broom.helpers
After eleven quiet months, the tidier under gtsummary is back to absorbing model classes.
A side-by-side editorial comparison of haze and scimesh — release velocity, themes, recent moves, and the top alternatives to consider.
Mesh interpolation drops its custom fork dependency and sheds weight.
haze does per-vertex smoothing and interpolation on triangular meshes, aimed at mapping neuroimaging surface data between subjects, with k-d tree lookup and interpolation in C++. After three years dormant it has shipped twice in a month: a modernization pass to get through R CMD check, and now the removal of its dependency on a custom Rvcg build. It has never been on CRAN because of package size.
A mesh renderer grows a scene graph and an exit route into glTF.
scimesh renders 3D meshes from R through a C++ core, aimed at scientific figures such as brain surfaces, with a CLI renderer example alongside the R interface. The newest release adds per-mesh placement transforms, a first-class scene object, and glTF 2.0 export. Development has been rapid and mostly CRAN-directed since mid-July, with several releases spent satisfying R CMD check.
haze does per-vertex smoothing and interpolation on triangular meshes, aimed at mapping neuroimaging surface data between subjects, with k-d tree lookup and interpolation in C++. After three years dormant it has shipped twice in a month: a modernization pass to get through R CMD check, and now the removal of its dependency on a custom Rvcg build. It has never been on CRAN because of package size.
Both recent releases point at the same obstacle. Needing a patched Rvcg meant users could not install from any normal source, and package size is the stated reason CRAN was ruled out at the first release; this release removes the first barrier and starts on the second by deleting unused data files. Nothing has been added to the interpolation surface since 2022.
The direction of travel suggests a CRAN attempt once the size problem is solved, though the package has not said so and the earlier note put it ten times over the limit.
scimesh renders 3D meshes from R through a C++ core, aimed at scientific figures such as brain surfaces, with a CLI renderer example alongside the R interface. The newest release adds per-mesh placement transforms, a first-class scene object, and glTF 2.0 export. Development has been rapid and mostly CRAN-directed since mid-July, with several releases spent satisfying R CMD check.
The package is moving from a renderer that takes a list of meshes to one that holds a scene: meshes now carry their own model matrix and name rather than being modified in place, and that structure is exactly what the glTF exporter walks. The same release corrects a transposed 4x4 matrix convention that only diagonal inputs had hidden, which is the kind of fix that comes from a growing scene layer exercising the transform path properly.
glTF export is one-way today, so import is the obvious next step; the alternative is filling the gap the exporter names, since fog, SSAO and shading mode have no glTF equivalent.
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 haze or scimesh.
After eleven quiet months, the tidier under gtsummary is back to absorbing model classes.
OpenHouse starts adding per-column defaults while still closing silent-failure holes.
Julia's distribution library keeps filing down the edges where sampling meets array types
Power BI's monthly grind: authoring defaults, DAX documentation, and mobile finally catching up.
ggquiver is awake again, fixing arrow scaling that quietly misread irregular data.
A lazy vector container keeps closing the gaps where it quietly materialised anyway.
See all haze alternatives → · See all scimesh alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. scimesh is currently shipping more aggressively (velocity 6.3 vs 5.0), with 1 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. scimesh is currently shipping more aggressively (velocity 6.3 vs 5.0), with 1 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 haze alternatives in Analytics are ranked by recent ship velocity. Browse the "haze alternatives" section above for the current picks, or visit /alternatives/haze for the full list with editorial commentary on each.
Top scimesh alternatives in Analytics are ranked by recent ship velocity. Browse the "scimesh alternatives" section above for the current picks, or visit /alternatives/scimesh for the full list with editorial commentary on each.