fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of dissmapr and spEDM — release velocity, themes, recent moves, and the top alternatives to consider.
dissmapr spent its first releases becoming citable rather than adding methods.
dissmapr provides an R workflow for compositional dissimilarity and turnover — occurrence data through spatial gridding and environmental linkage to order-wise dissimilarity and bioregional mapping. All three releases to date are infrastructure: a first citable archive in June 2026, then a maturity release aligning the package with the B-Cubed software development guide. The ten-function pipeline described in the notes has not changed across them.
Spatial causal discovery in R, one exposed method per release
spEDM brings empirical dynamic modeling to spatial data — cross mapping, convergent cross mapping and pattern causality over spatial vector and raster inputs, with the numerics in C++ behind S4 generics. The recent releases have exposed geographical pattern causality and spatially convergent partial cross mapping at the R level with vignettes, and 1.12 turns to consolidating the API. It is part of the stscl family alongside the temporal-domain tEDM, with which it shares both its C++ core and its maintainer.
dissmapr provides an R workflow for compositional dissimilarity and turnover — occurrence data through spatial gridding and environmental linkage to order-wise dissimilarity and bioregional mapping. All three releases to date are infrastructure: a first citable archive in June 2026, then a maturity release aligning the package with the B-Cubed software development guide. The ten-function pipeline described in the notes has not changed across them.
The work is compliance-shaped rather than method-shaped: explicit @importFrom in place of whole-namespace imports, library() calls removed from package code, roughly 11 MB of development caches dropped, a runnable README quick-start, and Zenodo archival with CITATION.cff and codemeta.json. dissmapr moves in lockstep with its B-Cubed sibling invasimapr — both tagged 0.1.0 within three minutes of each other and 0.2.0 on the same day — so releases here reflect project-wide standards deadlines more than package-specific work. The stated roadmap of additional ecological distance metrics has not yet landed.
With standards work now signed off and R CMD check clean, the next release is the first real chance for the roadmap items — additional ecological distance metrics — to arrive.
spEDM brings empirical dynamic modeling to spatial data — cross mapping, convergent cross mapping and pattern causality over spatial vector and raster inputs, with the numerics in C++ behind S4 generics. The recent releases have exposed geographical pattern causality and spatially convergent partial cross mapping at the R level with vignettes, and 1.12 turns to consolidating the API. It is part of the stscl family alongside the temporal-domain tEDM, with which it shares both its C++ core and its maintainer.
The cadence is steady and predictable: each release surfaces one more EDM method as an R-level API with a vignette, then spends the rest of its notes on parameter-handling consistency across the generics. Breaking changes are frequent and deliberate — argument renames, parameter reordering, NA-handling defaults — which reads as a package still settling its interface while the method surface expands. Shared changes appear in tEDM within days, so interface churn lands on both packages at once.
Expect the next release to expose another causality variant at the R level with an accompanying vignette, and to continue renaming or reordering parameters toward consistency across the spatial and temporal packages.
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 dissmapr or spEDM.
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 dissmapr alternatives → · See all spEDM alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. dissmapr and spEDM 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. dissmapr and spEDM 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 dissmapr alternatives in Analytics are ranked by recent ship velocity. Browse the "dissmapr alternatives" section above for the current picks, or visit /alternatives/dissmapr for the full list with editorial commentary on each.
Top spEDM alternatives in Analytics are ranked by recent ship velocity. Browse the "spEDM alternatives" section above for the current picks, or visit /alternatives/spedm for the full list with editorial commentary on each.