metatools
SDTM supplemental-qualifier merging got sturdier, then the package went quiet for two years.
A side-by-side editorial comparison of marquee and mlr3extralearners — 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.
The mlr3 learner catalogue is growing fast and pruning hyperparameters just as deliberately.
mlr3extralearners is the overflow catalogue for mlr3 learners that do not ship in the core packages — currently spanning H2O, Botorch, fastai, glmnet, survival and competing-risks models. The last two feature releases added roughly thirty learners between them. 1.6.0 then went the other way, cutting hyperparameters that were never correctly forwarded.
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.
mlr3extralearners is the overflow catalogue for mlr3 learners that do not ship in the core packages — currently spanning H2O, Botorch, fastai, glmnet, survival and competing-risks models. The last two feature releases added roughly thirty learners between them. 1.6.0 then went the other way, cutting hyperparameters that were never correctly forwarded.
Two forces are visible. The catalogue expands in bursts — 1.4.0 and 1.5.0 each added large batches, including a full H2O family and Bayesian regression models — while the maintenance releases in between are dominated by skipping tests on platforms where Python-backed learners crash. 1.6.0 marks a shift toward correctness of the existing surface: priority_lasso parameter sets reduced to what actually passes through, and Cox-inapplicable glmnet parameters removed.
The Python-backed learners are the recurring source of platform instability, so expect continued pinning and test-skipping there alongside the next batch of additions.
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 mlr3extralearners.
SDTM supplemental-qualifier merging got sturdier, then the package went quiet for two years.
A clinical-script logger that stopped shipping after its 0.2 line, changelogs made of merged PRs.
R's object inspector is losing its view of the internals as CRAN closes off the private C API.
A weather-station data client that broke one return type to hand back distances instead of bare IDs.
giscoR's 1.0 moved its dataset index into the cache, so new Eurostat releases arrive without a package update.
FedData has spent two major versions migrating US federal geodata off R's retiring spatial stack.
See all marquee alternatives → · See all mlr3extralearners 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 mlr3extralearners 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 mlr3extralearners 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 mlr3extralearners alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3extralearners alternatives" section above for the current picks, or visit /alternatives/mlr3extralearners for the full list with editorial commentary on each.