STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A side-by-side editorial comparison of maths.genealogy and sdsfun — release velocity, themes, recent moves, and the top alternatives to consider.
A young Mathematics Genealogy client spending its first four releases satisfying CRAN.
maths.genealogy queries the Mathematics Genealogy Project over a WebSocket connection and renders academic advisor-student trees, with plot_grviz() as the visualisation entry point. The package reached CRAN in early 2025 and its functional surface has barely moved since — max_zoom() for deep trees at 0.1.1 is the only user-facing addition in the visible history.
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.
maths.genealogy queries the Mathematics Genealogy Project over a WebSocket connection and renders academic advisor-student trees, with plot_grviz() as the visualisation entry point. The package reached CRAN in early 2025 and its functional surface has barely moved since — max_zoom() for deep trees at 0.1.1 is the only user-facing addition in the visible history.
Every release after the first is CRAN policy management. Three consecutive entries deal with the same underlying problem: examples that hit a live network resource and therefore fail unpredictably on check machines. The progression from wrapping them in \donttest{} to catching a stray case to rewriting all examples against published API-package guidance shows the maintainer converging on a pattern rather than adding features. That is the normal cost of shipping a network client to CRAN, and it appears to be settling.
With the examples problem resolved, the next release is the first plausible opportunity for feature work — likely on the plotting side, given max_zoom() was the sole non-compliance change so far. The entries do not name anything specific in progress.
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.
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 maths.genealogy or sdsfun.
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 maths.genealogy alternatives → · See all sdsfun alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. maths.genealogy and sdsfun 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. maths.genealogy and sdsfun 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 maths.genealogy alternatives in Analytics are ranked by recent ship velocity. Browse the "maths.genealogy alternatives" section above for the current picks, or visit /alternatives/maths-genealogy for the full list with editorial commentary on each.
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.