fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of bbk and tEDM — release velocity, themes, recent moves, and the top alternatives to consider.
One R interface is absorbing the world's central bank data portals, one API at a time.
bbk began as a Bundesbank client and has become a single R interface to central bank statistics generally: the ECB, BIS, and the national banks of Switzerland, Canada, the UK, France, Spain, Austria, Sweden, Norway, Portugal, Japan, Poland, the Czech Republic, and now Brazil and Mexico. Each provider gets a consistent set of verbs — a data function, a dimension function for the dataflow structure, and provider-specific extras like PRIBOR or CZEONIA fixings. Response caching, data.table returns, and an updated_after argument for incremental retrieval are shared plumbing rather than per-provider features.
The temporal half of the stscl EDM pair, tracking its spatial sibling
tEDM applies empirical dynamic modeling to time series — cross mapping, convergent cross mapping and the logistic map — as the temporal counterpart to spEDM, with which it shares a maintainer and a C++ core. The recent releases are consolidation rather than expansion: index handling in cross mapping corrected, generics taught to accept varying E, k and tau, and the associated paper now cited in the README. Only three releases are visible in the feed.
bbk began as a Bundesbank client and has become a single R interface to central bank statistics generally: the ECB, BIS, and the national banks of Switzerland, Canada, the UK, France, Spain, Austria, Sweden, Norway, Portugal, Japan, Poland, the Czech Republic, and now Brazil and Mexico. Each provider gets a consistent set of verbs — a data function, a dimension function for the dataflow structure, and provider-specific extras like PRIBOR or CZEONIA fixings. Response caching, data.table returns, and an updated_after argument for incremental retrieval are shared plumbing rather than per-provider features.
The expansion is steady and the integration work is what makes it more than a list of wrappers: arguments introduced for one provider get pushed to the others, dimension introspection is being generalised across dataflows, and the bug fixes in recent releases are almost all about the same class of problem — series with missing observations, unsupported frequency codes, or date/value misalignment breaking a parser written for a tidier feed. The maintainer ships the same infrastructure across their packages in lockstep; bbk 0.9.0 and the sibling treasury package's 0.5.0 landed identical opt-in caching within minutes of each other. Geography is the visible frontier, but consistency across an increasingly ragged set of upstream APIs is the actual work.
Expect more national central banks to be added on the same template, and the newer providers to be retrofitted with the dimension and updated_after functions the older ones already have.
tEDM applies empirical dynamic modeling to time series — cross mapping, convergent cross mapping and the logistic map — as the temporal counterpart to spEDM, with which it shares a maintainer and a C++ core. The recent releases are consolidation rather than expansion: index handling in cross mapping corrected, generics taught to accept varying E, k and tau, and the associated paper now cited in the README. Only three releases are visible in the feed.
tEDM moves in lockstep with spEDM. Configurable distance metrics, varying E/k/tau inputs, strict floating-point comparison and the S3 plotting font unification all appear in both packages within days or weeks, as does the maintainer surname correction. The recent balance has tilted toward correcting library and prediction index handling — the kind of repeated attention that suggests the indexing model was the weak point of the shared core.
Expect tEDM to keep inheriting the shared-core changes spEDM lands, with its own releases staying small and centred on cross-mapping parameter handling rather than new method surface.
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 bbk or tEDM.
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
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. bbk and tEDM 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. bbk and tEDM 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 bbk alternatives in Analytics are ranked by recent ship velocity. Browse the "bbk alternatives" section above for the current picks, or visit /alternatives/bbk for the full list with editorial commentary on each.
Top tEDM alternatives in Analytics are ranked by recent ship velocity. Browse the "tEDM alternatives" section above for the current picks, or visit /alternatives/tedm for the full list with editorial commentary on each.