fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of robma and treasury — release velocity, themes, recent moves, and the top alternatives to consider.
RoBMA 4.0 tears out its own constructor surface and rebuilds on one class hierarchy
RoBMA fits robust Bayesian model-averaged meta-analyses that adjust for publication bias. The 3.x line grew by accretion: separate constructors for each model family (RoBMA.reg, NoBMA, BiBMA and their .reg variants), a spike-and-slab algorithm in 3.3.0 that made estimation fast enough to matter, then a steady stream of post-estimation tooling gated on that algorithm — heterogeneity summaries, residuals, funnel plots, z-curve conversion, predict, extract, pooled and adjusted effects. Version 4.0.0 in May 2026 collapses all of it into a unified brma class hierarchy.
A thin Treasury rates wrapper has stopped adding endpoints and started making its tables self-describing.
treasury wraps the US Treasury's published rate feeds — bill rates, par yields, forward rates, long-term extrapolated rates, and the HQM and breakeven inflation curves — into one set of R functions. Since 0.3.0 every function returns a data.table, and 0.5.0 added optional on-disk response caching with a one-day default. The most recent release is about data fidelity rather than reach: identifying columns, correct maturity labels, and locale-safe date parsing.
RoBMA fits robust Bayesian model-averaged meta-analyses that adjust for publication bias. The 3.x line grew by accretion: separate constructors for each model family (RoBMA.reg, NoBMA, BiBMA and their .reg variants), a spike-and-slab algorithm in 3.3.0 that made estimation fast enough to matter, then a steady stream of post-estimation tooling gated on that algorithm — heterogeneity summaries, residuals, funnel plots, z-curve conversion, predict, extract, pooled and adjusted effects. Version 4.0.0 in May 2026 collapses all of it into a unified brma class hierarchy.
The 3.x series solved the modeling problem and left an interface problem behind: a caller had to know which of six constructors matched their data type, and argument names differed across them. 4.0.0 resolves that by making the model family a set of arguments rather than a function name, and by standardizing input naming on metafor-style conventions. It shipped one day after BayesTools 0.3.0, the author's own upstream infrastructure package, whose new standardization and prior-transformation machinery this rewrite depends on.
A rewrite this wide usually needs a follow-up, so expect 4.0.x patches addressing migration gaps as users hit the removed constructors and renamed arguments.
treasury wraps the US Treasury's published rate feeds — bill rates, par yields, forward rates, long-term extrapolated rates, and the HQM and breakeven inflation curves — into one set of R functions. Since 0.3.0 every function returns a data.table, and 0.5.0 added optional on-disk response caching with a one-day default. The most recent release is about data fidelity rather than reach: identifying columns, correct maturity labels, and locale-safe date parsing.
Endpoint coverage looks essentially complete, so the work has moved to the metadata a downstream analyst needs to join and audit results — cusip and maturity_date on bill quotes, the feed's updated_at stamp, and the extrapolation factor behind 2002-2006 long-term rate estimates. Error handling is tightening in the same direction: an out-of-range month now fails with a message instead of quietly returning nothing. That is the profile of a wrapper moving from coverage to correctness, where the remaining bugs are the subtle ones that only surface in other people's locales.
Expect further column-level enrichment and input validation on the endpoints already covered rather than new data sources, since the structural pieces — data.table returns and caching — are already in place.
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 robma or treasury.
Pattern fills for ggplot2, hardened against the ways users write sizes
gcube's recent releases are all packaging metadata, not simulation code
The R port of Quinlan's Cubist gets reproducibility fixes, not new modelling
ggstats keeps widening what a coefficient or Likert plot can be
ecodive rebuilt itself into a broad diversity-metric library, breaking as it went
State-space data simulation for R, filled in one function at a time
See all robma alternatives → · See all treasury alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. robma and treasury 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. robma and treasury 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 robma alternatives in Analytics are ranked by recent ship velocity. Browse the "robma alternatives" section above for the current picks, or visit /alternatives/robma for the full list with editorial commentary on each.
Top treasury alternatives in Analytics are ranked by recent ship velocity. Browse the "treasury alternatives" section above for the current picks, or visit /alternatives/treasury for the full list with editorial commentary on each.