Basedash
Basedash keeps pushing its data out of the workspace — now to people without accounts
A side-by-side editorial comparison of mantis and tulpa — release velocity, themes, recent moves, and the top alternatives to consider.
Thirteen months of rOpenSci review turned a pre-release into a 1.0 with a stable API
mantis builds interactive time-series reports — heatmaps, multipanel plots, alert tables — over routinely collected data, and its release history is essentially an rOpenSci peer-review log. The package went from a January 2025 pre-release that warned of breaking changes ahead, through submission and CRAN acceptance, to a 1.0.0 in October 2025 that locked the API. Since then it has shipped only two small fixes, the most recent chasing a dplyr 1.2.0 deprecation.
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.
mantis builds interactive time-series reports — heatmaps, multipanel plots, alert tables — over routinely collected data, and its release history is essentially an rOpenSci peer-review log. The package went from a January 2025 pre-release that warned of breaking changes ahead, through submission and CRAN acceptance, to a 1.0.0 in October 2025 that locked the API. Since then it has shipped only two small fixes, the most recent chasing a dplyr 1.2.0 deprecation.
The breaking changes cluster entirely below 1.0.0 and stop there: `period` became `timepoint_unit`, `save_directory`/`save_filename` became `file`, `function_call` became `expression`. Post-1.0 the work is defensive — stricter POSIXt validation, timepoint limits that clamp to the data rather than inventing plot points, daylight-savings handling. The feed reads as a package that has finished defining itself and is now maintaining compatibility with the tidyverse underneath it.
With the API frozen and review complete, expect the next release to be another upstream-compatibility fix rather than new report types. There is no signal in these entries about planned feature work.
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 mantis or tulpa.
Basedash keeps pushing its data out of the workspace — now to people without accounts
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Fulcrum is betting its whole map stack on Esri, with a hard Google Maps cutoff on September 1.
Holistics keeps fencing in the AI layer it spent the summer building.
Dovetail spent July opening doors to other tools and August making its own rooms easier to enter.
See all mantis 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 mantis alternatives in Analytics are ranked by recent ship velocity. Browse the "mantis alternatives" section above for the current picks, or visit /alternatives/mantis 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.