Whatagraph
Whatagraph keeps fixing what breaks when one account runs a thousand sources.
A side-by-side editorial comparison of ageproR and tulpa — release velocity, themes, recent moves, and the top alternatives to consider.
ageproR spent two years chasing a moving file format, then added the recruitment models that justify the effort.
An R interface for building and validating AGEPRO input files — the configuration format for a fisheries stock projection program used in stock assessments. Releases come every few months and are dominated by one recurring problem: keeping up with the AGEPRO input file format, which has moved between VERSION 4.0 and VERSION 4.25 in both directions across this window. The package spends considerable effort on validation, version detection, and clear error messages when a file does not match.
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.
An R interface for building and validating AGEPRO input files — the configuration format for a fisheries stock projection program used in stock assessments. Releases come every few months and are dominated by one recurring problem: keeping up with the AGEPRO input file format, which has moved between VERSION 4.0 and VERSION 4.25 in both directions across this window. The package spends considerable effort on validation, version detection, and clear error messages when a file does not match.
The version-format churn is settling. Release 0.7.1 reverted the default back to VERSION 4.0 as a bugfix, and 0.9.0 finally set 4.25 as current while retaining a 4.0 compatibility string and improving the detection messages — a resolution rather than another reversal. With that stabilising, the substantive work has been the recruitment model coverage added in 0.8.0, which brought autocorrelated lognormal error structures into the package for the first time. Naming has been converging too, with output_stock_summary and summary_output_flag renamed to auxiliary variants to match the AGEPRO-GUI specification.
Expect the remaining recruitment models to be filled in against the AGEPRO specification, and the version handling to stay on 4.25 now that both formats are supported and validated rather than swapped.
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 ageproR or tulpa.
Whatagraph keeps fixing what breaks when one account runs a thousand sources.
Lightdash keeps handing authoring to outside agents and keeping the governed layer for itself.
A 4.4.0 tag appears, but the feed carries only its release plumbing
distributions3 0.3.0 adds sample-based distributions and likelihood derivatives
Basedash keeps pushing its data out of the workspace — now to people without accounts
RStudio ships through release branches, and the notes are commit messages
See all ageproR 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 ageproR alternatives in Analytics are ranked by recent ship velocity. Browse the "ageproR alternatives" section above for the current picks, or visit /alternatives/agepror-r 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.