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 skylight — 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.
A frozen astronomical model quietly became the inner loop of its sibling's optimizer.
skylight returns sun and moon illuminance, azimuth and altitude for a given date, time and location, implemented as a near-verbatim transcription of a 1987 US Naval Observatory circular. The model formulation has not changed since the initial 2022 release and the author states so explicitly. Everything shipped since has been packaging, citation and speed: v1.3 moved the main routine from R to C++, and v1.4 removed a parameter check that was flooding the console with messages.
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.
skylight returns sun and moon illuminance, azimuth and altitude for a given date, time and location, implemented as a near-verbatim transcription of a 1987 US Naval Observatory circular. The model formulation has not changed since the initial 2022 release and the author states so explicitly. Everything shipped since has been packaging, citation and speed: v1.3 moved the main routine from R to C++, and v1.4 removed a parameter check that was flooding the console with messages.
This is a reference implementation of a published algorithm rather than a product accumulating features, and it is being maintained that way. The movement that does occur is driven from downstream: the C++ port was written for the inverse-modelling loop in the sibling skytrackr package, which calls skylight repeatedly during optimization. That reframes skylight from a standalone calculator into the compute kernel another package's fitting routine depends on.
With the model formulation deliberately fixed and the C++ path already in place, the next release is most likely another small maintenance fix. The entries give no indication of planned new capability.
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 skylight.
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.
See all sdsfun alternatives → · See all skylight alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. sdsfun and skylight 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 skylight 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 skylight alternatives in Analytics are ranked by recent ship velocity. Browse the "skylight alternatives" section above for the current picks, or visit /alternatives/skylight for the full list with editorial commentary on each.