out of the workspace — now to people without accounts
A side-by-side editorial comparison of tulpa and TwoSampleMR — release velocity, themes, recent moves, and the top alternatives to consider.
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.
The flagship Mendelian randomization package is auditing its own estimators, one bootstrap at a time.
TwoSampleMR is the MRC-IEU package for two-sample Mendelian randomization against OpenGWAS. Its 2026 releases are a sustained correctness review rather than feature work: 0.7.9 fixed bootstrap standard errors in mr_mode() and mr_rucker_bootstrap() that had been inflated since v0.6.30, and repaired two Rucker functions that were returning malformed objects. Point estimates were not affected by the bootstrap bug.
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.
TwoSampleMR is the MRC-IEU package for two-sample Mendelian randomization against OpenGWAS. Its 2026 releases are a sustained correctness review rather than feature work: 0.7.9 fixed bootstrap standard errors in mr_mode() and mr_rucker_bootstrap() that had been inflated since v0.6.30, and repaired two Rucker functions that were returning malformed objects. Point estimates were not affected by the bootstrap bug.
The pattern across these releases is a package being read line by line — a copy-paste weight vector in ldsc_rg(), chunking that produced zero splits for short SNP lists, penalisation recycled across the wrong SNPs, dead code paths removed, and regression tests added behind each fix. Alongside it runs a mechanical modernization pass: seq_len() for loop indices, tidyr in place of reshape2, current ggplot2 idioms, and the OpenGWAS URL migration. Feature work is limited to forest plot presentation.
Expect the audit to continue through the remaining bootstrap and jackknife routines, with releases staying in the 0.7.x patch range and each fix arriving with its own regression test.
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 tulpa or TwoSampleMR.
Basedash keeps pushing its data out of the workspace — now to people without accounts
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See all tulpa alternatives → · See all TwoSampleMR 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 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.
Top TwoSampleMR alternatives in Analytics are ranked by recent ship velocity. Browse the "TwoSampleMR alternatives" section above for the current picks, or visit /alternatives/twosamplemr for the full list with editorial commentary on each.