STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
Comparison · Analytics
sdsfun vs spEDM
A side-by-side editorial comparison of sdsfun and spEDM — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
sdsfun vs spEDM: at a glance
What is sdsfun?
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.
What is spEDM?
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.
sdsfun vs spEDM: editorial side-by-side
sdsfun
0.0ANALYTICS
A spatial-statistics utility package exists to be depended on, and is built accordingly.
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
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.
spEDM
0.0ANALYTICS
Spatial causal discovery in R, one exposed method per release
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
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.
Alternatives to sdsfun and spEDM
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 spEDM.
ggtrace
A debugger for ggplot2's internals, hardening its grip as the internals it traces keep moving.
kde1d
A univariate density estimator that added zero-inflated data and reopened its C++ API to do it.
svines
Stationary vine copulas for time series, released in lockstep with the rest of Nagler's vine stack.
gghighlight
A single-purpose ggplot2 extension that has spent six years tracking ggplot2 instead of growing.
rtrek
A Star Trek data package that became a Memory Alpha web client and has been patching scrapers ever since.
See all sdsfun alternatives → · See all spEDM alternatives →
Recent activity from sdsfun and spEDM
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 4mo agospEDMData slicing for large-scale pattern causality, plus API breaks
- 6mo agospEDMspEDM 1.11
- 6mo agospEDMSpatially convergent partial cross mapping reaches the R API
- 8mo agospEDMRaster cross mapping with anisotropic embedding
- 10mo agosdsfunPackage load stops touching the RNG state
- 11mo agospEDMConfigurable distance metrics and multithreaded distance computation
- 1y agospEDMSpatial logistic map exposed at the R level
- 1y agosdsfunUnified partial correlation testing and head/tails discretization
- 1y agosdsfunMissing-value handling added to linear trend removal
- 1y agosdsfunCovariate-based detrending and long-to-matrix spatial reshaping
- 1y agosdsfunSpatially constrained hierarchical clustering and SPADE estimation
- 1y agosdsfunFast geodetector q-value estimator added
Frequently asked questions
What is the difference between sdsfun and spEDM?
Both compete on the same themes — r-package — within Analytics. sdsfun 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.
Is sdsfun better than spEDM?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. sdsfun 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.
What are the best alternatives to sdsfun?
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.
What are the best alternatives to spEDM?
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.