webchem
Adding chemical databases with one hand while public ones close programmatic access with the other.
A side-by-side editorial comparison of marquee and mlr3spatial — release velocity, themes, recent moves, and the top alternatives to consider.
marquee is filling in the typographic details — outlines, border types, real font metrics for underlines.
marquee renders markdown text onto R graphics devices, and backs element_marquee() and geom_marquee() in ggplot2. Development ran in a tight burst through August and September 2025: 1.1.0 added text outlines, a size shortcut and remote PNG/JPEG support, 1.2.0 added border and outline line types and moved underline placement onto font metrics, and 1.2.1 cleaned up the bugs those introduced.
Raster prediction in mlr3 finally returns class probabilities, not just hard labels.
mlr3spatial connects mlr3 learners to raster and vector spatial data, handling chunked prediction over large rasters through DataBackendRaster. Development is slow and fix-heavy: most releases in the last two years were compatibility work against mlr3 and paradox rather than new capability. 0.7.0 is the exception.
marquee renders markdown text onto R graphics devices, and backs element_marquee() and geom_marquee() in ggplot2. Development ran in a tight burst through August and September 2025: 1.1.0 added text outlines, a size shortcut and remote PNG/JPEG support, 1.2.0 added border and outline line types and moved underline placement onto font metrics, and 1.2.1 cleaned up the bugs those introduced.
The package is converging on typographic fidelity rather than new capability. Early work settled layout semantics — CSS margin collapsing, inline padding reserving space during shaping, devices without glyph support — and recent releases refine how decorations are drawn and measured. The naming cleanup in 1.2.0, border_size becoming border_width, reads as an API being tidied ahead of wider use rather than one still being explored.
Expect continued small releases sanding down rendering edge cases in ggplot2 contexts, since that is where the recent bug reports come from; nothing here signals a new feature area.
mlr3spatial connects mlr3 learners to raster and vector spatial data, handling chunked prediction over large rasters through DataBackendRaster. Development is slow and fix-heavy: most releases in the last two years were compatibility work against mlr3 and paradox rather than new capability. 0.7.0 is the exception.
The package tracks the mlr3 core rather than leading it — 0.5.0 and 0.6.1 exist to absorb upstream changes in paradox and mlr3. Against that background, 0.7.0 adding probability predictions to predict_spatial() is the first genuine capability increase in a while, arriving alongside two DataBackendRaster fixes for multi-band sources and similarly-named layers. Cadence is roughly one release per year.
Given the pattern, the next release is more likely to be compatibility work against a new mlr3 or terra version than another feature; further raster-backend edge cases around layer naming are the visible loose end.
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 marquee or mlr3spatial.
Adding chemical databases with one hand while public ones close programmatic access with the other.
The spatiotemporal companion to sf, moving at the pace of the packages around it.
An interface to Europe's long-term ecosystem research network that went quiet after 2.0.
Spatial model assessment that spent the last year on cross-platform arithmetic and CRAN rules.
Stopped trusting the cloud to prepare its taxonomic databases and does the conversion locally.
Went from estimating felling dates to doing the crossdating that produces them.
See all marquee alternatives → · See all mlr3spatial alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. marquee and mlr3spatial are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). 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. marquee and mlr3spatial are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top marquee alternatives in Analytics are ranked by recent ship velocity. Browse the "marquee alternatives" section above for the current picks, or visit /alternatives/marquee for the full list with editorial commentary on each.
Top mlr3spatial alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3spatial alternatives" section above for the current picks, or visit /alternatives/mlr3spatial for the full list with editorial commentary on each.