STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A side-by-side editorial comparison of sdsfun and tEDM — release velocity, themes, recent moves, and the top alternatives to consider.
A spatial-statistics utility package exists to be depended on, and is built accordingly.
sdsfun collects spatial data science utilities — neighbour lists, spatial constrained clustering, discretization, dummy variable generation, geographical detector statistics and projection helpers — with the computationally heavy parts implemented in Rcpp. It was assembled quickly across late 2024, adding a function set roughly every three weeks, and has slowed since to a couple of releases a year. The most recent work is corrective: no longer initializing the RNG state at load, fixing matrix inputs misread as vectors, and clearing an Armadillo deprecation.
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.
sdsfun collects spatial data science utilities — neighbour lists, spatial constrained clustering, discretization, dummy variable generation, geographical detector statistics and projection helpers — with the computationally heavy parts implemented in Rcpp. It was assembled quickly across late 2024, adding a function set roughly every three weeks, and has slowed since to a couple of releases a year. The most recent work is corrective: no longer initializing the RNG state at load, fixing matrix inputs misread as vectors, and clearing an Armadillo deprecation.
This is infrastructure for a family of packages rather than an end-user tool, and the changelog says so directly — functions were added to support gdverse and sesp, and moran_test was migrated in from geocomplexity. That migration pattern is the defining move: capability consolidates here so the downstream packages can share it instead of each carrying its own copy. Growth has slowed as that consolidation completed, leaving correctness and dependency upkeep.
Given the package moves when its dependents need something, the next release most likely brings in another shared function or responds to a downstream requirement rather than following its own plan. Armadillo and CRAN check changes remain the reliable source of maintenance work.
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 sdsfun or tEDM.
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A debugger for ggplot2's internals, hardening its grip as the internals it traces keep moving.
A univariate density estimator that added zero-inflated data and reopened its C++ API to do it.
Stationary vine copulas for time series, released in lockstep with the rest of Nagler's vine stack.
A single-purpose ggplot2 extension that has spent six years tracking ggplot2 instead of growing.
A Star Trek data package that became a Memory Alpha web client and has been patching scrapers ever since.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. sdsfun 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. sdsfun 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 sdsfun alternatives in Analytics are ranked by recent ship velocity. Browse the "sdsfun alternatives" section above for the current picks, or visit /alternatives/sdsfun 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.