STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A side-by-side editorial comparison of reda and sdsfun — release velocity, themes, recent moves, and the top alternatives to consider.
A mature recurrent-event toolkit in careful maintenance, shedding weight rather than adding surface.
reda provides nonparametric mean cumulative function estimation, gamma-frailty rate regression, and event-data simulation for recurrent-event survival analysis. The core API settled at 0.5.0 when Recur() replaced Survr() and the MCF internals moved to C++. Everything since has been consolidation: small argument additions, method completions, and CRAN hygiene.
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.
reda provides nonparametric mean cumulative function estimation, gamma-frailty rate regression, and event-data simulation for recurrent-event survival analysis. The core API settled at 0.5.0 when Recur() replaced Survr() and the MCF internals moved to C++. Everything since has been consolidation: small argument additions, method completions, and CRAN hygiene.
The last three releases contain no new modelling capability at all — a dependency reshuffle, a test-example correction, and a print-order fix. The package is being kept installable and correct rather than extended. Its tightest coupling is to splines2, a sibling package from the same maintainer, which supplies the derivative machinery reda depends on.
Expect continued small-cadence CRAN-compliance releases tracking ggplot2 and splines2 changes. The entries show no in-progress feature work, so a substantive release would have to arrive without warning from this 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.
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 reda 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.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. reda 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. reda 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 reda alternatives in Analytics are ranked by recent ship velocity. Browse the "reda alternatives" section above for the current picks, or visit /alternatives/reda 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.