Whatagraph
Whatagraph keeps fixing what breaks when one account runs a thousand sources.
A side-by-side editorial comparison of ravetools and tulpa — release velocity, themes, recent moves, and the top alternatives to consider.
RAVE's signal-processing toolkit taught base R graphics to render lit 3D brain meshes.
ravetools supplies the signal processing and now the rendering primitives behind RAVE, a suite for analyzing intracranial electroencephalography. Its earlier releases built out Matlab-equivalent filtering, Welch spectra and wavelet tooling; 0.2.6 changes register entirely, adding plot_mesh_dotcloud and plot_mesh_polygon to draw 3D surface meshes in base R graphics with no rgl dependency, plus arbitrary clipping planes and per-mesh alpha.
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.
ravetools supplies the signal processing and now the rendering primitives behind RAVE, a suite for analyzing intracranial electroencephalography. Its earlier releases built out Matlab-equivalent filtering, Welch spectra and wavelet tooling; 0.2.6 changes register entirely, adding plot_mesh_dotcloud and plot_mesh_polygon to draw 3D surface meshes in base R graphics with no rgl dependency, plus arbitrary clipping planes and per-mesh alpha.
The package is absorbing the visualization layer that RAVE previously delegated to WebGL-backed renderers, and doing it in a way that works in headless and figure-generating contexts. Exporting ensure_mesh3d to coerce mesh3d, ieegio_surface, fs.surface and surf.asc into one canonical form makes ravetools the convergence point for surface formats across the wider toolchain — the sibling ieegio package shipped its own base R plot method within minutes of this release.
Expect the base R renderers to pick up the features rgl users would miss next — richer materials, labeling and camera control — and for more of the RAVE stack to route surface handling through ensure_mesh3d.
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 ravetools 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 ravetools 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 ravetools alternatives in Analytics are ranked by recent ship velocity. Browse the "ravetools alternatives" section above for the current picks, or visit /alternatives/ravetools-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.