STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A side-by-side editorial comparison of benviplot and sdsfun — release velocity, themes, recent moves, and the top alternatives to consider.
A Brazilian housing-data palette package went from internal tooling to public 1.0 in five days.
benviplot supplies color palettes, ggplot2 scales, themes and plot helpers for charts in a consistent house style, oriented around Brazilian urban and rental-market data. The entire public history is compressed into early October 2025: a six-phase release plan took it from removing proprietary data through modernization, testing, vignettes, documentation and CI to a stable 1.0.0. The shipped package carries 36 curated palettes, discrete and continuous scale functions, and a rental price index dataset covering six Brazilian cities.
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.
benviplot supplies color palettes, ggplot2 scales, themes and plot helpers for charts in a consistent house style, oriented around Brazilian urban and rental-market data. The entire public history is compressed into early October 2025: a six-phase release plan took it from removing proprietary data through modernization, testing, vignettes, documentation and CI to a stable 1.0.0. The shipped package carries 36 curated palettes, discrete and continuous scale functions, and a rental price index dataset covering six Brazilian cities.
This is an internal tool being packaged for public consumption rather than a product evolving in the open — the phases were about legal separation, test coverage and check compliance, not new capability. Removing the sensitive QuintoAndar dataset and adding a disclaimer establishing independence was phase one, which frames the whole exercise. The one substantive addition along the way was the IQAIW rental index, built from a public source to replace what was removed.
With the release plan completed and the package stable, the most likely next work is periodic refreshes of the rental index dataset, which is published on an ongoing basis from 2023 onward. The entries give no indication of planned new palettes or plot functions.
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 benviplot 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 benviplot 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. benviplot 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. benviplot 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 benviplot alternatives in Analytics are ranked by recent ship velocity. Browse the "benviplot alternatives" section above for the current picks, or visit /alternatives/benviplot 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.