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Comparison · Analytics
sdsfun vs STACAS
A side-by-side editorial comparison of sdsfun and STACAS — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
sdsfun vs STACAS: 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 STACAS?
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
STACAS integrates single-cell RNA-seq datasets by finding and weighting anchors between them, with rPCA-distance-based downweighting and an optional semi-supervised mode that uses cell type labels to discard inconsistent anchors. IntegrateData.STACAS() performs the integration natively rather than handing off, and StandardizeGeneSymbols() normalises gene naming across datasets before anchors are computed.
sdsfun vs STACAS: 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.
STACAS
0.0ANALYTICS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
◆ Current state
STACAS integrates single-cell RNA-seq datasets by finding and weighting anchors between them, with rPCA-distance-based downweighting and an optional semi-supervised mode that uses cell type labels to discard inconsistent anchors. IntegrateData.STACAS() performs the integration natively rather than handing off, and StandardizeGeneSymbols() normalises gene naming across datasets before anchors are computed.
◆ Where it's heading
The method work concentrated in version 2.0 and has been stable since; everything after is Seurat compatibility and operational robustness. Versions 2.1.1 through 2.3.0 track Seurat v5 assays, v3-to-v5 conversion, multi-layer objects and SCT normalisation, with the genuinely useful additions — a reference seed dataset, max.seed.datasets for large-scale integration, min.sample.size — arriving as side effects of that work. The package is from the same lab as GeneNMF, and its release rhythm follows the single-cell ecosystem's upstream churn rather than an internal roadmap.
◆ Prediction
Expect the next release to follow further Seurat object-model changes, which have driven the last three. Nothing in the entries indicates new anchor-scoring or correction methodology in progress.
Alternatives to sdsfun and STACAS
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 STACAS.
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.
eiaapi
A thin EIA energy-data client whose whole story is making bulk queries survive the API's limits.
See all sdsfun alternatives → · See all STACAS alternatives →
Recent activity from sdsfun and STACAS
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 10mo agosdsfunPackage load stops touching the RNG state
- 1y agoSTACASMulti-layer objects and Seurat v3-to-v5 conversion handled
- 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
- 2y agoSTACASscale.data option for extreme batch effects; gene name conversion table
- 3y agoSTACASReference seeding, gene symbol standardisation, large-scale integration path
- 4y agoSTACASSemi-supervised integration and rPCA anchor downweighting ⚡
- 5y agoSTACASSeurat 4.0.0 compatibility and SCTransform support
Frequently asked questions
What is the difference between sdsfun and STACAS?
Both compete on the same themes — r-package — within Analytics. sdsfun and STACAS 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 STACAS?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. sdsfun and STACAS 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 STACAS?
Top STACAS alternatives in Analytics are ranked by recent ship velocity. Browse the "STACAS alternatives" section above for the current picks, or visit /alternatives/stacas for the full list with editorial commentary on each.