STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A side-by-side editorial comparison of ojoregex and sdsfun — release velocity, themes, recent moves, and the top alternatives to consider.
Oklahoma's court-data nonprofit maintains the regex layer that turns charge text into categories.
ojoregex is Open Justice Oklahoma's pattern library for classifying criminal charge descriptions from court records — the unglamorous translation layer between free-text charge fields and analysable categories. Its entire release history reached this feed as four tags published within three minutes, so the feed order reflects a backfill rather than a shipping cadence. Release notes are merge references rather than descriptions, which limits how much can be read from the changelog alone.
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.
ojoregex is Open Justice Oklahoma's pattern library for classifying criminal charge descriptions from court records — the unglamorous translation layer between free-text charge fields and analysable categories. Its entire release history reached this feed as four tags published within three minutes, so the feed order reflects a backfill rather than a shipping cadence. Release notes are merge references rather than descriptions, which limits how much can be read from the changelog alone.
What the notes do show is a package alternating between domain corrections and R tooling upkeep: a fix to property-crime matching in one release, dplyr select semantics in the next. That is the expected shape for a regex catalogue — accuracy work arrives as individual charge types get miscategorised in real analyses, and the rest is keeping the package installable against a moving tidyverse. Contributions come from a small internal team, and the vignette work referenced in the earliest tag suggests the pattern list doubles as documentation for analysts.
The visible pattern is incremental match fixes as charge types surface in use; the release notes carry too little detail to predict anything beyond that without reading the underlying pull requests.
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 ojoregex 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 ojoregex 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. ojoregex 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. ojoregex 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 ojoregex alternatives in Analytics are ranked by recent ship velocity. Browse the "ojoregex alternatives" section above for the current picks, or visit /alternatives/ojoregex 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.