simlandr
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
A side-by-side editorial comparison of spEDM and vinereg — release velocity, themes, recent moves, and the top alternatives to consider.
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.
Conditional density and log-likelihood fill out a vine copula regression package.
vinereg fits D-vine copula-based regression models on top of rvinecopulib and kde1d, in Thomas Nagler's package stack. The January 2025 pair - 0.10.0 and 0.11.0 tagged the same day - adds a pdf() function and then fixes conditional density computation for discrete variables while requiring the newer kde1d. Release notes run to one or two bullets each.
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.
vinereg fits D-vine copula-based regression models on top of rvinecopulib and kde1d, in Thomas Nagler's package stack. The January 2025 pair - 0.10.0 and 0.11.0 tagged the same day - adds a pdf() function and then fixes conditional density computation for discrete variables while requiring the newer kde1d. Release notes run to one or two bullets each.
Work has concentrated on evaluation rather than fitting: cll() in 0.9.0, pdf() in 0.10.0, and the discrete-variable correction in 0.11.0 all concern what can be computed from a model already fitted. Releases arrive in same-day pairs, and the notes are terse enough that 0.10.0 reuses 0.9.0's wording verbatim, describing pdf() with cll()'s sentence. Version floors also track the sibling packages - kde1d here, rvinecopulib in 0.8.3.
Given the shared release rhythm across the stack, the next entry is as likely to be a dependency-driven bump as a new function; the discrete-variable path is the one area these notes show as recently unstable.
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 spEDM or vinereg.
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
SEM reporting helpers converging on APA output, one CRAN resubmission at a time.
A raster-to-terra migration is the only readable change in a feed of merge notes.
A nycflights13 generator whose recent work is all about the data being right.
A drop-in string API for base R, kept alive by upstream check failures.
From a bundled hospital dataset to a live CMS API client.
See all spEDM alternatives → · See all vinereg alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. spEDM and vinereg 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. spEDM and vinereg 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 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.
Top vinereg alternatives in Analytics are ranked by recent ship velocity. Browse the "vinereg alternatives" section above for the current picks, or visit /alternatives/vinereg for the full list with editorial commentary on each.